Steering ratio determination method and system, electronic equipment and vehicle

Abstract

The invention provides a steering ratio determining method and system, electronic equipment and a vehicle, and belongs to the technical field of vehicle control. The steering ratio determining method comprises the steps that the vehicle speed, the steering wheel angle, the steering wheel angular speed and the steering wheel angular acceleration at the current moment are obtained; fuzzy reasoning is carried out based on the vehicle speed and the steering wheel angle, and after a reasoning result is defuzzified, a first reasoning result reflecting the steady-state driving requirement is obtained; fuzzy reasoning is carried out based on the first reasoning result and the angular velocity of the steering wheel, and after the reasoning result is defuzzified, a second reasoning result reflecting the dynamic steering trend is obtained; fuzzy reasoning is conducted based on the second reasoning result and the steering wheel angular acceleration, and after the reasoning result is defuzzified, the steering transmission ratio reflecting the transient control intention is obtained and serves as the steering transmission ratio of the next moment. According to the embodiment of the invention, the coupling decision of any number of variables can be supported, and the high-dimensional decision problem is solved with linear calculation complexity.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and in particular to a steering ratio determination method, system, electronic device, and vehicle. Background Technology

[0002] In traditional electric power steering systems, the steering ratio is typically a fixed value, meaning the ratio of the steering wheel angle to the front wheel steering angle remains constant. With advancements in vehicle control technology, some vehicles employ variable steering ratio control strategies, dynamically adjusting the steering ratio through an electronic control system. A common implementation method in existing systems is to consult a preset mapping table based on vehicle speed and steering wheel angle to determine the appropriate steering ratio. Summary of the Invention

[0003] This invention aims to solve at least one of the technical problems existing in the prior art, and proposes a steering ratio determination method, system, electronic device and vehicle that can support coupled decision-making of any number of variables and solve high-dimensional decision problems with linear computational complexity.

[0004] In a first aspect, embodiments of the present invention provide a steering ratio determination method, the method comprising: acquiring the vehicle speed, steering wheel angle, steering wheel angular velocity, and steering wheel angular acceleration at the current moment; performing fuzzy inference based on the vehicle speed and steering wheel angle, and defuzzifying the inference result to obtain a first inference result reflecting steady-state driving requirements; performing fuzzy inference based on the first inference result and steering wheel angular velocity, and defuzzifying the inference result to obtain a second inference result reflecting dynamic steering trends; and performing fuzzy inference based on the second inference result and steering wheel angular acceleration, and defuzzifying the inference result to obtain a steering transmission ratio reflecting transient driving intentions, which is used as the steering transmission ratio at the next moment.

[0005] Secondly, embodiments of the present invention provide a steering ratio determination system, including a first fuzzy inferencer, a second fuzzy inferencer, and a third fuzzy inferencer.

[0006] The first fuzzy inference engine is used to perform fuzzy inference based on vehicle speed and steering wheel angle, and after defuzzifying the inference results, a first inference result reflecting the steady-state driving requirements is obtained.

[0007] The second fuzzy inference engine is used to perform fuzzy inference based on the first inference result and the steering wheel angular velocity, and after defuzzifying the inference result, a second inference result reflecting the dynamic steering trend is obtained.

[0008] The third fuzzy inference engine is used to perform fuzzy inference based on the second inference result and the steering wheel angular acceleration. After defuzzifying the inference result, the steering ratio reflecting the transient driving intention is obtained and used as the steering ratio at the next moment.

[0009] Thirdly, an electronic device is provided, comprising: a memory for storing instructions; and a processor for calling the instructions stored in the memory to implement the steering ratio determination method of the first aspect.

[0010] Fourthly, embodiments of the present invention provide a vehicle including electronic equipment as described in the third aspect.

[0011] Fifthly, a computer-readable storage medium is provided having computer instructions stored thereon, which, when executed by a processor, implement the steering ratio determination method of the first aspect.

[0012] In a sixth aspect, a computer program product is provided, the computer program product storing instructions that, when executed by a computer, cause the computer to implement the steering ratio determination method of the first aspect.

[0013] In a seventh aspect, a chip is provided, including at least one processor and an interface; the interface is used to provide program instructions or data to the at least one processor; the at least one processor is used to execute the program instructions to implement the steering ratio determination method of the first aspect.

