Vehicle abnormal wheel speed correction method, device and equipment

Abstract

The application relates to the field of vehicle control, and discloses a vehicle abnormal wheel speed correction method, device and equipment, wherein the method comprises the following steps: acquiring four-wheel wheel speed signals and multi-source vehicle speed signals of a target vehicle; for any wheel speed signal, performing abnormality detection on the wheel speed signal based on the multi-source vehicle speed signals; judging whether the wheel speed signal is an abnormal wheel speed signal based on the detection result of the wheel speed signal; in the case that the wheel speed signal is determined to be an abnormal wheel speed signal, acquiring state information of the vehicle speed signal for any vehicle speed signal; determining a target confidence degree of the vehicle speed signal based on the state information; and performing weighted fusion on each vehicle speed signal and the corresponding target confidence degree to determine a reference wheel speed of the target vehicle, wherein the reference wheel speed is used as a correction reference for the abnormal wheel speed signal. The technical scheme provided by the application can realize accurate identification of abnormal wheel speed and adaptive correction of the reference wheel speed, so as to improve the reliability, safety and adaptability of the vehicle in a complex environment.

Description

Technical Field

[0001] This application relates to the field of vehicle control, and in particular to a method, apparatus and equipment for correcting abnormal wheel speeds of a vehicle. Background Technology

[0002] In vehicle active safety and stability control systems, wheel speed signals serve as the foundation for effective control. They are not only used to calculate key state parameters such as wheel slip ratio and wheel acceleration, but also directly affect the accuracy of vehicle dynamics models and the real-time performance of control decisions.

[0003] In related technologies, the raw signal output by the wheel speed sensor itself is usually used as the vehicle's wheel speed signal. When an abnormal wheel speed occurs, the wheel speed signals of other wheels are used as reference signals to reconstruct and replace the abnormal wheel speed. However, under some complex dynamic conditions, wheels may slip or lock up. In this case, the wheel speed signal cannot accurately reflect the actual vehicle speed, and the system has difficulty accurately identifying and correcting such signal anomalies.

[0004] Therefore, how to accurately identify abnormal wheel speeds and adaptively correct reference wheel speeds under various abnormal operating conditions, so as to improve the reliability, safety and adaptability of vehicle stability control systems in complex environments, has become a current research focus in the field of vehicle control. Summary of the Invention

[0005] This application provides a method, apparatus, and device for correcting abnormal wheel speeds in vehicles, which can accurately identify abnormal wheel speeds and adaptively correct reference wheel speeds under various abnormal operating conditions, thereby improving the reliability, safety, and adaptability of the vehicle stability control system in complex environments.

[0006] This application provides a method for correcting abnormal wheel speeds of a vehicle. The method includes: acquiring four-wheel wheel speed signals and multi-source vehicle speed signals of a target vehicle, wherein the multi-source vehicle speed signals include GPS vehicle speed signals, IMU-calculated vehicle speed signals, and motor-calculated vehicle speed signals; for any one of the four-wheel wheel speed signals, performing anomaly detection on the wheel speed signal based on the multi-source vehicle speed signals, and determining whether the wheel speed signal is an abnormal wheel speed signal based on the detection result; if the wheel speed signal is determined to be an abnormal wheel speed signal, acquiring state information of the vehicle speed signal for any one of the multi-source vehicle speed signals, and determining a target confidence level of the vehicle speed signal based on the state information; and performing weighted fusion on each of the vehicle speed signals and their corresponding target confidence levels to determine a reference wheel speed of the target vehicle, wherein the reference wheel speed is used to provide a correction benchmark for the abnormal wheel speed signal.

[0007] In one embodiment, anomaly detection of the wheel speed signal is performed based on the multi-source vehicle speed signal. Determining whether the wheel speed signal is an abnormal wheel speed signal based on the detection result includes: performing signal quality detection and wheel speed state detection on the wheel speed signal respectively, and performing dynamic detection on the wheel speed signal based on the multi-source vehicle speed signal. If any of the signal quality detection result, dynamic detection result, and wheel speed state detection result of the wheel speed signal indicates an anomaly, the wheel speed signal is determined to be an abnormal wheel speed signal.

[0008] In one embodiment, signal quality detection of the wheel speed signal includes: determining the wheel speed sensor corresponding to the wheel speed signal, acquiring the power supply voltage of the wheel speed sensor, and determining whether the power supply voltage is within a preset voltage range; determining the conversion relationship between the original value and the physical value of the wheel speed signal, verifying whether the conversion relationship conforms to the communication protocol, and evaluating the accuracy of the wheel speed signal; and determining the signal quality detection result of the wheel speed signal based on the judgment result, the verification result, and the evaluation result, wherein the physical value is obtained by converting the original value using the communication protocol.

[0009] In one embodiment, dynamic detection of the wheel speed signal based on the multi-source vehicle speed signals includes: performing difference integration on the wheel speed signal and the vehicle speed signal for each vehicle speed signal in the multi-source vehicle speed signals to obtain the cumulative deviation between the wheel speed signal and the corresponding vehicle speed signal, determining a preset threshold for the corresponding vehicle speed signal, comparing the cumulative deviation with the preset threshold, and determining the dynamic detection result of the wheel speed signal based on the comparison result of each vehicle speed signal.

[0010] In one embodiment, the wheel speed state detection includes: acquiring the historical slip rate of the wheel speed signal, determining a slip rate reference range of the wheel speed signal based on the historical slip rate, acquiring the real-time slip rate of the wheel speed signal, determining whether the real-time slip rate is within the slip rate reference range, and determining the wheel speed state detection result of the wheel speed signal based on the determination result.

[0011] In one embodiment, the state information includes signal state information and sensor state information; determining the target confidence level of the vehicle speed signal based on the state information includes: if both the signal state information and the sensor state information are valid, determining the target confidence level as a first confidence level; if the signal state information is valid but the sensor state information is invalid, determining the target confidence level as a second confidence level; and if both the signal state information and the sensor state information are invalid, determining the target confidence level as a third confidence level.

