Adjusting method and device for control hand feeling softness of steering wheel and storage medium

Abstract

The invention relates to the technical field of steering wheel adjustment, in particular to an adjustment method and device for the control hand feeling softness of a steering wheel and a storage medium. The method comprises the steps that data related to vehicle steering wheel control are collected; understanding and reasoning the data to obtain a first softness range; performing multi-objective optimization on the steering wheel adjusting parameters, wherein a second softness range corresponding to the optimized steering wheel adjusting parameters is within the first softness range; target softness is selected from the second softness range, and steering wheel adjusting parameters corresponding to the target softness are sent to a steering wheel electronic power assisting system. According to the invention, personalized, automatic and fine-grained steering wheel control hand feeling softness adjustment is realized.

Description

Technical Field

[0001] The present application relates to the technical field of steering wheel adjustment, and in particular to a method, device and storage medium for adjusting the softness of the steering wheel's control feel. Background Art

[0002] The softness of the steering wheel's control feel refers to the feedback strength and control resistance felt by the driver when operating the steering wheel. For example, different lightness and sensitivity of the steering wheel control will affect the degree of softness.

[0003] In existing technologies, vehicles offer a limited number of driving mode options, such as Economy, Comfort, and Sport. Each driving mode corresponds to fixed steering wheel adjustment parameters (such as the power steering system's power gain coefficient), providing the driver with a fixed degree of control feel.

[0004] The above-mentioned method of adjusting the softness by driving mode adopts a discrete classification setting and only supports a limited number of mode switches. It is difficult to meet the fine-grained requirements of different drivers for steering wheel control feel in different scenarios, resulting in insufficient personalized adaptability. In view of this, this application is filed. Summary of the Invention

[0005] The purpose of this application is to provide a method, device and storage medium for adjusting the softness of the steering wheel's control feel, so as to achieve personalized, automated and fine-grained adjustment of the softness of the steering wheel's control feel.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions: In a first aspect, the present application provides a method for adjusting the softness of a steering wheel's control feel, comprising: Collect data related to vehicle steering wheel operation; Understanding and reasoning the data to obtain a first softness range; performing multi-objective optimization on steering wheel adjustment parameters, wherein a second softness range corresponding to the optimized steering wheel adjustment parameters is within the first softness range; wherein the multi-objective optimization includes optimizing steering wheel control comfort, steering wheel control energy efficiency, and steering wheel control responsiveness; A target softness is selected from the second softness range, and a steering wheel adjustment parameter corresponding to the target softness is sent to a steering wheel electronic power assist system.

[0007] In a second aspect, the present application provides an electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors so that the at least one processor can execute the above-mentioned method for adjusting the softness of the steering wheel's control feel.

[0008] In a third aspect, the present application provides a computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions are used to enable a computer to execute the above-mentioned method for adjusting the softness of the steering wheel's control feel.

[0009] Compared with the prior art, the present invention has the following advantages: By integrating data related to vehicle steering wheel control, understanding and reasoning about the data, this application can accurately understand the driver's steering wheel adjustment needs in the current scenario, breaking the limitations of traditional fixed driving modes and achieving differentiated adjustment. This application uses a multi-objective optimization strategy for comfort, responsiveness, and energy efficiency to output the optimal steering wheel adjustment parameters, accurately driving the steering wheel electronic power steering system, and achieving the optimal match of softness while meeting multiple performance requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0011] Figure 1 This is a flow chart of a method for adjusting the softness of steering wheel control feel provided by an embodiment of the present application; Figure 2 is a schematic diagram of a steering wheel softness adjustment interface provided in an embodiment of the present application; Figure 3 This is a flow chart of another method for adjusting the softness of steering wheel control feel provided by an embodiment of the present application; Figure 4 This is a block diagram for understanding and reasoning about collected data provided by an embodiment of the present application; Figure 5 It is a structural diagram of the electronic device provided in this application. DETAILED DESCRIPTION

[0012] The following description of exemplary embodiments of the present application is made in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0013] The present application is further described in detail below with reference to the embodiments.