[0014] The steering ratio determination method, system, electronic device, and vehicle provided by this invention achieve refined dynamic adjustment of the steering ratio by hierarchically fusing multi-dimensional driving state information such as vehicle speed, steering wheel angle, angular velocity, and angular acceleration. First, a basic steering ratio reflecting steady-state driving needs is obtained based on vehicle speed and steering wheel angle. Then, the dynamic steering trend is corrected by combining steering wheel angular velocity. Finally, steering wheel angular acceleration is introduced to capture the driver's transient driving intentions, refining the decision accuracy step by step. Compared to traditional lookup table methods or single-condition adjustment strategies, this scheme can complete high-dimensional variable coupling decision-making using only the vehicle's built-in sensors without relying on external environmental perception. This significantly improves the responsiveness, stability, and driving adaptability of the steering system under all operating conditions, while also possessing advantages such as clear structure, high computational efficiency, and easy scalability. Attached Figure Description

[0015] Figure 1 A flowchart illustrating a steering ratio determination method provided in an embodiment of the present invention;

[0016] Figure 2 This is a flowchart illustrating an optional specific implementation method of step S102 in an embodiment of the present invention;

[0017] Figure 3 This is a flowchart illustrating an optional specific implementation method of step S103 in an embodiment of the present invention;

[0018] Figure 4 This is a flowchart illustrating an optional specific implementation method of step S104 in an embodiment of the present invention;

[0019] Figure 5 A flowchart illustrating another method for determining the steering ratio provided in an embodiment of the present invention;

[0020] Figure 6 This is a schematic diagram of a single-layer fuzzy inference process in an embodiment of the present invention;

[0021] Figure 7 A structural block diagram of a steering ratio determination system provided in an embodiment of the present invention;

[0022] Figure 8 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0024] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0025] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0026] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0027] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0028] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0029] Steering ratio, the ratio of the steering wheel rotation angle to the front wheel steering angle, is typically set to a constant value in traditional electric power steering (EPS) systems. To achieve variable steering ratio, the traditional approach involves a combination of electronic control and mechanical devices, such as altering the pitch of the steering rack. However, with advancements in steering technology and the rise of steer-by-wire, the steering wheel and front wheels have been completely decoupled mechanically. While ensuring safety, the vehicle's steering ratio can be dynamically adjusted in real time, significantly improving driving agility and comfort. Existing methods for variable steering ratio primarily rely on looking up values ​​in a table based on vehicle speed. When the car is in different speed ranges, the ideal steering ratio for that speed is determined by consulting the table, thus reducing driver difficulty at low speeds and improving vehicle stability at high speeds.

[0030] Currently, there are two main methods for switching steering ratios. The first method involves looking up the optimal steering ratio using current vehicle speed, steering wheel angle, and yaw rate. This method struggles to handle coupled decisions involving more than two variables. When switching steering ratios, too many factors need to be considered, leading to rule explosion. Furthermore, the impact of each factor on the steering ratio is difficult to quantify, making it challenging to calculate the steering ratio based on the specific values ​​of each factor. The second method involves indirectly changing the front wheel angle by altering the steering ratio in emergency situations to achieve hazard avoidance. Determining the steering ratio relies on environmental information acquired by sensors, resulting in significant response delays, high costs, and applicability only in emergency situations.

[0031] To address the aforementioned issues, the proposed solution can calculate the variable steering ratio based on various factors that may affect steering, supports coupled decision-making of any number of variables, solves high-dimensional decision-making problems with linear computational complexity, and requires only vehicle-mounted sensors, resulting in lower cost and faster response speed.

[0032] In some embodiments, this application primarily uses a multi-level serial fuzzy decision system to calculate the optimal steering ratio based on the current vehicle state and the driver's steering wheel input. The method in this application supports coupled decision-making with any number of variables, solves high-dimensional decision problems with linear computational complexity, requires only vehicle-mounted sensors, resulting in low cost and fast response. A fuzzy decision tree of any number of levels can be built to consider various factors that may affect vehicle steering, and the optimal steering ratio is calculated based on each factor. If additional decision factors need to be considered later, multiple layers of fuzzy decision trees can be added to meet the requirements.