[0012] In one embodiment, the vehicle speed signal further includes a reference vehicle speed signal, and the state information includes a noise level; determining the target confidence level of the reference vehicle speed signal based on the state information includes: sorting the four wheel speed signals; determining an initial confidence level for each wheel speed signal based on the sorting result; for each wheel speed signal, determining a reference confidence level for the wheel speed signal based on the noise level of the wheel speed signal and the initial confidence level; determining a target wheel speed signal from the four wheel speed signals based on the reference confidence level of each wheel speed signal; using the target wheel speed signal as the reference vehicle speed signal; and using the reference confidence level corresponding to the target wheel speed signal as the target confidence level.

[0013] In one embodiment, the noise level of the wheel speed signal is determined as follows: the wheel acceleration of the wheel speed signal is determined, and the historical wheel acceleration corresponding to the wheel speed signal is obtained; the noise level of the wheel speed signal is determined based on the difference between the wheel acceleration and the historical wheel acceleration, wherein the noise level characterizes the gain coefficient used to adjust the initial confidence level to the reference confidence level.

[0014] A second aspect of this application provides a vehicle abnormal wheel speed correction device, the device comprising: a signal acquisition unit, configured to acquire four-wheel wheel speed signals and multi-source vehicle speed signals of a target vehicle, wherein the multi-source vehicle speed signals include GPS vehicle speed signals, IMU-calculated vehicle speed signals, and motor-calculated vehicle speed signals; an anomaly detection unit, configured to perform anomaly detection on any wheel speed signal among the four-wheel wheel speed signals based on the multi-source vehicle speed signals, and determine whether the wheel speed signal is an abnormal wheel speed signal based on the detection result; an information processing unit, configured to, when the wheel speed signal is determined to be an abnormal wheel speed signal, acquire state information of any wheel speed signal among the multi-source vehicle speed signals, and determine a target confidence level of the vehicle speed signal based on the state information; and an anomaly correction unit, configured to perform weighted fusion of each of the vehicle speed signals and their corresponding target confidence levels to determine a reference wheel speed of the target vehicle, wherein the reference wheel speed is used to provide a correction benchmark for the abnormal wheel speed signal.

[0015] A third aspect of this application provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the vehicle abnormal wheel speed correction method described in the first aspect above.

[0016] The technical solution provided in one or more embodiments of this application improves vehicle driving safety and stability by identifying abnormal wheel speeds and adaptively correcting reference wheel speeds using multi-source vehicle speed signals. Specifically, through multi-dimensional verification and anomaly detection of multi-source vehicle speed signals, abnormal wheel speed signals caused by complex conditions such as sensor failure, wheel slippage, or wheel lock-up can be effectively identified. The confidence level is then dynamically adjusted and weighted fused based on real-time status information of the multi-source vehicle speed signals, resulting in a more reliable reference wheel speed. This corrects abnormal wheel speed signals, effectively reducing misjudgments and malfunctions caused by abnormal wheel speed signals, preventing vehicle stability function failure, and maintaining the accuracy and reliability of wheel speed signals even under complex conditions. This improves the reliability, safety, and adaptability of the vehicle stability control system in complex environments. Simultaneously, the corrected wheel speed signal smoothly transitions to a normal state, preventing sudden signal changes from causing instantaneous disturbances to the vehicle control system, further enhancing vehicle driving safety and stability.

[0017] As can be seen, the technical solution provided in this application achieves accurate identification of abnormal wheel speeds and adaptive correction of reference wheel speeds under various abnormal operating conditions. Simultaneously, it improves the reliability, safety, and adaptability of the vehicle stability control system in complex environments. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram illustrating the steps of a method for correcting abnormal wheel speeds of a vehicle, provided in an embodiment of this application.

[0020] Figure 2 This is a schematic flowchart illustrating a wheel speed signal anomaly detection method according to one embodiment of this application.

[0021] Figure 3 A schematic diagram of a vehicle abnormal wheel speed correction device provided in one embodiment of this application;

[0022] Figure 4 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Furthermore, the use of terms such as "first," "second," etc., in this application is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of embodiments in this application, unless otherwise stated, "multiple" means two or more. Additionally, the use of "based on" or "according to" implies openness and inclusiveness, because processes, steps, calculations, or other actions "based on" or "according to" one or more of the stated conditions or values ​​may in practice be based on additional conditions or beyond the stated values.

[0025] In vehicle active safety and stability control systems, such as Electronic Stabilization Program (ESP), Traction Control System (TCS), and Electronic Stability Program (ESP), accurate and reliable wheel speed signals are fundamental to effective control, directly impacting the accuracy of the vehicle dynamics model and the real-time performance of control decisions. Specifically, these systems require assessments of road surface and vehicle conditions based on changes in wheel speed and other information. For example, wheel slip can be calculated using wheel speed to determine if wheel lockup or slippage is occurring; wheel acceleration can be calculated using wheel speed to assess the vehicle's potential road surface adhesion. Related technologies typically use the raw signal output from the wheel speed sensor itself as the vehicle's wheel speed signal. When abnormal wheel speeds occur, the wheel speed signals of other wheels are used as reference signals to reconstruct and replace the abnormal wheel speed.

[0026] However, in practical applications, vehicles may encounter various complex operating conditions, leading to limitations in traditional wheel speed signal processing methods and consequently affecting the normal operation of vehicle stability functions. Specifically, in complex dynamic conditions such as low-friction surfaces, asymmetric conditions, or during severe acceleration and deceleration, wheels may slip or lock up. In these situations, the wheel speed signal cannot accurately reflect the actual vehicle speed, and traditional methods struggle to accurately identify and process such signal anomalies. Furthermore, when the wheel speed sensor used to collect the wheel speed signal malfunctions, the wheel speed signal also fails to accurately reflect the actual vehicle speed. For example, hardware failures in the sensor (such as short circuits, open circuits, or electromagnetic interference) can cause the wheel speed signal to completely fail or deviate significantly. Similarly, software failures (such as signal drift or transient noise) can lead to unreliable wheel speed data, limiting the robustness and safety of the vehicle control system under abnormal operating conditions.

