A wheel speed credibility evaluation method, system and device based on dynamic fusion

Abstract

This invention relates to a method, system, and apparatus for wheel speed credibility assessment based on dynamic fusion. The method includes: real-time acquisition of vehicle wheel speed, electronic parking brake status, and transmission gear position; determining the vehicle's predefined scenario mode and assessing the vehicle's wheel speed credibility based on the vehicle's wheel speed, electronic parking brake status, and transmission gear position; selecting a corresponding weight vector based on the vehicle's predefined scenario mode and dynamically weighting and fusing it with the vehicle's wheel speed credibility to obtain a wheel speed credibility fusion value. This invention significantly improves the accuracy and robustness of wheel speed credibility determination by introducing scenario-adaptive dynamic weights and continuous, refined assessment. It also intelligently handles evidence conflicts and automatically focuses on the most relevant evidence under different operating conditions, effectively suppressing false alarms due to stationary disturbances while ensuring signal continuity during actual start-up or low-speed driving, thus enhancing the overall performance and safety of the chassis control system.

Description

Technical Field

[0001] This invention relates to the field of vehicle control, and more specifically to a method, system, and device for wheel speed reliability assessment based on dynamic fusion. Background Technology

[0002] In vehicle chassis control systems, the accuracy of wheel speed signals is crucial.

[0003] Existing wheel speed processing techniques typically employ a weighted average method with fixed weights or fuse multiple reliability evaluation metrics by setting a simple threshold. These techniques can effectively process wheel speed when the wheel speed sensors and vehicle condition are normal; however, they still have significant drawbacks in certain scenarios: First, the fixed-weighted average method cannot adapt to different vehicle operating scenarios, such as strong parking and low-speed creep, which may lead to the risk of misjudgment in specific scenarios. Second, when there is a conflict between the evaluation indicators, such as the parking status indicating stationary while the wheel speed indicates movement, the fixed fusion strategy may not be able to make the optimal decision, resulting in insufficient robustness. Summary of the Invention

[0004] This invention provides a method, system, and apparatus for evaluating wheel speed reliability based on dynamic fusion, in order to solve at least one of the above-mentioned technical problems.

[0005] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A method for evaluating wheel speed credibility based on dynamic fusion, comprising: Real-time acquisition of vehicle wheel speed, electronic parking brake status, and transmission gear position; Based on the vehicle's wheel speed, electronic parking brake status, and transmission gear, determine the predefined scenario mode to which the vehicle belongs and assess the reliability of the vehicle's wheel speed. The corresponding weight vector is selected based on the predefined scene mode to which the vehicle belongs, and dynamically weighted and fused with the vehicle's wheel speed credibility to obtain a wheel speed credibility fusion value, in order to evaluate the vehicle's wheel speed credibility.

[0006] Based on the above technical solution, the present invention can be further improved as follows.

[0007] Furthermore, the predefined scenario modes include a forced parking mode, a parking crawl mode, and a normal driving mode; among which, The criteria for determining that a vehicle belongs to the aforementioned strong parking mode are: the electronic parking brake of the vehicle is in a clamped state or the vehicle's transmission gear is in parking gear. The criteria for determining that a vehicle belongs to the parking creep mode are: the vehicle is not in the strong parking mode and the vehicle's wheel speed is not higher than a preset wheel speed threshold. The criterion for determining that the vehicle belongs to the normal driving mode is: the vehicle's wheel speed is higher than the preset wheel speed threshold.

[0008] Furthermore, the wheel speed reliability includes the parking status reliability; evaluating the parking status reliability of a vehicle specifically includes: When the vehicle's electronic parking brake is in the clamped state or the vehicle's transmission is in the parking gear and the vehicle's wheel speed is not zero, a preset non-zero low confidence value is output as the confidence value of the vehicle's parking state; otherwise, a preset high confidence value is output as the confidence value of the vehicle's parking state.