[0033] The deficiencies of the above solutions and the proposed solutions are the result of the inventor's practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed in this disclosure below should be considered as the inventor's contribution to this disclosure.

[0034] It is understood that the data involved in this disclosure (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and provisions. Before using the technical solutions disclosed in the embodiments of this disclosure, users shall be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and their authorization shall be obtained.

[0035] Figure 1 A flowchart of a steering ratio determination method according to an embodiment of this disclosure is shown, as follows: Figure 1 As shown, the steering ratio determination method provided in this embodiment includes S101-S104.

[0036] In S101, obtain the current vehicle speed, steering wheel angle, steering wheel angular velocity, and steering wheel angular acceleration.

[0037] The system collects real-time vehicle status and driver operation signals, including vehicle speed, steering wheel angle, steering wheel angular velocity, and steering wheel angular acceleration, providing an input basis for subsequent multi-level decision-making.

[0038] In S102, fuzzy reasoning is performed based on vehicle speed and steering wheel angle, and the reasoning results are defuzzified to obtain the first reasoning result that reflects the steady-state driving requirements.

[0039] The first level of fuzzy inference is performed based on vehicle speed and steering wheel angle. After defuzzification, the first inference result is output. This result represents the basic steering requirements of the vehicle under steady-state driving conditions, taking into account both low-speed agility and high-speed stability.

[0040] In S103, fuzzy reasoning is performed based on the first reasoning result and the steering wheel angular velocity, and after the reasoning result is defuzzified, a second reasoning result reflecting the dynamic steering trend is obtained.

[0041] The first inference result and the steering wheel angular velocity are input into the second-level fuzzy inference engine, which outputs a dynamic trend correction amount that reflects the speed of the driver's steering operation, thereby achieving adaptive adjustment of steering response agility.

[0042] In S104, fuzzy reasoning is performed based on the second reasoning result and the steering wheel angular acceleration. After defuzzifying the reasoning result, the steering transmission ratio reflecting the transient driving intention is obtained, which is used as the steering transmission ratio at the next moment.

[0043] The third level of fuzzy reasoning is performed by combining the second reasoning result with the steering wheel angular acceleration, and finally the steering transmission ratio at the next moment is output. This accurately captures transient control intentions such as sudden steering and improves vehicle following performance in emergency or aggressive driving scenarios.

[0044] This invention employs a three-level serial fuzzy inference architecture to decouple steering ratio decision-making into three levels: steady-state, dynamic, and transient. This enables the temporal fusion and progressive processing of multi-source driving information. The method relies solely on conventional onboard sensors, requiring no external environmental perception, and can dynamically optimize the steering ratio under all operating conditions. While maintaining computational efficiency, it significantly improves the responsiveness, stability, and driving adaptability of the steering system, and possesses excellent scalability.

[0045] In some embodiments, before S102, the steering wheel angle is normalized to limit the steering wheel angle to the range of [-1,1]; fuzzy inference is performed based on vehicle speed and steering wheel angle, and the inference result is defuzzified to obtain a first inference result reflecting steady-state driving requirements, including: fuzzy inference is performed based on vehicle speed and normalized steering wheel angle, and the inference result is defuzzified to obtain a first inference result reflecting steady-state driving requirements.

[0046] This invention implements a normalization process for the steering wheel angle, which can eliminate the dimensional influence caused by the difference between the steering ratio and the front wheel angle, standardize the fuzzy inference input, and improve the generalization ability and control consistency under different vehicle models or operating conditions.

[0047] In some embodiments, fuzzy inference based on vehicle speed and steering wheel angle may include... Figure 2 S201-S204 are shown.

[0048] In S201, vehicle speed is fuzzified using a trapezoidal membership function, and the fuzzy set of vehicle speed is divided into five levels: very low, low, medium, high, and very high.

[0049] Vehicle speed is fuzzified using a trapezoidal membership function, and divided into five levels: very low, low, medium, high, and very high, to smoothly represent different speed ranges.

[0050] In S202, the steering wheel angle is fuzzified using a triangular membership function, and the fuzzy set of the steering wheel angle is divided into seven levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large.