[0027] In view of the above, one or more embodiments of this application provide a method, apparatus and device for correcting abnormal wheel speeds of a vehicle, which can solve the above problems. By accurately identifying abnormal wheel speeds and adaptively correcting reference wheel speeds, the accuracy and reliability of wheel speed signals can be maintained even under complex working conditions, thereby improving the reliability, safety and adaptability of the vehicle stability control system in complex environments.

[0028] Please see Figure 1 One embodiment of this application provides a method for correcting abnormal wheel speeds of a vehicle, which may include the following steps:

[0029] S1: Acquire the four-wheel speed signals and multi-source vehicle speed signals of the target vehicle, wherein the multi-source vehicle speed signals include GPS vehicle speed signals, IMU inverse-calculated vehicle speed signals and motor inverse-calculated vehicle speed signals.

[0030] The aforementioned four-wheel speed signals include the individual wheel speed signals of each of the four wheels of the target vehicle. These signals are used to identify abnormal wheel speed signals and to perform multi-dimensional verification and correction of the wheel speed signals through the fusion of multi-source vehicle speed signals. The aforementioned multi-source vehicle speed signals can be understood as the overall vehicle speed signals determined by various sensors or methods, providing multiple independent vehicle speed references for verification and correction of the wheel speed signals. Specifically, the aforementioned GPS vehicle speed signal is the vehicle speed signal measured by the GPS (Global Positioning System); the aforementioned IMU-calculated vehicle speed signal is the vehicle speed signal determined based on the longitudinal acceleration measured by the vehicle's IMU (Inertial Navigation System) unit; and the aforementioned motor-calculated vehicle speed signal is the vehicle speed signal determined based on the motor speed.

[0031] In one embodiment, the IMU-calculated vehicle speed signal is determined based on the vehicle's longitudinal acceleration measured by the IMU unit on the vehicle. Specifically, the vehicle's longitudinal acceleration signal is measured by the IMU unit on the vehicle, and the IMU-calculated vehicle speed is obtained by time integration of the longitudinal acceleration signal, which serves as the IMU-calculated vehicle speed signal. This IMU-calculated vehicle speed signal can reflect the dynamic changes of the vehicle in real time and is unaffected by wheel slippage or wheel lockup.

[0032] In one embodiment, the motor-calculated vehicle speed signal is determined based on the motor rotation speed. Specifically, the motor rotation speed is measured and combined with the vehicle's power transmission parameters (such as tire rolling radius, gear ratio, etc.) to calculate the motor-calculated vehicle speed signal. This motor-calculated vehicle speed signal has high reliability during normal vehicle operation.

[0033] S3: For any wheel speed signal among the four wheel speed signals, perform anomaly detection on the wheel speed signal based on the multi-source vehicle speed signal, and determine whether the wheel speed signal is an abnormal wheel speed signal based on the detection result of the wheel speed signal.

[0034] The aforementioned abnormal wheel speed signals can be understood as distorted wheel speed signals caused by sensor malfunction, wheel slippage, wheel lock-up, or other complex operating conditions, which cannot accurately reflect the actual motion state of the wheels. Since the four wheels of a vehicle may be in different operating conditions—for example, one wheel slipping while the other is normal—it is necessary to detect the wheel speed signal of each wheel separately to ensure the reliability of each signal. Furthermore, vehicle speed signals from different sources have different advantages and limitations. Anomaly detection of wheel speed signals using multi-source vehicle speed signals verifies the accuracy of the wheel speed signals. This anomaly detection method can more comprehensively determine whether wheel speed signals are abnormal, thereby reconstructing and updating abnormal wheel speed signals, ensuring improved vehicle driving safety and stability.

[0035] For example, the above-mentioned anomaly detection can be performed by directly comparing multi-source vehicle speed signals and wheel speed signals. Under normal operating conditions, the two should be approximately the same. Alternatively, the above-mentioned anomaly detection can perform more detailed state and functional checks on multi-source vehicle speed signals and wheel speed signals, including signal quality detection, dynamics detection, and wheel speed state detection. This is achieved by comparing parameters and determining thresholds to identify whether each wheel speed signal is abnormal. Through this multi-dimensional verification and anomaly detection process, misjudgments and erroneous operations caused by abnormal wheel speed signals can be effectively reduced, preventing vehicle stability function failures due to abnormal signals.

[0036] S5: If the wheel speed signal is determined to be an abnormal wheel speed signal, for any one of the multi-source vehicle speed signals, obtain the state information of the vehicle speed signal, and determine the target confidence level of the vehicle speed signal based on the state information.

[0037] The aforementioned status information is used to evaluate the quality and reliability of the corresponding vehicle speed signal, characterizing the validity of the vehicle speed signal and the working status of the sensor. For example, the validity of the vehicle speed signal can be determined by whether it conforms to the software communication protocol, or by whether the signal is within a reasonable range and whether there is noise or data loss. For example, the working status of the sensor corresponding to the vehicle speed signal can be determined by judging the hardware fault results, and then the target confidence level of the vehicle speed signal can be determined based on the status information. Since vehicle driving conditions are constantly changing, the aforementioned status information can reflect the current reliability of the vehicle speed signal in real time, thereby dynamically adjusting the confidence level. Specifically, the sensor corresponding to the GPS vehicle speed signal is the signal receiver in the GPS system; the sensor corresponding to the IMU-calculated vehicle speed signal is the triaxial accelerometer and gyroscope used in the IMU unit to sense the vehicle's inertial motion; and the sensor corresponding to the motor-calculated vehicle speed signal is the motor speed sensor.