[0014] Figure 1 This is a flow chart of a method for adjusting the softness of the steering wheel's control feel provided by an embodiment of the present application. The method can be executed by a computer program and integrated into an electronic device, which can be an electronic control unit (ECU) or a vehicle-mounted communication module (Telematics box, T-box). This embodiment determines the softness in real time through the method for adjusting the softness of the steering wheel's control feel integrated in the ECU or T-box, and sends the steering wheel adjustment parameters corresponding to the softness to the steering wheel electronic power steering system, so as to adjust the softness through the steering wheel electronic power steering system. Figure 1 As shown, this embodiment provides a method for adjusting the softness of the steering wheel's control feel, comprising the following steps: S110: Collect data related to vehicle steering wheel control.

[0015] This embodiment can collect data from vehicle-side sensors, the CAN network, and the cloud. Optionally, it can also collect driver behavior data (such as steering wheel angle, steering speed, and change frequency), vehicle status data (such as speed, yaw rate, acceleration, and current driving mode), environmental perception data (such as road conditions, adhesion coefficient, and weather), driver voice (i.e., voice commands for steering wheel adjustments), and historical driver operation preference data (such as driving style, past steering wheel adjustment records, and the driver's steering wheel manipulation habits). All of this data is aggregated in real time to provide data support for subsequent analysis.

[0016] S120: Understand and infer the data to obtain a first softness range.

[0017] Based on all collected data, a model algorithm is used to understand the semantics and intent of the data and to perform logical reasoning on the understood content to determine a first softness range for the driver's steering wheel control softness (hereinafter referred to as steering wheel softness). Optionally, the overall softness range is predefined as [0, 1], where 0 represents the softest and 1 represents the hardest. This first softness range includes an upper and lower limit, for example, [0.1, 0.9].

[0018] S130. Perform multi-objective optimization on the steering wheel adjustment parameters, and the second softness range corresponding to the optimized steering wheel adjustment parameters is within the first softness range.

[0019] Steering wheel adjustment parameters are specific to the electronic steering wheel power steering system. Different values ​​for these parameters directly affect the steering wheel's handling feel. Optionally, these parameters include at least: the electric power steering motor's output current (i.e., the actual current output by the motor); the power assist gain factor (which determines the power assist strength of the power steering system and is related to steering wheel torque and vehicle speed); the inertia compensation factor (used to offset response delays caused by factors such as inertia and friction in the motor or mechanical structure); and the return control factor (used to adjust the dynamic characteristics of the return control).

[0020] Multi-objective optimization involves optimizing steering wheel comfort, steering wheel energy efficiency, and steering wheel responsiveness. Different values ​​of the steering wheel adjustment parameters correspond to different levels of steering wheel comfort, steering wheel energy efficiency, and steering wheel responsiveness. Furthermore, the steering wheel adjustment parameters are mapped to steering wheel softness, resulting in different levels of steering wheel softness for the driver. This step iterates through each softness within the first softness range to find a second softness range that satisfies the multi-objective optimization.

[0021] S140: Select a target softness from the second softness range, and send a steering wheel adjustment parameter corresponding to the target softness to the steering wheel electronic power assist system.

[0022] The target softness is any softness within the second softness range. Optionally, the second softness range is provided to the driver (e.g., via an image or voice); and in response to the driver's softness selection operation (e.g., operating a screen displaying the image or issuing a voice selection command), the target softness is determined from within the second softness range.

[0023] Figure 2 This is a schematic diagram of the steering wheel softness adjustment interface provided by an embodiment of the present application. The second softness range is displayed on the vehicle's human-machine interface and user fine-tuning instructions are received. Specifically, the recommended target softness for the current scenario and its second softness range are displayed to the driver in real time via the central control screen, instrument panel, head-up display, or in-vehicle voice assistant interface. Figure 2 The second softness range is [0.35, 0.85], and the recommended steering wheel softness parameter is 0.65. If the driver has subjective preferences for the current recommended value, the user is supported to make slight adjustments within the second softness range through knobs, touch, sliders, voice commands, etc. Figure 2 In the , slide the progress bar to select the softness parameter.