[0051] The steering wheel angle is fuzzified using a triangular membership function, divided into seven levels from negative large to positive large, to accurately depict the direction and magnitude of the steering operation.

[0052] In S203, fuzzy inference is performed based on a preset fuzzy rule base. The fuzzy rule base is used to determine the basic steering ratio level that is suitable for the current driving comfort and handling stability based on the combination of vehicle speed and steering wheel angle under steady-state driving conditions.

[0053] Based on a preset fuzzy rule base, the system maps a basic steering ratio level that is suitable for the current steady-state operating conditions according to the combination of vehicle speed and steering wheel angle, taking into account both driving comfort and handling stability.

[0054] In S204, the fuzzy inference result is defuzzified using the area center method to obtain the first inference result with the output value located in the interval [0.5, 2.0].

[0055] The area center method is used to defuzzify the fuzzy output and obtain the first inference result with a value in the range of [0.5, 2.0], which serves as the basis for subsequent dynamic correction.

[0056] This invention, through the rational design of membership function types and fuzzy set partitioning, combined with a rule base for steady-state driving and a standardized output range, achieves stable, smooth calculation of the basic steering transmission ratio that conforms to human-vehicle interaction characteristics, providing a reliable initial value for multi-level decision-making systems.

[0057] In some embodiments, the fuzzy rule base includes the following rule: when the vehicle speed is very low and the steering wheel angle is at its maximum, the first inference result is very low; when the vehicle speed is very high and the steering wheel angle is zero, the first inference result is very high. This rule outputs a small steering ratio at low speeds and large steering angles to improve agility, and outputs a large steering ratio at high speeds and straight driving to enhance stability, which aligns with driving intuition and effectively balances ease of handling with high-speed safety.

[0058] In some embodiments, fuzzy reasoning based on the first reasoning result and the steering wheel angular velocity may include... Figure 3 S301-S304 are shown.

[0059] In S301, the first inference result is fuzzified using a trapezoidal membership function, and the fuzzy set of the first inference result is divided into five levels: very low, low, medium, high, and very high.

[0060] In S302, the steering wheel angular velocity is fuzzified using a triangular membership function. The fuzzy set of the steering wheel angular velocity is divided into seven levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large.

[0061] In S303, fuzzy inference is performed based on a preset fuzzy rule base. The fuzzy rule base is used to make real-time corrections to the basic steering gear ratio based on the dynamic changes in the driver's steering operation, combined with the first inference result and the absolute value of the steering wheel angular velocity.

[0062] In S304, the fuzzy inference result is defuzzified using the area center method to obtain a second inference result with an output value in the interval [0.5, 2.0].

[0063] This invention focuses on the dynamic characteristics of steering operations, introducing steering wheel angular velocity to correct the steady-state basic steering ratio in real time. Compared to S201–S204, which only reflect static conditions, this level of inference can sense the speed at which the driver turns the steering wheel: the greater the angular velocity, the smaller the steering ratio, thereby improving vehicle responsiveness. This mechanism effectively enhances the system's adaptability to dynamic driving intentions, compensates for the lag in pure steady-state decision-making in rapid steering scenarios, and achieves more natural and sensitive human-vehicle collaborative control.

[0064] In some embodiments, fuzzy inference based on the second inference result and the steering wheel angular acceleration may include... Figure 4 S401-S404 are shown.

[0065] In S401, the second inference result is fuzzified using a trapezoidal membership function, and the fuzzy set of the second inference result is divided into five levels: very low, low, medium, high, and very high.

[0066] In S402, the steering wheel angular acceleration is fuzzified using triangular or trapezoidal membership functions. The fuzzy set of the steering wheel angular acceleration is divided into seven levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large.

[0067] In S403, fuzzy inference is performed based on a preset fuzzy rule base. The fuzzy rule base defines the steering ratio level corresponding to the combination of the level of the second inference result and the level of the steering wheel angular acceleration.

[0068] In S404, the fuzzy inference result is defuzzified using the area center method to obtain the steering ratio with the output value in the interval [0.5, 2.0], which is used as the steering ratio for the next moment.