[0009] Furthermore, the wheel speed reliability includes wheel speed consistency reliability; evaluating the wheel speed consistency reliability of a vehicle specifically includes: Using the second fastest wheel speed among the four wheel speeds of the vehicle as the reference wheel speed, calculate the deviations of the other three wheel speeds from the reference wheel speed; By using a predefined lookup table method, the deviations of the other three wheel speeds from the reference wheel speed are converted into continuous confidence values ​​between 0 and 1, thus obtaining the vehicle's wheel speed consistency confidence.

[0010] Furthermore, the wheel speed reliability includes the reliability of the wheel acceleration rationality; specifically, assessing the reliability of the vehicle's wheel acceleration rationality includes: Calculate the wheel acceleration of the vehicle based on the wheel speed, and take the absolute value to obtain the absolute value of the wheel acceleration; A continuous mapping function is used to map the ratio of the absolute value of wheel acceleration to a preset acceleration rationality threshold to a continuous confidence value between 0 and 1, thereby obtaining the confidence level of the vehicle's wheel acceleration rationality.

[0011] Furthermore, the mapping formula for the reasonableness and credibility of the vehicle's wheel acceleration is as follows: ; In the formula, This indicates the reasonableness and credibility of the stated wheel acceleration. This represents the absolute value of the wheel's acceleration. This indicates the preset acceleration reasonableness threshold.

[0012] Furthermore, the predefined scenario modes include a strong parking mode, a parking creep mode, and a normal driving mode; correspondingly, the weight vectors include a strong parking mode weight vector, a parking creep mode weight vector, and a normal driving mode weight vector. The corresponding weight vector is selected based on the predefined scene mode to which the vehicle belongs, and then dynamically weighted and fused with the vehicle's wheel speed confidence level, specifically as follows: When the vehicle belongs to the predefined scenario mode of the strong parking mode, the weight vector of the strong parking mode and the reliability of the vehicle's wheel speed are dynamically weighted and fused. When the predefined scenario mode to which the vehicle belongs is the parking creep mode, the parking creep mode weight vector and the vehicle's wheel speed credibility are dynamically weighted and fused. When the vehicle belongs to the predefined scenario mode, which is the normal driving mode, the weight vector of the normal driving mode and the vehicle's wheel speed credibility are dynamically weighted and fused.

[0013] Furthermore, the wheel speed credibility includes parking status credibility, wheel speed consistency credibility, and wheel acceleration rationality credibility; correspondingly, the weight vector includes parking status credibility weight, wheel speed consistency credibility weight, and wheel acceleration rationality credibility weight. The formula for dynamic weighted fusion is: ; In the formula, This represents the wheel speed reliability fusion value. This indicates the reliability of the stated parking status. This indicates the reliability of the wheel speed consistency. This indicates the reasonableness and credibility of the stated wheel acceleration. This represents the parking status confidence weight in the weight vector. This represents the wheel speed consistency credibility weight in the weight vector. This represents the weight of the wheel acceleration's reasonableness and credibility in the weight vector.

[0014] Based on the aforementioned wheel speed reliability assessment method based on dynamic fusion, this invention also provides a wheel speed reliability assessment system based on dynamic fusion.

[0015] A dynamic fusion-based wheel speed credibility assessment system, applied to the dynamic fusion-based wheel speed credibility assessment method described above, includes: The vehicle status acquisition module is used to acquire the vehicle's wheel speed, electronic parking brake status, and transmission gear position in real time. The scene recognition and wheel speed credibility assessment module is used to determine the predefined scene mode to which the vehicle belongs and to assess the credibility of the vehicle's wheel speed based on the vehicle's wheel speed, electronic parking brake status and transmission gear. The dynamic weighted fusion module is used to select the corresponding weight vector according to the predefined scene mode to which the vehicle belongs, and to dynamically weight and fuse it with the vehicle's wheel speed credibility to obtain a wheel speed credibility fusion value, so as to evaluate the vehicle's wheel speed credibility.