[0024] The steering wheel adjustment parameters corresponding to the driver's fine-tuned softness are sent to the electronic steering system, which then adjusts accordingly, ensuring the driver feels the target softness when maneuvering the steering wheel. Finally, the electronic steering system's execution information is fed back to the user interface or backend, and the driver's fine-tuning operations are synchronized and updated to the cloud.

[0025] Optionally, a target softness is selected from the second softness range based on the driver's historical softness preference data. For example, the softness selected by the driver within a recent period (e.g., a week) is obtained and the softness preference data, such as 0.6, is averaged. If 0.6 is within the second softness range, 0.6 is selected as the target softness. If 0.6 is not within the second softness range, the softness closest to 0.6 is selected from the second softness range. For example, if the second softness range is [0.3, 0.5], 0.5, which is closest to 0.6, is selected as the target softness.

[0026] By integrating data related to vehicle steering wheel control, understanding and reasoning about the data, this application can accurately understand the driver's steering wheel adjustment needs in the current scenario, breaking the limitations of traditional fixed driving modes and achieving differentiated adjustment. This application uses a multi-objective optimization strategy for comfort, responsiveness, and energy efficiency to output the optimal steering wheel adjustment parameters, accurately driving the steering wheel electronic power steering system, and achieving the optimal match of softness while meeting multiple performance requirements.

[0027] Figure 3 This is a flowchart of another method for adjusting the softness of the steering wheel's control feel provided in an embodiment of the present application. On the basis of the above embodiment, a determination and adjustment stage and a safety boundary verification stage are added. Figure 3 The methods provided include: S210: Collect data related to vehicle steering wheel control.

[0028] S220: Understand and infer the data to obtain a first softness range.

[0029] Figure 4 This is a block diagram for understanding and reasoning about collected data provided by the embodiment of the present application. Figure 4, understanding driver behavior data, vehicle status data, and environmental perception data to derive driving behavior and driving scenarios. Specifically, these data are fed into a driving behavior and scenario understanding model. This model can be a large, multimodal, fusion perception model using a Transformer model combined with long short-term memory (LSTM). This model models the context of driver behavior data, vehicle status data, and environmental perception data. By understanding the current driving behavior pattern in real time and combining it with current environmental perception data, it outputs driving behavior and driving scenarios. Examples include driving in congested urban areas, driving on highways, driving at low speeds on mountain roads, cornering on elevated roads, and driving on off-road sections.

[0030] Recognize the driver's voice and obtain the driver's softness requirements. Figure 4 The driver's voice data is input into a speech recognition model (such as Wav2Vec 2.0). The speech recognition model converts the voice data into text, which is then sent to the BERT+IntentClassifier model framework. BERT uses a bidirectional Transformer architecture to pre-train language representations and can capture contextual information. The IntentClassifier is a classifier consisting of a dropout layer and a linear layer. In this embodiment, BERT extracts features from the converted text. The IntentClassifier then uses the extracted features to accurately understand the driver's steering softness requirements. For example, the steering softness requirements output by the model framework include soft steering, hard steering, 30% soft steering, 30% hard steering, and 30% high steering.

[0031] The driver's historical operating preference data is understood to obtain the driver's softness preference. Figure 4 The system then inputs historical preference data, including driving style, past steering wheel adjustment records, and the driver's steering wheel manipulation habits, into a historical preference analysis model, a Time-series Transformer model for long-sequence modeling. This model extracts individual operating styles and adjustment preferences from the historical preference data and predicts the driver's preferred steering wheel softness in different driving scenarios. For example, a driver's preferred steering wheel softness is 0.7 when driving on highways, 0.75 when driving off-road, and 0.6 when driving in congested urban areas.

[0032] Next, a weight-based rule engine is used to obtain a first softness range according to driving behavior, driving scenario, softness requirement and softness preference.