[0069] This invention introduces steering wheel angular acceleration to capture the instantaneous rate of change of steering operations (such as sharp turns), and further responds to the driver's emergency or aggressive maneuvering intentions based on dynamic correction. Compared to the first two stages that process steady-state position and velocity information respectively, this stage focuses on the "operational abruptness" represented by acceleration, which can significantly reduce the steering ratio within milliseconds, improve the vehicle's transient response capability, and effectively enhance handling and safety under emergency obstacle avoidance or extreme conditions.

[0070] In some embodiments, after obtaining the steering ratio that reflects the transient handling intention as the steering ratio for the next moment, the corresponding yaw rate gain is calculated based on the current vehicle speed, and the steering ratio is adjusted by limiting when the yaw rate gain exceeds a preset stable range.

[0071] This invention incorporates vehicle dynamics stability verification after outputting the steering ratio. By calculating the yaw rate gain at the current vehicle speed, it determines whether the steering ratio might lead to oversteer or instability. If it exceeds a preset stability range, the steering ratio is adjusted to a limit, ensuring that the vehicle remains within a controllable and stable dynamic boundary while preserving the responsiveness to driving intentions, effectively balancing handling agility and driving safety.

[0072] In this embodiment of the invention, the factors considered in calculating the steering ratio include: vehicle speed, steering wheel angle, and steering wheel angular velocity. The steering ratio is the ratio of the steering wheel angle to the pinion angle.

[0073] Figure 5 A flowchart of a steering ratio determination method according to an embodiment of this disclosure is shown, as follows: Figure 5 As shown, a total of three layers of fuzzy inference are performed. The first layer is called the static steering layer, which uses vehicle speed and steering wheel angle for fuzzy inference and outputs the inference result after defuzzification to the second layer. The second layer is the dynamic trend correction layer, which takes the inference result of the first layer and the steering wheel angular velocity as input to the fuzzy inference. Similarly, the inference result is defuzzified and output to the third layer. The third layer is the transient response layer, which takes the output result of the second layer and the steering wheel angular velocity as input. The output result of the third layer is defuzzified to obtain the final inference result, which is the optimal steering gear ratio at the next moment.

[0074] In practice, if other factors need to be added, more fuzzy inference engines can be added by taking the output of the third layer and the fuzzy variables that need to be considered as inputs.

[0075] For each fuzzy inference, the process is as follows: Figure 6 As shown, different membership functions, inference methods, and fuzzy rule bases are selected according to the characteristics of the input variables. Each layer's output needs to be defuzzified before it can be passed to the next layer.

[0076] For the first layer, the inputs are the steering wheel angle and vehicle speed. The steering wheel angle needs to be normalized before input.

[0077] First, obtain the original steering wheel angle in real time. Read the current steering ratio R and the maximum front wheel angle. Calculate the normalized rotation angle:

[0078]

[0079] In addition, it is necessary to Restricted to the interval [-1, 1] Then, the calculated The steering wheel angle, along with vehicle speed, is input into the fuzzy decision-maker. The table below shows the rule table for this fuzzy controller, where the vehicle speed fuzzy set is VB (very high), B (high), M (medium), S (low), VS (very low). A trapezoidal membership function is used to achieve a smooth transition. The parameter table for the vehicle speed fuzzy set membership function is as follows:

[0080] surface Vehicle speed fuzzy set membership function parameter table

[0081] The fuzzy set for steering wheel angle is defined as NB (negative large), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (positive medium), and PB (positive large). A triangular membership function is used, and the parameter table for the steering wheel fuzzy set membership function is as follows:

[0082] surface Steering wheel angle fuzzy set membership function parameter table

[0083] The first layer output results are designed with fuzzy set membership functions. The universe of discourse ranges from [0.5, 2], and the functions are divided into five fuzzy sets: VB (very low), B (low), M (medium), S (high), and VS (very high). A trapezoidal membership function is used, and the membership function parameter table is as follows:

[0084] surface First layer output result fuzzy set membership function parameter table

[0085] The fuzzy reasoning rule table is as follows:

[0086] surface First-level fuzzy inference rule table

[0087] The reasoning method uses the Mamdani method, i.e., "If A and B then C". Defuzzification uses the centroid method from the median approach.