[0016] Based on the aforementioned wheel speed reliability assessment method based on dynamic fusion, the present invention also provides a wheel speed reliability assessment device based on dynamic fusion.

[0017] A wheel speed reliability assessment device based on dynamic fusion includes a processor, a memory, and a computer program stored in the memory. When the computer program is executed by the processor, it implements the wheel speed reliability assessment method based on dynamic fusion as described above.

[0018] The beneficial effects of this invention are as follows: The wheel speed credibility assessment method, system and device based on dynamic fusion of this invention significantly improves the accuracy and robustness of wheel speed credibility determination by introducing scene-adaptive dynamic weights and continuous fine assessment; and can intelligently handle evidence conflicts and automatically focus on the most relevant evidence under different working conditions, thereby effectively suppressing false alarms due to static disturbances, while ensuring the continuity of signals during actual start-up or low-speed driving, and enhancing the overall performance and safety of the chassis control system. Attached Figure Description

[0019] Figure 1 This is a flowchart of a wheel speed reliability assessment method based on dynamic fusion according to the present invention; Figure 2 This is a schematic diagram of a wheel speed reliability evaluation method based on dynamic fusion according to the present invention. Figure 3 This is a structural block diagram of a wheel speed reliability evaluation system based on dynamic fusion according to the present invention; Figure 4 This is a structural block diagram of a wheel speed reliability assessment device based on dynamic fusion according to the present invention. Detailed Implementation

[0020] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0021] like Figure 1 and Figure 2 As shown, a wheel speed credibility evaluation method based on dynamic fusion includes: S1, which obtains the vehicle's wheel speed, electronic parking brake status and transmission gear in real time; S2 determines the predefined scenario mode to which the vehicle belongs and assesses the reliability of the vehicle's wheel speed based on the vehicle's wheel speed, electronic parking brake status, and transmission gear. S3 selects the corresponding weight vector according to the predefined scene mode to which the vehicle belongs, and dynamically weights and fuses it with the vehicle's wheel speed credibility to obtain the wheel speed credibility fusion value, so as to evaluate the vehicle's wheel speed credibility.

[0022] The present invention provides a wheel speed credibility assessment method based on dynamic fusion. The method selects corresponding weights according to the real-time operation scenario of the vehicle and performs dynamic weight fusion, thereby achieving adaptive scenario, intelligent conflict resolution and more refined wheel speed credibility assessment.

[0023] In this preferred embodiment, the predefined scenario modes include a forced parking mode, a parking crawl mode, and a normal driving mode; wherein... The criteria for determining that a vehicle belongs to the aforementioned strong parking mode are: the electronic parking brake of the vehicle is in a clamped state or the vehicle's transmission gear is in parking gear. The criteria for determining that a vehicle belongs to the parking creep mode are: the vehicle is not in the strong parking mode and the vehicle's wheel speed is not higher than a preset wheel speed threshold. The criterion for determining that the vehicle belongs to the normal driving mode is: the vehicle's wheel speed is higher than the preset wheel speed threshold.

[0024] Specifically, this embodiment divides the predefined scene modes of the vehicle into three categories: when the vehicle's EPB (electronic parking brake) is detected to be in a clamped state or the gearbox is in P (parking gear), it is identified as a strong parking scene; when the vehicle is detected not in a parking scene and the vehicle speed is not higher than the preset wheel speed threshold, it is identified as a parking creep mode; when the vehicle speed is detected to be higher than the preset wheel speed threshold, it is identified as a normal driving mode.

[0025] In this embodiment, the wheel speed credibility includes parking status credibility, wheel speed consistency credibility, and wheel acceleration rationality credibility; the weight vector is the proportion vector of parking status credibility, wheel speed consistency credibility, and wheel acceleration rationality credibility; therefore, the weight vector consists of three weights: parking status credibility weight, wheel speed consistency credibility weight, and wheel acceleration rationality credibility weight.