[0033] Specifically, a rule base is pre-constructed, and the rule base includes upper and lower limits of softness corresponding to different driving behaviors, different driving scenarios, different softness requirements, and different softness preferences; the upper and lower limits of softness constitute the softness recommendation interval. The driving behaviors, driving scenarios, softness requirements, and softness preferences obtained from the above understanding are matched in the rule base to obtain softness recommendation intervals 1 to 4 corresponding to the driving behaviors, driving scenarios, softness requirements, and softness preferences. The weights of the driving behaviors, driving scenarios, softness requirements, and softness preferences are determined according to the current driving scenario. For example, in the driving scenario of a highway, more emphasis is placed on driving behaviors and driving scenarios, so the weights of driving behaviors and driving scenarios are increased, and the weights of driving behaviors, driving scenarios, softness requirements, and softness preferences are added together to be 1. According to the weights, the upper limits of softness corresponding to the driving behaviors, driving scenarios, softness requirements, and softness preferences are weighted averaged to obtain the upper limit of the first softness range; according to the weights, the lower limits of softness corresponding to the driving behaviors, driving scenarios, softness requirements, and softness preferences are weighted averaged to obtain the lower limit of the first softness range. See the following formula: Res_s=0.25×A_s+0.25×S_s+0.25×V_s+0.25×R_s; Formula (1) Res_x=0.35×A_x+0.35×S_x+0.2×V_x+0.1×R_x; Formula (2) Where Res_s is the upper limit of the first softness range, Res_x is the lower limit of the first softness range, A_s is the upper limit of softness corresponding to driving behavior, S_s is the upper limit of softness corresponding to driving scenario, V_s is the upper limit of softness corresponding to softness demand, R_s is the upper limit of softness corresponding to softness preference, 0.25 is the weight, A_x is the lower limit of softness corresponding to driving behavior, S_x is the lower limit of softness corresponding to driving scenario, V_x is the lower limit of softness corresponding to softness demand, and R_x is the lower limit of softness corresponding to softness preference. Formula (1) applies to driving scenarios on urban roads, and Formula (2) applies to driving scenarios on highways. The weights corresponding to driving behavior and driving scenarios are increased to 0.35.

[0034] S230: Calculate the distance between the vehicle's current flexibility and the first flexibility range, and determine the relationship between the distance and the set value. If the distance is greater than the set value, execute S240; otherwise, execute S210.

[0035] If the current softness is within the first softness range, the distance is 0; if the current softness is outside the first softness range, the smaller value of the distance between the current softness and the upper and lower limits of the first softness range is used as the distance between the current softness and the first softness range.

[0036] For example, the first softness range is [0.3, 0.8], and the current softness is 0.5. The current softness is within the first softness range, and the distance between the current softness and the first softness range is 0, which is less than the set value of 0.2. There is no need to adjust the steering wheel, and the system returns to continue collecting data related to vehicle steering wheel control.

[0037] For example, the first softness range is [0.3, 0.8], and the current softness is 0.05. The distance between the current softness and the lower limit of the first softness range is 0.25, which is greater than the set value 0.2. The current softness is significantly different from the first softness range, and S240 is executed for further judgment.

[0038] S240: Determine whether to trigger a multi-objective optimization operation of the steering wheel adjustment parameters based on the current driving scene. If yes, execute S250; otherwise, execute S210.

[0039] Preset multiple dangerous driving scenarios where steering wheel softness adjustment is not suitable, such as lane changes, turning, high-speed driving, steep slopes, and icy and snowy roads. If the current driving scenario falls into one of the pre-set dangerous driving scenarios, steering wheel softness adjustment is prohibited and the system returns to collecting data related to vehicle steering wheel operation. If the current driving scenario does not fall into one of the pre-set dangerous driving scenarios, steering wheel softness adjustment is permitted.

[0040] S250: Perform multi-objective optimization on the steering wheel adjustment parameters, and the second softness range corresponding to the optimized steering wheel adjustment parameters is within the first softness range.