[0088] For the second layer, the input is the steering wheel angular velocity and the output of the first layer. The following are the parameters of the fuzzy set membership function for the steering wheel angular velocity:

[0089] surface Steering wheel angular velocity fuzzy set membership function parameter table

[0090] The fuzzy inference rule table for the second layer is shown below:

[0091] surface Second-level fuzzy inference rule table

[0092] The membership function parameters output by the second layer are as follows: As shown.

[0093] The output of the second-layer fuzzy inference engine, after defuzzification, is input to the third-layer fuzzy inference engine along with the steering wheel angular velocity. The following are the parameters of the fuzzy set membership function for the steering wheel angular acceleration:

[0094] surface Steering wheel angular acceleration fuzzy set membership function parameter table

[0095] The following is the fuzzy reasoning rule table for the third layer:

[0096] surface Third-level fuzzy inference rule table

[0097] The parameters of the fuzzy membership function of the obtained third-layer output are as follows: As shown, the final steering ratio result is obtained after deblurring the output of the third layer.

[0098] The final steering ratio result should be adjusted to ensure that the vehicle yaw rate gain is within a reasonable range.

[0099] For factors that need to be considered, a fuzzy inference engine can be added after the third layer to take more factors into account.

[0100] The variable steering ratio calculation strategy based on a time-series decoupled decision chain proposed in this invention supports coupled decision-making of any number of variables, solves high-dimensional decision-making problems with linear computational complexity, and has arbitrary level expansion capability. By adding decision layers (such as a fourth layer that integrates yaw rate), more strategies for calculating steering ratio can be added without reconstructing the rule base.

[0101] Based on the same inventive concept, embodiments of the present invention also provide a steering ratio determination system, such as... Figure 7 As shown, the steering ratio determination system includes a first fuzzy inferencer 701, a second fuzzy inferencer 702, and a third fuzzy inferencer 703.

[0102] The first fuzzy inferencer 701 is used to perform fuzzy inference based on vehicle speed and steering wheel angle, and after defuzzifying the inference result, obtain the first inference result that reflects the steady-state driving requirements.

[0103] The second fuzzy inferencer 702 is used to perform fuzzy inference based on the first inference result and the steering wheel angular velocity, and after defuzzifying the inference result, obtain the second inference result that reflects the dynamic steering trend.

[0104] The third fuzzy inferencer 703 is used to perform fuzzy inference based on the second inference result and the steering wheel angular acceleration, and after defuzzifying the inference result, obtains the steering transmission ratio that reflects the transient driving intention, which is used as the steering transmission ratio at the next moment.

[0105] In some embodiments, the steering ratio determination system further includes a data processing module. The data processing module is used to normalize the steering wheel angle, limiting the steering wheel angle to the range of [-1, 1].

[0106] The aforementioned first fuzzy inferencer 701 is used to perform fuzzy inference based on vehicle speed and normalized steering wheel angle, and after defuzzifying the inference result, obtain the first inference result that reflects the steady-state driving requirements.

[0107] In some embodiments, the first fuzzy inferencer 701 performs fuzzy inference based on vehicle speed and steering wheel angle, including: fuzzifying the vehicle speed using a trapezoidal membership function, dividing the fuzzy set of vehicle speed into five levels: very low, low, medium, high, and very high; fuzzifying the steering wheel angle using a triangular membership function, dividing the fuzzy set of steering wheel angle into seven levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large; performing fuzzy inference according to a preset fuzzy rule library, which is used to determine the basic steering ratio level suitable for the current driving comfort and handling stability based on the combination of vehicle speed and steering wheel angle under steady-state driving conditions; and defuzzifying the fuzzy inference result using the area center method to obtain a first inference result with an output value in the interval [0.5, 2.0].

[0108] In some embodiments, the fuzzy rule base includes the following rules: when the vehicle speed is very low and the steering wheel angle is positive, the first inference result is very low; when the vehicle speed is very high and the steering wheel angle is zero, the first inference result is very high.