[0038] In this embodiment, if the wheel speed signal is determined to be an abnormal wheel speed signal, a subsequent confidence assessment process is triggered, that is, the target confidence level of each vehicle speed signal is determined based on the status information. The target confidence level can be understood as the reliability of the vehicle speed signal determined based on the status information. The higher the target confidence level, the more reliable the vehicle speed signal, and the more reliable it is, serving as a reference for correcting abnormal wheel speed signals. Only after confirming the existence of an abnormal wheel speed signal is it necessary to assess other vehicle speed signals. The target confidence level is dynamically adjusted based on the real-time status information of multiple source vehicle speed signals for weighted fusion, ensuring that the most reliable vehicle speed signal is selected as a reference under different operating conditions. If all wheel speed signals are normal, no additional confidence assessment is required.

[0039] S7: Weighted fusion is performed on each of the vehicle speed signals and their corresponding target confidence levels to determine the reference wheel speed of the target vehicle. The reference wheel speed is used to provide a correction benchmark for the abnormal wheel speed signals.

[0040] In this embodiment, when the wheel speed signal is determined to be an abnormal wheel speed signal, vehicle speed signals from different sources are weighted and combined according to their target confidence levels to obtain a more reliable reference wheel speed. The target confidence level reflects the real-time reliability of the signal. Weighted fusion can dynamically adjust the weights of each signal based on the confidence level, ensuring that the most reliable reference wheel speed can be generated under different operating conditions. For example, the reference wheel speed is determined in the following manner: ,in, For reference wheel speed, For a certain vehicle speed signal, For vehicle speed signal The corresponding target confidence level. By weighted fusing of multi-source vehicle speed signals, the actual driving state of the vehicle can be better reflected, ensuring that signals with high confidence levels contribute more to the reference wheel speed. The fused wheel speed is then used as the reference wheel speed to correct abnormal wheel speed signals.

[0041] In this embodiment, abnormal wheel speed signals are corrected based on reference wheel speeds. For example, the preset wheel speed value of the abnormal wheel speed signal can be directly replaced by the reference wheel speed, i.e., at the input interface of subsequent vehicle stability control algorithms (such as ABS, ESC, TCS). For example, the control algorithm uses this reference wheel speed to calculate slip ratio, distribute braking force, and make other decisions regarding the abnormal wheel speed signal. Based on the abnormal operating conditions represented by the decisions, it issues control execution commands to adjust the braking pressure required by the abnormal wheel corresponding to the abnormal wheel speed signal, thereby correcting the abnormal wheel speed. For example, if the slip ratio is too large, it indicates that the abnormal wheel is about to lock up; if the abnormal wheel speed is much higher than the reference speed, it indicates that the drive wheel is slipping. The corrected wheel speed signal can smoothly transition to a normal state, avoiding instantaneous disturbances to the vehicle control system caused by signal abrupt changes.

[0042] Based on the above ideas, the technical solution provided in this embodiment of the application improves the vehicle's driving safety and stability by identifying abnormal wheel speeds and adaptively correcting reference wheel speeds using multi-source vehicle speed signals. Specifically, through multi-dimensional verification and anomaly detection of multi-source vehicle speed signals, abnormal wheel speed signals caused by complex conditions such as sensor failure, wheel slippage, or wheel lock-up can be effectively identified. The confidence level is then dynamically adjusted and weighted fused based on the real-time status information of the multi-source vehicle speed signals, resulting in a more reliable reference wheel speed. This corrects abnormal wheel speed signals, effectively reducing misjudgments and malfunctions caused by abnormal wheel speed signals, preventing vehicle stability function failure, and maintaining the accuracy and reliability of wheel speed signals even under complex conditions. This improves the reliability, safety, and adaptability of the vehicle stability control system in complex environments. Simultaneously, the corrected wheel speed signal smoothly transitions to a normal state, preventing sudden signal changes from causing instantaneous disturbances to the vehicle control system, further enhancing the vehicle's driving safety and stability.

[0043] In one implementation, please refer to Figure 2 Based on step S3 above, the anomaly detection includes signal quality detection, dynamics detection, and wheel speed state detection. Signal quality detection, dynamics detection, and wheel speed state detection are performed on the wheel speed signals based on multi-source vehicle speed signals. Based on the obtained signal quality detection results, dynamics detection results, and wheel speed state detection results, it is determined whether the wheel speed signal is an abnormal wheel speed signal.

[0044] Specifically, the wheel speed signal is subjected to signal quality detection and wheel speed state detection, and dynamic detection is performed on the wheel speed signal based on multi-source vehicle speed signals. If any of the detection results in the signal quality detection result, dynamic detection result, and wheel speed state detection result of the wheel speed signal is identified as an abnormal wheel speed signal.

[0045] In this embodiment, signal quality detection is performed on each wheel speed signal in the following manner, including wheel speed sensor fault detection and wheel speed signal accuracy detection: The wheel speed sensor corresponding to the wheel speed signal is identified, the power supply voltage of the wheel speed sensor is acquired, and it is determined whether the power supply voltage is within a preset voltage range to complete wheel speed sensor fault detection. The conversion relationship between the original value and the physical value of the wheel speed signal is determined, the conversion relationship is verified to conform to the communication protocol, and the accuracy of the wheel speed signal is evaluated to complete wheel speed signal accuracy detection. Further, based on the judgment result, verification result, and accuracy detection result, the signal quality detection result of the wheel speed signal is determined, wherein the aforementioned physical value is obtained by converting the aforementioned original value using the communication protocol. Specifically, if either the judgment result or the verification result of the wheel speed signal indicates an anomaly, the wheel speed signal is determined to be an abnormal wheel speed signal.