[0041] First, the range of steering wheel adjustment parameters is traversed to obtain multiple steering wheel adjustment parameter groups with different values. The steering wheel adjustment parameter groups include the output current value of the electric power steering motor, the power assist gain coefficient, the inertia compensation coefficient, and the return-to-center control coefficient. Assuming that the output current value of the electric power steering motor ranges from 1 to 10, the power assist gain coefficient ranges from 0.1 to 1, the inertia compensation coefficient ranges from 0.1 to 1, and the return-to-center control coefficient ranges from 0.1 to 1, with a step size of 0.1, 100 × 10 × 10 × 10 = 100,000 steering wheel adjustment parameter groups are obtained.

[0042] Then, the actual softness corresponding to each steering wheel adjustment parameter set is determined. Due to the structural design of the electronic steering wheel power steering system, if a steering wheel adjustment parameter set is determined, the steering wheel will exhibit a corresponding control feel softness (for ease of distinction, the softness here is referred to as actual softness). Therefore, there is a corresponding relationship between the steering wheel adjustment parameters and the actual softness.

[0043] This embodiment constructs a steering wheel perception function S(x1, x2, x3, x4) from steering wheel adjustment parameters to softness, where x1, x2, x3, x4 are the output current value of the electric power steering motor, the power assist gain coefficient, the inertia compensation coefficient, and the return control coefficient, respectively, and S is the softness.

[0044] First, the output current value of the electric power assist motor, the assist gain coefficient, the inertia compensation coefficient, and the return control coefficient are divided by their respective maximum values ​​and normalized to obtain four normalized parameters, as shown in the following formula: x1_norm = x1 / 20.0; x2_norm = x2 / 5.0; x3_norm = x3 / 0.5; x4_norm = x4 / 20.0; Where 20.0 is the maximum electric assist motor output current, 5.0 is the maximum assist gain coefficient, 0.5 is the maximum inertia compensation coefficient, and 20.0 is the maximum return-to-positive control coefficient. x1_norm is the normalized electric assist motor output current, x2_norm is the normalized assist gain coefficient, x3_norm is the normalized inertia compensation coefficient, and x4_norm is the normalized return-to-positive control coefficient.

[0045] Then, the four normalized parameters are nonlinearly combined as shown in the following formula: feature1 = np.tanh(x1_norm×x2_norm); feature2 = np.exp(-x3_norm^2); feature3 = np.sin(np.pi×x4_norm / 2); feature4 = x1_norm×x3_norm×x4_norm; Here, feature1, feature2, feature3, and feature4 are the four eigenvalues ​​obtained through nonlinear operations. np.tanh is used to calculate the hyperbolic tangent of (x1_norm × x2_norm), np.exp is used to calculate the exponential of (-x3_norm^2), and np.sin is used to calculate the sine of (np.pi × x4_norm / 2). np.pi is the standard constant used to represent pi.

[0046] Next, perform a weighted sum operation on the aforementioned four eigenvalues ​​to obtain the softness S: S =0.4×feature1 + 0.3×feature2 + 0.2×feature3 + 0.1×feature4; Finally, a saturation nonlinearity is applied to ensure that the softness S output is within a reasonable range.

[0047] S = np.clip(S, 0, 1); Among them, np.clip is used to limit the softness S to a specified minimum value of 0 and a maximum value of 1.

[0048] This embodiment does not limit the specific form of the steering wheel perception function, which can be any multivariate nonlinear function. Each steering wheel adjustment parameter group is input into the steering wheel perception function to obtain the corresponding actual softness.