[0109] In some embodiments, the second fuzzy inferencer 702 performs fuzzy inference based on the first inference result and the steering wheel angular velocity, including: fuzzifying the first inference result using a trapezoidal membership function, dividing the fuzzy set of the first inference result into five levels: very low, low, medium, high, and very high; fuzzifying the steering wheel angular velocity using a triangular membership function, dividing the fuzzy set of the steering wheel angular velocity into seven levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large; performing fuzzy inference according to a preset fuzzy rule library, which is used to correct the basic steering transmission ratio in real time based on the dynamic change trend of the driver's steering operation and the absolute value of the first inference result and the steering wheel angular velocity; and defuzzifying the fuzzy inference result using the area center method to obtain a second inference result with an output value in the interval [0.5, 2.0].

[0110] In some embodiments, the third fuzzy inferencer 703 performs fuzzy inference based on the second inference result and the steering wheel angular acceleration, including: fuzzifying the second inference result using a trapezoidal membership function, dividing the fuzzy set of the second inference result into five levels: very low, low, medium, high, and very high; fuzzifying the steering wheel angular acceleration using a triangular or trapezoidal membership function, dividing the fuzzy set of the steering wheel angular acceleration into seven levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large; performing fuzzy inference according to a preset fuzzy rule base, the fuzzy rule base defining the steering ratio level corresponding to the combination of the level of the second inference result and the level of the steering wheel angular acceleration; and defuzzifying the fuzzy inference result using the area center method to obtain a steering ratio with an output value in the interval [0.5, 2.0], which is used as the steering ratio at the next moment.

[0111] In some embodiments, the steering ratio determination system further includes a steering ratio adjustment module. The steering ratio adjustment module is used to calculate the corresponding yaw rate gain based on the current vehicle speed, and to limit the steering ratio when the yaw rate gain exceeds a preset stable range.

[0112] Regarding the steering ratio determination system in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the steering ratio determination method, and will not be elaborated upon here.

[0113] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 8 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 8 As shown, an embodiment of the present invention provides an electronic device including: one or more processors 801, a memory 802, and one or more I / O interfaces 803. The memory 802 stores one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement any of the steering ratio determination methods described in the above embodiments; the one or more I / O interfaces 803 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0114] Among them, processor 801 is a device with data processing capabilities, including but not limited to central processing unit (CPU); memory 802 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory (FLASH); I / O interface (read-write interface) 803 is connected between processor 801 and memory 802, and can realize information interaction between processor 801 and memory 802, including but not limited to data bus (Bus).

[0115] In some embodiments, the processor 801, memory 802, and I / O interface 803 are interconnected via bus 804, and thus connected to other components of the computing device.

[0116] In some embodiments, the one or more processors 801 include a field-programmable gate array.

[0117] Based on the same inventive concept, embodiments of the present invention also provide a vehicle, which includes electronic equipment or the steering ratio determination system described above. The electronic equipment may be the electronic equipment described in the preceding embodiments, and will not be repeated here.

[0118] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps in any of the steering ratio determination methods described in the above embodiments. The computer-readable storage medium may be volatile or non-volatile.

[0119] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described steering ratio determination method.

[0120] This invention also provides a chip, including at least one processor and an interface; the interface is used to provide program instructions or data to at least one processor; the at least one processor is used to execute the program instructions to implement the steering ratio determination method described in the above method embodiments.

[0121] In some embodiments, the chip may further include a memory for storing program instructions and data, the memory being located within or outside the processor.

[0122] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0123] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0124] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0125] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0126] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0127] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should 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-readable program instructions.

[0128] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0129] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0130] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0131] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A method for determining steering ratio, characterized in that, The method includes: Get the vehicle speed, steering wheel angle, steering wheel angular velocity, and steering wheel angular acceleration at the current moment; Based on the vehicle speed and the steering wheel angle, fuzzy reasoning is performed, and the reasoning results are defuzzified to obtain the first reasoning result that reflects the steady-state driving requirements. Based on the first reasoning result and the steering wheel angular velocity, fuzzy reasoning is performed, and after defuzzifying the reasoning result, a second reasoning result reflecting the dynamic steering trend is obtained; Based on the second reasoning result and the steering wheel angular acceleration, fuzzy reasoning is performed, and the reasoning result is defuzzified to obtain the steering transmission ratio that reflects the transient driving intention, which is then used as the steering transmission ratio for the next moment.