[0046] In one embodiment, signal quality detection is performed on each wheel speed signal, including wheel speed sensor fault detection. Specifically, the system checks whether the wheel speed sensor corresponding to each wheel speed signal has a power supply fault, and determines whether the actual voltage fed back by the sensor is within the normal voltage range. This helps determine if there are faults such as insufficient voltage or internal short circuits / open circuits. If a fault is found, the corresponding wheel speed signal is considered an abnormal wheel speed signal. For example, the normal voltage range is 4.5V to 5V. If the actual voltage of the wheel speed signal is less than 4V, the sensor voltage is considered insufficient, and the wheel speed signal is considered abnormal. If the actual voltage of the wheel speed signal is 0V, the sensor is considered to have an internal short circuit or open circuit problem, and the wheel speed signal is considered abnormal.

[0047] In one embodiment, signal quality detection is performed on each wheel speed signal, including wheel speed signal accuracy detection. Specifically, for any wheel speed signal, it is first confirmed whether the wheel speed signal is a valid value. If the wheel speed signal is invalid, it is directly determined to be an abnormal wheel speed signal. If the wheel speed signal is valid, its accuracy is further determined to meet the requirements through the conversion relationship between the original value and the physical value of the wheel speed signal. Specifically, the original value is the unprocessed primary wheel speed signal (analog signal) directly generated by the wheel speed sensor, and the physical value is the wheel speed value (mathematical signal) that can be understood and used for calculation and has a clear physical meaning and unit. The conversion relationship can be expressed as: ,in, For physical values, The original value, As the conversion factor, The offset is calculated by dividing the physical value by the original value, obtaining the quotient and remainder. The quotient, conversion factor, remainder, and offset are then compared. If any discrepancy exists, the signal accuracy is considered to be unsatisfactory, and the wheel speed signal is determined to be an abnormal wheel speed signal.

[0048] In this embodiment, each wheel speed signal is dynamically detected in the following manner, including GPS vehicle speed signal verification, IMU inverse vehicle speed signal verification, and motor inverse vehicle speed signal verification: For each vehicle speed signal in the multi-source vehicle speed signal, the wheel speed signal and the vehicle speed signal are integrated to obtain the cumulative deviation between the wheel speed signal and the corresponding vehicle speed signal, and a preset threshold for the corresponding vehicle speed signal is determined. The cumulative deviation is compared with the preset threshold, and the dynamic detection result of the wheel speed signal is determined based on the comparison results of each vehicle speed signal. Specifically, for any wheel speed signal, if its cumulative deviation is greater than the above-mentioned preset threshold, the wheel speed signal is determined to be an abnormal wheel speed signal.

[0049] In one embodiment, based on the aforementioned GPS vehicle speed signal, a GPS vehicle speed signal verification is performed on each wheel speed signal. Normally, the wheel speed signal is close to the GPS vehicle speed signal. If a wheel speed signal is consistently higher or lower than the GPS vehicle speed signal, it may indicate that the wheel is slipping or locked, or that the wheel speed sensor itself is malfunctioning, causing signal distortion. Therefore, for each wheel speed signal... Calculation of GPS vehicle speed signals The difference is calculated by integrating this difference over time to obtain the cumulative deviation. This cumulative deviation is then used to accumulate the instantaneous deviations of the wheel speed signal. The accumulated deviation is compared with a preset threshold. If the cumulative deviation exceeds the preset threshold, the wheel speed signal is considered abnormal. The integral value gradually increases if there is a continuous deviation, while the integral value remains relatively small if it is only a momentary interference, helping to avoid misjudgments caused by momentary interference.

[0050] In another embodiment, based on the vehicle speed signal calculated by the IMU, an IMU-calculated vehicle speed signal verification is performed on each wheel speed signal. The vehicle speed signal is reconstructed using the longitudinal acceleration of the vehicle's IMU as the IMU-calculated vehicle speed signal. Then, the difference between the IMU-calculated vehicle speed signal and the wheel speed signal is integrated over time to effectively detect the cumulative deviation of the wheel speed signal. By comparing the cumulative deviation with a preset threshold, if the deviation exceeds the preset threshold, the wheel speed signal is considered abnormal. Under normal circumstances, the wheel speed signal should be consistent with the IMU-calculated vehicle speed signal, but significant deviations can occur due to slippage, sensor malfunction, etc. Specifically, the vehicle speed signal is reconstructed using the longitudinal acceleration of the vehicle's IMU in the following manner: ,in, Indicates the longitudinal acceleration of the IMU. The initial velocity, The sampling time is typically 10ms. The initial speed mentioned above can be obtained from GPS vehicle speed signals, the average wheel speed of the previous moment, or other methods when the system starts up.

[0051] In another embodiment, based on the aforementioned motor-calculated vehicle speed signal, a motor-calculated vehicle speed signal verification is performed on each wheel speed signal. The vehicle speed signal is reconstructed using the motor rotation speed as the motor-calculated vehicle speed signal. Then, the difference between the motor-calculated vehicle speed signal and the wheel speed signal is integrated over time to effectively detect the cumulative deviation of the wheel speed signal. By comparing the cumulative deviation with a preset threshold, if the deviation exceeds the preset threshold, the wheel speed signal is considered abnormal. Specifically, the vehicle speed signal is reconstructed using the vehicle's IMU longitudinal acceleration in the following manner: ,in, The vehicle speed is calculated by inversely from the motor speed, where N is the motor speed. For tire rolling radius, The transmission ratio represents the proportional relationship between the motor speed and the wheel speed.

[0052] The aforementioned dynamic detection can include any one or a combination of the three embodiments described above. When any detection result indicates an abnormality, the wheel speed signal is determined to be an abnormal wheel speed signal.

[0053] In this embodiment, wheel speed status detection is performed on each wheel speed signal in the following manner, including wheel speed change trend verification: the historical slip rate of the wheel speed signal is obtained; a slip rate reference range for the wheel speed signal is determined based on the historical slip rate; the real-time slip rate of the wheel speed signal is obtained; it is determined whether the real-time slip rate is within the slip rate reference range; and the wheel speed status detection result of the wheel speed signal is determined based on the determination result. Specifically, for any wheel speed signal, if its real-time slip rate exceeds the slip rate reference range, the wheel speed signal is determined to be an abnormal wheel speed signal.