[0049] In this embodiment, the multi-objective optimization constraint is that the actual softness must be within the first softness range. If the actual softness is not within the first softness range, the steering wheel adjustment parameter set is discarded and the next steering wheel adjustment parameter set is traversed. If the actual softness is within the first softness range, the steering wheel control comfort, steering wheel control energy efficiency, and steering wheel control responsiveness are calculated based on the steering wheel adjustment parameter set to determine whether the actual softness meets the multi-objective optimization requirements. The multi-objective optimization process is described in detail below: The steering wheel adjustment parameter group determines the steering wheel's electromechanical performance, thereby affecting its handling comfort, energy efficiency, and responsiveness. For example, the return-to-center control coefficient can be used to determine the absolute value of the residual yaw rate after releasing the steering wheel, which is used to evaluate steering return comfort. The energy consumption of the electronic steering system can be determined by combining the output current of the electric power steering motor with the motor output voltage and time. Lower energy consumption indicates higher energy efficiency. In return-to-center control, the power assist gain coefficient determines the magnitude of the return-to-center torque. When the steering wheel angle is large, increasing the power assist gain coefficient quickly generates the return-to-center torque, shortening the return time and improving responsiveness. The inertia compensation coefficient uses the steering wheel angular acceleration signal to adjust the power assist torque in real time, reducing inertial delay during steering of heavy vehicles and thus improving responsiveness. A larger inertia compensation coefficient indicates higher responsiveness.

[0050] It should be noted that the calculation methods for steering wheel comfort, steering wheel energy efficiency, and steering wheel responsiveness described above are merely examples. Those skilled in the art can independently construct and calculate these values. This embodiment focuses on selecting a steering wheel adjustment parameter set to achieve optimal steering comfort, energy efficiency, and responsiveness, providing the driver with a positive steering experience while simultaneously achieving vehicle energy conservation.

[0051] If the steering wheel comfort, energy efficiency, and responsiveness exceed the set thresholds, the optimization requirements are met and a second softness range is determined based on the actual softness. After all steering wheel adjustment parameter groups are traversed, all actual softnesses are obtained, forming the second softness range. This indicates that the second softness range is within the first softness range.

[0052] S260: Determine a safety limit of softness based on the current vehicle state and driving scenario.

[0053] To ensure driving safety during the softness adjustment process, the second softness range is verified and narrowed. During the vehicle testing phase, prior to collecting data related to steering wheel operation, safety margin functions are derived through testing. These include the upper and lower safety margin functions described below.

[0054] Optionally, the vehicle's handling safety index values ​​(e.g., steering response delay, maximum lateral acceleration, maximum yaw rate, etc.) can be tested under different vehicle states (e.g., high / low speed) and driving scenarios (e.g., straight / curved roads, dry / slippery roads) at different levels of flexibility. For example, a specific level of flexibility can be set on the vehicle, and the vehicle can be controlled in a certain state and driving scenario. Onboard sensors can then collect values ​​such as steering angle, time, lateral acceleration, and yaw rate. The collected values ​​are then calculated to obtain a handling safety index value. The specific calculation formula is available in the prior art and will not be further described here. In this way, handling safety index values ​​corresponding to multiple levels of flexibility under a given vehicle state and driving scenario can be obtained.

[0055] For a given vehicle state and driving scenario, the handling safety index is compared with a set threshold (e.g., maximum steering response delay time threshold, maximum lateral acceleration threshold, maximum yaw rate threshold). A softness value below the set threshold is selected, and the upper and lower limits of the selected softness values ​​are used to form the safety margin. For example, for a given vehicle state and driving scenario, softness values ​​below the set threshold values ​​are 0.2, 0.3, 0.4, and 0.5, resulting in a safety margin of [0.2, 0.5], with a lower limit of 0.2 and an upper limit of 0.5. This ultimately forms a point set corresponding to the vehicle state, driving scenario, and upper and lower limits of the safety margin.

[0056] Based on the aforementioned point set, construct a function that maps the vehicle state and driving scene to the upper limit of the safety boundary, and a function that maps the vehicle state and driving scene to the lower limit of the safety boundary. If the parameters of the vehicle state and driving scene are relatively few, use linear interpolation to linearly interpolate the vehicle state, driving scene, and upper limit of the safety boundary, and then perform linear fitting on the interpolated point set to obtain the function of the upper limit of the safety boundary; use linear interpolation to linearly interpolate the vehicle state, driving scene, and lower limit of the safety boundary, and then perform linear fitting on the interpolated point set to obtain the function of the lower limit of the safety boundary. See the following formula: Y_s=a_1×m1+b_1×m2+c_1; Y_x=a_2×m1+b_2×m2+c_2; Among them, Y_s is the upper limit of the safety margin, Y_x is the lower limit of the safety margin, a_1, b_1, c_1, a_2, b_2, and c_2 are all parameters that need to be fitted, m1 is the variable of the vehicle state (that is, the quantitative value of the vehicle state), and m2 is the variable of the driving scene (that is, the quantitative value of the driving scene).