[0026] In the strong parking mode, parking evidence is absolutely dominant, and interference from human disturbances such as shaking is strictly prevented. The weight vectors for parking status assessment, wheel speed consistency assessment, and wheel acceleration rationality assessment in the strong parking mode are the same as the strong parking mode weight vector. In this embodiment, a pre-defined weight vector for the strong parking mode can be used. .

[0027] In parking creep mode, the weight of parking is reduced, and more attention is paid to the rationality of wheel speed and acceleration. The weight vectors of parking status assessment, wheel speed consistency assessment, and wheel acceleration rationality assessment in this mode are the parking creep mode weight vectors. In this embodiment, a pre-defined parking creep mode weight vector can be used. .

[0028] In normal driving mode, ignoring parking status and relying entirely on wheel speed and dynamic rationality, the weight vectors for parking status assessment, wheel speed consistency assessment, and wheel acceleration rationality assessment in this mode are the same as the normal driving mode weight vectors. In this embodiment, a weight vector for the normal driving mode can be preset. .

[0029] It should be noted that the weight vector of the strong parking mode Parking Crawl Mode Weight Vector Normal driving mode weight vector It can be set reasonably according to actual needs, and no restrictions are imposed here.

[0030] In this preferred embodiment, assessing the reliability of the vehicle's parking status specifically includes: When the vehicle's electronic parking brake is in the clamped state or the vehicle's transmission is in the parking gear and the vehicle's wheel speed is not zero, a preset non-zero low confidence value is output as the confidence value of the vehicle's parking state; otherwise, a preset high confidence value is output as the confidence value of the vehicle's parking state.

[0031] Specifically, when it is confirmed to be a strong parking mode and the wheel speed is not zero, a preset low confidence value (e.g., 0.1) is output instead of simply being zeroed, as a strong negative evidence to participate in subsequent fusion; otherwise, a strong confidence value (e.g., 1) is output.

[0032] In this preferred embodiment, the wheel speed reliability includes wheel speed consistency reliability; specifically, evaluating the wheel speed consistency reliability of a vehicle includes: Using the second fastest wheel speed among the four wheel speeds of the vehicle as the reference wheel speed, calculate the deviations of the other three wheel speeds from the reference wheel speed; By using a predefined lookup table method, the deviations of the other three wheel speeds from the reference wheel speed are converted into continuous confidence values ​​between 0 and 1, thus obtaining the vehicle's wheel speed consistency confidence.

[0033] Specifically, the greater the wheel speed deviation, the lower the reliability of wheel speed consistency.

[0034] In this preferred embodiment, the wheel speed reliability includes the reliability of the wheel acceleration rationality; specifically, evaluating the reliability of the vehicle's wheel acceleration rationality includes: Calculate the wheel acceleration of the vehicle based on the wheel speed, and take the absolute value to obtain the absolute value of the wheel acceleration; A continuous mapping function is used to map the ratio of the absolute value of wheel acceleration to a preset acceleration rationality threshold to a continuous confidence value between 0 and 1, thereby obtaining the confidence level of the vehicle's wheel acceleration rationality.

[0035] Specifically, the mapping formula for the reasonableness and credibility of vehicle wheel acceleration is as follows: ; in, This indicates the reasonableness and credibility of the stated wheel acceleration. This represents the absolute value of the wheel's acceleration. This indicates the preset acceleration reasonableness threshold.

[0036] In this preferred embodiment, a corresponding weight vector is selected based on the predefined scene mode to which the vehicle belongs, and then dynamically weighted and fused with the vehicle's wheel speed confidence level. Specifically: When the vehicle belongs to the predefined scenario mode of the strong parking mode, the weight vector of the strong parking mode and the reliability of the vehicle's wheel speed are dynamically weighted and fused. When the predefined scenario mode to which the vehicle belongs is the parking creep mode, the parking creep mode weight vector and the vehicle's wheel speed credibility are dynamically weighted and fused. When the vehicle belongs to the predefined scenario mode, which is the normal driving mode, the weight vector of the normal driving mode and the vehicle's wheel speed credibility are dynamically weighted and fused.