2. The method according to claim 1, wherein, The method further includes: The steering wheel angle is normalized and limited to the range of [-1, 1]. The step of performing fuzzy inference based on the vehicle speed and the steering wheel angle, and then defuzzifying the inference result to obtain a first inference result reflecting the steady-state driving requirements, includes: performing fuzzy inference based on the vehicle speed and the normalized steering wheel angle, and then defuzzifying the inference result to obtain a first inference result reflecting the steady-state driving requirements.

3. The method according to claim 1, wherein, The fuzzy reasoning based on the vehicle speed and the steering wheel angle includes: The vehicle speed is fuzzified using a trapezoidal membership function, and the fuzzy set of the vehicle speed is divided into five levels: very low, low, medium, high, and very high. The steering wheel angle is fuzzified using a triangular membership function, and the fuzzy set of the steering wheel angle is divided into seven levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. Fuzzy inference is performed based on a preset fuzzy rule base. The fuzzy rule base is used to determine a basic steering ratio level that is suitable for the current driving comfort and handling stability based on the combination of the vehicle speed and the steering wheel angle under steady-state driving conditions. The fuzzy inference result is defuzzified using the area center method to obtain the first inference result with an output value in the interval [0.5, 2.0].

4. The method according to claim 3, wherein, The fuzzy rule base includes the following rules: when the vehicle speed is very low and the steering wheel angle is positive, the first inference result is very low; when the vehicle speed is very high and the steering wheel angle is zero, the first inference result is very high.

5. The method according to claim 3, wherein, The fuzzy reasoning based on the first reasoning result and the steering wheel angular velocity includes: The first inference result is fuzzified using a trapezoidal membership function, and the fuzzy set of the first inference result is divided into five levels: very low, low, medium, high, and very high. The steering wheel angular velocity is fuzzified using a triangular membership function, and the fuzzy set of the steering wheel angular velocity is divided into seven levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. Fuzzy inference is performed based on a preset fuzzy rule base. The fuzzy rule base is used to make real-time corrections to the basic steering transmission ratio based on the dynamic changes in the driver's steering operation, combined with the first inference result and the absolute value of the steering wheel angular velocity. The fuzzy inference result is defuzzified using the area center method to obtain the second inference result with an output value in the interval [0.5, 2.0].

6. The method according to claim 5, wherein, Fuzzy reasoning based on the second reasoning result and the steering wheel angular acceleration includes: The second inference result is fuzzified using a trapezoidal membership function, and the fuzzy set of the second inference result is divided into five levels: very low, low, medium, high, and very high. The steering wheel angular acceleration is fuzzified using a triangular or trapezoidal membership function, and the fuzzy set of the steering wheel angular acceleration is divided into seven levels: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. Fuzzy inference is performed based on a preset fuzzy rule base, wherein the fuzzy rule base defines the steering ratio level corresponding to the combination of the level of the second inference result and the level of the steering wheel angular acceleration. The fuzzy inference results are defuzzified using the area center method to obtain the steering ratio with the output value in the interval [0.5, 2.0], which is used as the steering ratio for the next moment.

7. The method according to claim 1, wherein, After obtaining the steering ratio reflecting the transient handling intention as the steering ratio for the next moment, the method further includes: The yaw rate gain is calculated based on the current vehicle speed, and the steering ratio is adjusted to a limit when the yaw rate gain exceeds a preset stable range.

8. A steering ratio determination system, characterized in that, include: The first fuzzy inference engine is used to perform fuzzy inference based on the vehicle speed and the steering wheel angle, and after defuzzifying the inference result, obtain the first inference result that reflects the steady-state driving requirements. The second fuzzy inference engine is used to perform fuzzy inference based on the first inference result and the steering wheel angular velocity, and after defuzzifying the inference result, obtain a second inference result that reflects the dynamic steering trend. The third fuzzy inference engine is used to perform fuzzy inference based on the second inference result and the steering wheel angular acceleration, and after defuzzifying the inference result, obtains the steering transmission ratio that reflects the transient driving intention, which is used as the steering transmission ratio at the next moment.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 7.

10. A vehicle, characterized in that, Includes the electronic device as described in claim 9.

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