[0054] In one embodiment, the real-time slip rate of the wheel speed signal is verified by referring to historical slip rate changes. If the slip rate remains within the range of 0-3% for the first four cycles (10ms is considered one cycle), then the slip rate reference range is determined to be 0-3%, indicating that the wheel speed state should be stable in the recent period. If the real-time slip rate exceeds the above slip rate reference range, then wheel slippage is considered, and the wheel speed signal is determined to be an abnormal wheel speed signal. The real-time slip rate or historical slip rate of the wheel speed signal is determined as follows: ,in, It represents the current reference vehicle speed (e.g., GPS vehicle speed signal). This is the current wheel speed signal. This represents the slip ratio.

[0055] The technical solution provided in this embodiment detects wheel speed signals anomalies through three dimensions: signal quality detection, dynamics detection, and wheel speed state detection. Signal quality detection includes fault detection and signal accuracy testing of the wheel speed sensor. Dynamics detection involves comparing and analyzing the wheel speed signal with GPS vehicle speed signals, IMU-calculated vehicle speed signals, and motor-calculated vehicle speed signals. Wheel speed state detection assesses the stability of the wheel speed signal based on historical and real-time slip rate data. This enables accurate judgment of wheel speed signal anomalies, effectively avoiding the limitations of single detection methods and reducing misjudgments of wheel speed signal anomalies caused by transient interference, sensor failure, or wheel slippage / lockup. It provides a reliable basis for subsequent accurate correction of abnormal wheel speed signals, further improving the response accuracy and reliability of the vehicle stability control system.

[0056] In one implementation, based on step S5 above, the aforementioned state information includes signal state information and sensor state information. The target confidence level of the vehicle speed signal is determined based on this state information. Specifically, the signal state information characterizes the integrity and reliability of the vehicle speed signal itself. Its validity is determined by judging whether the vehicle speed signal conforms to the software communication protocol (CAN communication protocol), and the signal validity judgment result is used as the signal state information. The aforementioned sensor state information characterizes whether the sensor's hardware state is valid, and the sensor validity judgment result is used as the sensor state information. Examples include whether the sensor is working normally, whether there is a fault, and whether the power supply is normal.

[0057] Specifically, if both signal state information and sensor state information are valid, the target confidence level is set to the first confidence level (e.g., 25%). If the signal state information is valid but the sensor state information is invalid, the target confidence level is set to the second confidence level (e.g., 15%). If both signal state information and sensor state information are invalid, the target confidence level is set to the third confidence level (e.g., 10%). It should be noted that in practical applications, there is no situation where the signal state information is invalid while the sensor state information is valid.

[0058] In one embodiment, the target confidence level of the GPS vehicle speed signal is determined based on its state information. Specifically, the signal state information corresponding to the GPS vehicle speed signal is its own state information, and the sensor state information corresponding to the GPS vehicle speed signal is the hardware state information of the signal receiver in the GPS system. The system checks whether the GPS vehicle speed signal conforms to the communication protocol, for example, confirming whether the relevant identifiers of the GPS vehicle speed signal in the CAN message were received on time, whether the data length is correct, and whether the signal value is within the agreed range. If the GPS vehicle speed signal conforms to the communication protocol, its signal state information is considered valid. Furthermore, the sensor state corresponding to the GPS vehicle speed signal is checked; if the sensor state is normal, its sensor state information is considered valid.

[0059] In one embodiment, the target confidence level of the IMU-calculated vehicle speed signal is determined based on its state information. Specifically, the signal state information corresponding to the IMU-calculated vehicle speed signal is the state information of the IMU longitudinal acceleration signal, and the sensor state information corresponding to the IMU-calculated vehicle speed signal is the hardware state information of the triaxial accelerometer and gyroscope. The IMU longitudinal acceleration signal is checked to see if it conforms to the communication protocol; if it does, its signal state information is considered valid. Furthermore, the sensor state corresponding to the IMU longitudinal acceleration signal is checked; if the sensor state is normal, its sensor state information is considered valid.

[0060] In one embodiment, the target confidence level of the motor-calculated vehicle speed signal is determined based on its state information. Specifically, the signal state information corresponding to the motor-calculated vehicle speed signal is the state information of the motor speed signal, and the sensor state information corresponding to the motor-calculated vehicle speed signal is the hardware state information of the motor speed sensor. The motor speed signal is checked to see if it conforms to the communication protocol; if it does, its signal state information is considered valid. Furthermore, the state of the sensor corresponding to the motor-calculated vehicle speed signal, i.e., the motor speed sensor, is checked; if the motor speed sensor is functioning normally, its sensor state information is considered valid.

[0061] The technical solution provided in this embodiment adjusts the target confidence level of multi-source vehicle speed signals by analyzing signal state information and sensor state information, thereby achieving adaptive correction of the reference wheel speed. Specifically, for any vehicle speed signal, the target confidence level of that signal is adjusted based on its corresponding signal state information and sensor state information, using a pre-set confidence assessment mechanism, to generate a reference signal based on the target confidence level. By performing dual judgments on multi-source vehicle speed signals at both the signal level and the sensor hardware level, the real-time reliability of various vehicle speed signals under different operating conditions can be accurately reflected. Simultaneously, the weights of different vehicle speed signals are dynamically adjusted, avoiding confidence deviations caused by single-dimensional evaluation. This ensures the accuracy and reliability of the reference wheel speed under different operating conditions, thereby improving the accuracy and stability of abnormal wheel speed correction and ultimately enhancing vehicle driving safety and stability.