[0037] The formula for dynamic weighted fusion is as follows: ; In the formula, This represents the wheel speed reliability fusion value. This indicates the reliability of the stated parking status. This indicates the reliability of the wheel speed consistency. This indicates the reasonableness and credibility of the stated wheel acceleration. This represents the parking status confidence weight in the weight vector. This represents the wheel speed consistency credibility weight in the weight vector. This represents the weight of the wheel acceleration's reasonableness and credibility in the weight vector.

[0038] Specifically, when the vehicle belongs to the predefined scenario mode of the strong parking mode, the weight vector is the strong parking mode weight vector. ,therefore, When the vehicle belongs to the predefined scenario mode of the parking creep mode, the weight vector is the parking creep mode weight vector. ,therefore, When the vehicle belongs to the predefined scenario mode of the normal driving mode, the weight vector is the normal driving mode weight vector. ,therefore, . for The first item in the list is the parking status credibility weight; for The second item in the list is the wheel speed consistency credibility weight; for The third item is the weight of the reasonableness and credibility of wheel acceleration.

[0039] In this embodiment, the wheel speed credibility fusion value is obtained by dynamic weighted fusion. Then, based on this value, the following measures can be taken: High reliability: Wheel speed can be directly output and used in all control systems; Medium reliability: Only suitable for instrument display, etc., and not applicable to system control such as vehicle stability. Low reliability: Sets wheel speed to zero and issues an alarm; only used for wheel speed fault diagnosis.

[0040] Based on the aforementioned wheel speed reliability assessment method based on dynamic fusion, this invention also provides a wheel speed reliability assessment system based on dynamic fusion.

[0041] In summary, the core innovation of this invention, a wheel speed reliability assessment method based on dynamic fusion, lies in: First, the most fundamental innovation of the method of this invention lies in the transformation of methodology: from the traditional static threshold or fixed weight judgment to dynamic weight fusion based on the real-time operation scenario of the vehicle.

[0042] Secondly, the process of achieving this dynamic fusion includes several key sub-innovations: First, a multi-level and refined single-item credibility evaluation index was designed; second, multiple scene pattern recognition was constructed; and third, an optimized dynamic weight vector was pre-set for each scene, and conflict detection and fine-tuning mechanisms were considered.

[0043] like Figure 3 As shown, a dynamic fusion-based wheel speed credibility assessment system, applied to the dynamic fusion-based wheel speed credibility assessment method described above, includes: The vehicle status acquisition module is used to acquire the vehicle's wheel speed, electronic parking brake status, and transmission gear position in real time. The scene recognition and wheel speed credibility assessment module is used to determine the predefined scene mode to which the vehicle belongs and to assess the credibility of the vehicle's wheel speed based on the vehicle's wheel speed, electronic parking brake status and transmission gear. The dynamic weighted fusion module is used to select the corresponding weight vector according to the predefined scene mode to which the vehicle belongs, and to dynamically weight and fuse it with the vehicle's wheel speed credibility to obtain a wheel speed credibility fusion value, so as to evaluate the vehicle's wheel speed credibility.

[0044] It should be noted that the specific functions of each module in the dynamic fusion-based wheel speed credibility assessment system of the present invention are described in the specific steps of the dynamic fusion-based wheel speed credibility assessment method of the present invention, and will not be repeated here.

[0045] Based on the aforementioned wheel speed reliability assessment method based on dynamic fusion, the present invention also provides a wheel speed reliability assessment device based on dynamic fusion.

[0046] like Figure 4 As shown, a wheel speed reliability assessment device based on dynamic fusion includes a processor, a memory, and a computer program stored in the memory. When the computer program is executed by the processor, it implements the wheel speed reliability assessment method based on dynamic fusion as described above.

[0047] In other words, the wheel speed reliability assessment device of this invention may include, but is not limited to: a processor and a memory; the memory is used to store computer programs; the processor is used to execute the wheel speed reliability assessment method based on dynamic fusion of this invention by calling the computer programs.

[0048] In one optional embodiment, a wheel speed reliability assessment device based on dynamic fusion is provided, such as Figure 4 As shown. Figure 4 The wheel speed reliability assessment device shown includes a processor and a memory. The processor and memory are connected, for example, via a bus. Optionally, the wheel speed reliability assessment device may further include a transceiver, which can be used for data interaction between the wheel speed reliability assessment device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver is not limited to one unit, and the structure of this wheel speed reliability assessment device does not constitute a limitation on the embodiments of the present invention.

[0049] The processor can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), a PLC (Programmable Logic Controller), a FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0050] A bus can include a pathway for transmitting information between the aforementioned components. The bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0051] The memory may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited to these.

[0052] The memory stores application code (computer program) that executes the present invention, and its execution is controlled by a processor. The processor executes the application code stored in the memory to implement the content shown in the foregoing method embodiments.

[0053] The wheel speed reliability assessment device can also be a terminal device, which can be any device that can install applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart in-vehicle devices.

[0054] It should be noted that, Figure 4 The wheel speed reliability assessment device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0055] This invention provides a method, system, and device for wheel speed credibility assessment based on dynamic fusion. By introducing scenario-adaptive dynamic weights and continuous fine-grained assessment, it significantly improves the accuracy and robustness of wheel speed credibility determination. Furthermore, it can intelligently handle evidence conflicts and automatically focus on the most relevant evidence under different operating conditions, thereby effectively suppressing false alarms due to static disturbances. At the same time, it ensures the continuity of signals during actual start-up or low-speed driving, enhancing the overall performance and safety of the chassis control system.

[0056] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating the reliability of wheel speed based on dynamic fusion, characterized in that, include: Real-time acquisition of vehicle wheel speed, electronic parking brake status, and transmission gear position; Based on the vehicle's wheel speed, electronic parking brake status, and transmission gear, determine the predefined scenario mode to which the vehicle belongs and assess the reliability of the vehicle's wheel speed. The corresponding weight vector is selected based on the predefined scene mode to which the vehicle belongs, and dynamically weighted and fused with the vehicle's wheel speed credibility to obtain a wheel speed credibility fusion value, in order to evaluate the vehicle's wheel speed credibility.

2. The wheel speed reliability assessment method based on dynamic fusion according to claim 1, characterized in that, The predefined scenario modes include forced parking mode, parking crawl mode, and normal driving mode; among them... The criteria for determining that a vehicle belongs to the aforementioned strong parking mode are: the electronic parking brake of the vehicle is in a clamped state or the vehicle's transmission gear is in parking gear. The criteria for determining that a vehicle belongs to the parking creep mode are: the vehicle is not in the strong parking mode and the vehicle's wheel speed is not higher than a preset wheel speed threshold. The criterion for determining that the vehicle belongs to the normal driving mode is: the vehicle's wheel speed is higher than the preset wheel speed threshold.

3. The wheel speed reliability assessment method based on dynamic fusion according to claim 1, characterized in that, The wheel speed reliability includes the parking status reliability; the evaluation of the vehicle's parking status reliability specifically includes: When the vehicle's electronic parking brake is in the clamped state or the vehicle's transmission is in the parking gear and the vehicle's wheel speed is not zero, a preset non-zero low confidence value is output as the confidence value of the vehicle's parking state; otherwise, a preset high confidence value is output as the confidence value of the vehicle's parking state.

4. The wheel speed reliability assessment method based on dynamic fusion according to claim 1, characterized in that, The wheel speed reliability includes wheel speed consistency reliability; the evaluation of the vehicle's wheel speed consistency reliability specifically includes: Using the second fastest wheel speed among the four wheel speeds of the vehicle as the reference wheel speed, calculate the deviations of the other three wheel speeds from the reference wheel speed; By using a predefined lookup table method, the deviations of the other three wheel speeds from the reference wheel speed are converted into continuous confidence values ​​between 0 and 1, thus obtaining the vehicle's wheel speed consistency confidence.