[0062] In one implementation, the vehicle speed signal further includes a reference vehicle speed signal, and the state information includes a noise level; a target confidence level for the reference vehicle speed signal is determined based on the state information. Specifically, the four wheel speed signals are sorted, and an initial confidence level for each wheel speed signal is determined based on the sorting result. For example, the initial confidence levels are set to 25%, 20%, 15%, and 10% respectively, in descending order of the wheel speed signals. For each wheel speed signal, a reference confidence level is determined based on the noise level and the initial confidence level; that is, the initial confidence level is dynamically adjusted using the noise level to adjust it to the reference confidence level, where the noise level serves as a gain coefficient for adjusting the initial confidence level. Based on the reference confidence level of each wheel speed signal, a target wheel speed signal and its corresponding reference confidence level are determined from the four wheel speed signals. The target wheel speed signal is used as the reference vehicle speed signal, and the reference confidence level corresponding to the target wheel speed signal is used as the target confidence level. For example, among the four wheel speed signals, the wheel speed signal with the highest reference confidence is taken as the target wheel speed signal, and the reference confidence is taken as the target confidence.

[0063] In this embodiment, the noise level of the wheel speed signal is determined as follows: the wheel acceleration of the wheel speed signal is determined, and the historical wheel acceleration corresponding to the wheel speed signal is obtained. Based on the difference between the wheel acceleration and the historical wheel acceleration, the noise level of the wheel speed signal is determined, wherein the noise level represents the gain coefficient used to adjust the initial confidence level to the reference confidence level. For example, if the difference is greater than 80, the noise level is set to 0%; if the difference is between 60 and 80, the noise level is set to 30%; if the difference is between 30 and 60, the noise level is set to 60%; and if the difference is less than 30, the noise level is set to 100%. Further, the product of the noise level and the initial confidence level is used as the reference confidence level.

[0064] In one embodiment, the GPS vehicle speed signal, the IMU-calculated vehicle speed signal, the motor-calculated vehicle speed signal, and the reference wheel speed signal are used as multi-source vehicle speed signals. Each vehicle speed signal and its corresponding target confidence level are weighted and fused in the following manner: ,in, , , and These represent the GPS vehicle speed signal, the IMU-calculated vehicle speed signal, the motor-calculated vehicle speed signal, and the reference wheel speed signal, respectively. , , and These represent the target confidence levels corresponding to GPS vehicle speed signals, IMU-calculated vehicle speed signals, motor-calculated vehicle speed signals, and reference wheel speed signals, respectively.

[0065] The technical solution provided in this embodiment incorporates a reference vehicle speed signal into multi-source vehicle speed signals, further improving the accuracy of reference signal generation. Specifically, by introducing a reference vehicle speed signal and dynamically adjusting the confidence level of the wheel speed signal in conjunction with noise levels, the compositional dimensions of the multi-source vehicle speed signal are enriched, thereby further improving the accuracy and dynamic adaptability of confidence assessment. Furthermore, weighted fusion based on the expanded multi-source vehicle speed signals can integrate more dimensions of vehicle speed reference data, making the generated reference wheel speed more closely match the actual driving state of the vehicle. This further improves the accuracy of abnormal wheel speed correction, reduces interference from abnormal wheel speed signals on the vehicle stability control system, and ensures the safety and stability of vehicle operation.

[0066] Please see Figure 3 This application also provides a vehicle abnormal wheel speed correction device, the device comprising:

[0067] The signal acquisition unit 100 is used to acquire the four-wheel wheel speed signals and multi-source vehicle speed signals of the target vehicle, wherein the multi-source vehicle speed signals include GPS vehicle speed signals, IMU inverse-calculated vehicle speed signals and motor inverse-calculated vehicle speed signals.

[0068] The anomaly detection unit 200 is used to perform anomaly detection on any wheel speed signal among the four wheel speed signals based on the multi-source vehicle speed signals, and determine whether the wheel speed signal is an abnormal wheel speed signal based on the detection result of the wheel speed signal.

[0069] The information processing unit 300 is used to, when the wheel speed signal is determined to be an abnormal wheel speed signal, acquire the state information of any vehicle speed signal among the multi-source vehicle speed signals, and determine the target confidence level of the vehicle speed signal based on the state information;

[0070] Anomaly correction unit 400 is used to perform weighted fusion of each of the vehicle speed signals and their corresponding target confidence levels to determine the reference wheel speed of the target vehicle. The reference wheel speed is used to provide a correction benchmark for the abnormal wheel speed signals.

[0071] in,

[0072] In one embodiment, the anomaly detection unit 200 is specifically used to perform signal quality detection and wheel speed state detection on any wheel speed signal among the four wheel speed signals, and to perform dynamic detection on the wheel speed signal based on the multi-source vehicle speed signals. If any of the detection results in the signal quality detection result, dynamic detection result, and wheel speed state detection result of the wheel speed signal indicates an anomaly, the wheel speed signal is determined to be an abnormal wheel speed signal.

[0073] In one embodiment, the information processing unit 300 is specifically configured to, when determining that the wheel speed signal is an abnormal wheel speed signal, acquire the state information of any vehicle speed signal among the multi-source vehicle speed signals; if both the signal state information and the sensor state information are valid, determine the target confidence level as a first confidence level; if the signal state information is valid and the sensor state information is invalid, determine the target confidence level as a second confidence level; and if both the signal state information and the sensor state information are invalid, determine the target confidence level as a third confidence level.

[0074] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0075] The vehicle abnormal wheel speed correction device in this application embodiment is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, or other devices that can provide the above functions.

[0076] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application, such as... Figure 4As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 4 Take a processor 10 as an example.