5. The wheel speed reliability assessment method based on dynamic fusion according to claim 1, characterized in that, The wheel speed reliability includes the reliability of the wheel acceleration reasonableness; the evaluation of the reliability of the vehicle's wheel acceleration reasonableness specifically includes: Calculate the wheel acceleration of the vehicle based on the wheel speed, and take the absolute value to obtain the absolute value of the wheel acceleration; A continuous mapping function is used to map the ratio of the absolute value of wheel acceleration to a preset acceleration rationality threshold to a continuous confidence value between 0 and 1, thereby obtaining the confidence level of the vehicle's wheel acceleration rationality.

6. The wheel speed reliability assessment method based on dynamic fusion according to claim 5, characterized in that, The mapping formula for the reasonableness and credibility of vehicle wheel acceleration is: ； In the formula, This indicates the reasonableness and credibility of the stated wheel acceleration. This represents the absolute value of the wheel's acceleration. This indicates the preset acceleration reasonableness threshold.

7. The wheel speed reliability assessment method based on dynamic fusion according to claim 1, characterized in that, The predefined scenario modes include a strong parking mode, a parking creep mode, and a normal driving mode; correspondingly, the weight vectors include a strong parking mode weight vector, a parking creep mode weight vector, and a normal driving mode weight vector. The corresponding weight vector is selected based on the predefined scene mode to which the vehicle belongs, and then dynamically weighted and fused with the vehicle's wheel speed confidence level, specifically as follows: When the vehicle belongs to the predefined scenario mode of the strong parking mode, the weight vector of the strong parking mode and the reliability of the vehicle's wheel speed are dynamically weighted and fused. When the predefined scenario mode to which the vehicle belongs is the parking creep mode, the parking creep mode weight vector and the vehicle's wheel speed credibility are dynamically weighted and fused. When the vehicle belongs to the predefined scenario mode, which is the normal driving mode, the weight vector of the normal driving mode and the vehicle's wheel speed credibility are dynamically weighted and fused.

8. The wheel speed reliability assessment method based on dynamic fusion according to claim 7, characterized in that, The wheel speed credibility includes parking status credibility, wheel speed consistency credibility, and wheel acceleration rationality credibility; correspondingly, the weight vector includes parking status credibility weight, wheel speed consistency credibility weight, and wheel acceleration rationality credibility weight. The formula for dynamic weighted fusion is: ； In the formula, This represents the wheel speed reliability fusion value. This indicates the reliability of the stated parking status. This indicates the reliability of the wheel speed consistency. This indicates the reasonableness and credibility of the stated wheel acceleration. This represents the parking status confidence weight in the weight vector. This represents the wheel speed consistency credibility weight in the weight vector. This represents the weight of the wheel acceleration's reasonableness and credibility in the weight vector.

9. A wheel speed reliability assessment system based on dynamic fusion, characterized in that, The method for evaluating wheel speed based on dynamic fusion as described in any one of claims 1 to 8 includes: The vehicle status acquisition module is used to acquire the vehicle's wheel speed, electronic parking brake status, and transmission gear position in real time. The scene recognition and wheel speed credibility assessment module is used to determine the predefined scene mode to which the vehicle belongs and to assess the credibility of the vehicle's wheel speed based on the vehicle's wheel speed, electronic parking brake status and transmission gear. The dynamic weighted fusion module is used to select the corresponding weight vector according to the predefined scene mode to which the vehicle belongs, and to dynamically weight and fuse it with the vehicle's wheel speed credibility to obtain a wheel speed credibility fusion value, so as to evaluate the vehicle's wheel speed credibility.

10. A wheel speed reliability assessment device based on dynamic fusion, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory, wherein the computer program, when executed by the processor, implements the wheel speed reliability assessment method based on dynamic fusion as described in any one of claims 1 to 8.

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