[0077] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0078] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0079] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0080] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0081] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0082] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0083] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0084] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0085] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer devices. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer devices according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0087] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0088] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0089] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0090] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0091] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

[0092] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for correcting abnormal wheel speeds in vehicles, characterized in that, The method includes: The four-wheel wheel speed signals and multi-source vehicle speed signals of the target vehicle are acquired, wherein the multi-source vehicle speed signals include GPS vehicle speed signals, IMU inverse-calculated vehicle speed signals and motor inverse-calculated vehicle speed signals; For any one of the four wheel speed signals, anomaly detection is performed on the wheel speed signal based on the multi-source vehicle speed signal, and based on the detection result of the wheel speed signal, it is determined whether the wheel speed signal is an abnormal wheel speed signal; If the wheel speed signal is determined to be an abnormal wheel speed signal, for any vehicle speed signal among the multi-source vehicle speed signals, the state information of the vehicle speed signal is obtained, and the target confidence level of the vehicle speed signal is determined based on the state information; The vehicle speed signals and their corresponding target confidence scores are weighted and fused to determine the reference wheel speed of the target vehicle. The reference wheel speed is used to provide a correction benchmark for the abnormal wheel speed signals. The state information includes signal state information and sensor state information; determining the target confidence level of the vehicle speed signal based on the state information includes: If both the signal state information and the sensor state information indicate that the signal is valid, the target confidence level is determined as the first confidence level. If the signal state information representation is valid and the sensor state information representation is invalid, the target confidence level is determined as the second confidence level. If both the signal state information and the sensor state information are invalid, the target confidence level is determined as the third confidence level.

2. The method according to claim 1, characterized in that, Anomaly detection is performed on the wheel speed signal based on the multi-source vehicle speed signal. Based on the detection result of the wheel speed signal, determining whether the wheel speed signal is an abnormal wheel speed signal includes: The wheel speed signals are subjected to signal quality detection and wheel speed state detection, and dynamic detection is performed on the wheel speed signals based on the multi-source vehicle speed signals. If any of the wheel speed signal's signal quality detection results, dynamics detection results, and wheel speed state detection results indicates an abnormality, the wheel speed signal is determined to be an abnormal wheel speed signal.

3. The method according to claim 2, characterized in that, Signal quality detection of the wheel speed signal includes: Identify the wheel speed sensor corresponding to the wheel speed signal, obtain the power supply voltage of the wheel speed sensor, and determine whether the power supply voltage is within a preset voltage range; Determine the conversion relationship between the original value and the physical value of the wheel speed signal, verify whether the conversion relationship conforms to the communication protocol, and evaluate the accuracy of the wheel speed signal; Based on the judgment results, verification results, and evaluation results, the signal quality detection result of the wheel speed signal is determined, wherein the physical value is obtained by communication protocol conversion based on the original value.

4. The method according to claim 2, characterized in that, Based on the multi-source vehicle speed signals, the dynamic detection of the wheel speed signals includes: For each vehicle speed signal in the multi-source vehicle speed signals, the wheel speed signal and the vehicle speed signal are integrated by difference to obtain the cumulative deviation between the wheel speed signal and the corresponding vehicle speed signal, and a preset threshold corresponding to the vehicle speed signal is determined. The cumulative deviation is compared with the preset threshold, and the dynamic detection result of the wheel speed signal is determined based on the comparison results of each vehicle speed signal.

5. The method according to claim 2, characterized in that, The wheel speed status detection includes: Obtain the historical slip ratio of the wheel speed signal, and determine the slip ratio reference range of the wheel speed signal based on the historical slip ratio; The real-time slip ratio of the wheel speed signal is obtained, and it is determined whether the real-time slip ratio is within the slip ratio reference range. Based on the determination result, the wheel speed state detection result of the wheel speed signal is determined.

6. The method according to claim 1, characterized in that, The vehicle speed signal also includes a reference vehicle speed signal, and the state information includes a noise level; determining the target confidence level of the reference vehicle speed signal based on the state information includes: The four wheel speed signals are sorted, and the initial confidence level of each wheel speed signal is determined based on the sorting result; For each wheel speed signal, a reference confidence level for the wheel speed signal is determined based on the noise level of the wheel speed signal and the initial confidence level. Based on the reference confidence level of each wheel speed signal, a target wheel speed signal is determined from the four wheel speed signals, the target wheel speed signal is used as the reference vehicle speed signal, and the reference confidence level corresponding to the target wheel speed signal is used as the target confidence level.

7. The method according to claim 6, characterized in that, The noise level of the wheel speed signal is determined as follows: Determine the wheel acceleration of the wheel speed signal, and obtain the historical wheel acceleration corresponding to the wheel speed signal; The noise level of the wheel speed signal is determined based on the difference between the wheel acceleration and the historical wheel acceleration, wherein the noise level characterizes the gain coefficient used to adjust the initial confidence level to the reference confidence level.

8. A vehicle abnormal wheel speed correction device, characterized in that, The device includes: The signal acquisition unit is used to acquire the four-wheel wheel speed signals and multi-source vehicle speed signals of the target vehicle, wherein the multi-source vehicle speed signals include GPS vehicle speed signals, IMU inverse-calculated vehicle speed signals and motor inverse-calculated vehicle speed signals; An anomaly detection unit is used to perform anomaly detection on any wheel speed signal among the four wheel speed signals based on the multi-source vehicle speed signals, and determine whether the wheel speed signal is an abnormal wheel speed signal based on the detection result of the wheel speed signal. An information processing unit is configured to, when determining that the wheel speed signal is an abnormal wheel speed signal, acquire state information of any vehicle speed signal from the multi-source vehicle speed signals, and determine a target confidence level of the vehicle speed signal based on the state information; wherein, the state information includes signal state information and sensor state information; determining the target confidence level of the vehicle speed signal based on the state information includes: if both the signal state information and the sensor state information are valid, determining the target confidence level as a first confidence level; if the signal state information is valid and the sensor state information is invalid, determining the target confidence level as a second confidence level; if both the signal state information and the sensor state information are invalid, determining the target confidence level as a third confidence level; An anomaly correction unit is used to perform weighted fusion of each of the vehicle speed signals and their corresponding target confidence levels to determine the reference wheel speed of the target vehicle. The reference wheel speed is used to provide a correction benchmark for the abnormal wheel speed signals.

9. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the vehicle abnormal wheel speed correction method according to any one of claims 1 to 7.

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