[0057] If there are many parameters for the vehicle state and driving scenario, polynomial fitting is used to fit the vehicle state, driving scenario, and upper / lower safety margins. Specifically, a polynomial fitting is performed using the vehicle state, driving scenario, and lower safety margin to obtain a function for the lower safety margin; a polynomial fitting is performed using the vehicle state, driving scenario, and upper safety margin to obtain a function for the upper safety margin. See the following formula: Y_s=n_0+n_1×m1+n_2×m2+n_3×m1 2 +n_4×m2 2 +n_5×m1×m2; Y_x=p_0+p_1×m1+p_2×m2+p_3×m1 2 +p_4×m2 2 +p_5×m1×m2; Among them, Y_s is the upper limit of the safety margin, Y_x is the lower limit of the safety margin, n_0, n_1, n_3, n_4, n_5, p_0, p_1, p_2, p_3, p_4, p_5 are all parameters that need to be fitted, m1 is the variable of the vehicle state (that is, the quantitative value of the vehicle state), and m2 is the variable of the driving scene (that is, the quantitative value of the driving scene).

[0058] This embodiment realizes non-polarization safety restriction by converting the point set into the form of a continuous function.

[0059] In this step, the current vehicle state and driving scene are substituted into the function of the upper limit of the safety boundary to obtain the upper limit of the safety boundary; the current vehicle state and driving scene are substituted into the function of the lower limit of the safety boundary to obtain the upper limit of the safety boundary. The upper and lower limits of the safety boundary constitute the safety boundary of softness.

[0060] S270: Determine whether the second softness range exceeds the safety limit. If yes, execute S280; otherwise, execute S290.

[0061] S280: Limit the second softness range according to the safety margin.

[0062] If the second flexibility range is outside the safety margin, it means the second flexibility range exceeds the safety margin. The excess will affect vehicle handling safety, so the excess will be limited. For example, if the second flexibility range is [0.2, 0.5] and the safety margin is [0.3, 0.8], then the second flexibility range (0.2, 0.3) will affect vehicle handling safety. The second flexibility range will be limited to [0.3, 0.5].

[0063] If the second softness range is within the safety margin, no shrinkage processing is performed.

[0064] S290: Select a target softness from the second softness range, and send a steering wheel adjustment parameter corresponding to the target softness to the steering wheel electronic power assist system.

[0065] If the second softness range has been narrowed, this step requires selecting a target softness within the narrowed second softness range.

[0066] Based on the above embodiments of the present application, the following technical effects are achieved: 1. Personalize the steering wheel's control feel and softness: By integrating multi-source data such as driver behavior, vehicle status, environmental perception, voice input, and historical preferences, and leveraging large models for understanding and reasoning, the system can accurately understand the driver's adjustment needs in different scenarios, breaking the limitations of traditional fixed driving modes and achieving differentiated adjustments.

[0067] 2. Improve the intelligence level of steering wheel adjustment: Through large models, it can understand multi-source data such as driving behavior, voice commands, and historical preferences, automatically identify adjustment needs, and intelligently determine whether to perform adjustment actions to avoid invalid or unsafe operations.

[0068] 3. Improve the safety and reliability of system responses: By introducing a safety boundary verification mechanism based on actual vehicle testing, we ensure that recommended or driver-adjusted values ​​are always within the safety boundary, effectively avoiding driving risks in extreme scenarios and ensuring driving safety.

[0069] 4. Introducing multi-objective optimization to enhance the overall driving experience: Through a multi-objective optimization strategy for comfort, responsiveness, and energy efficiency, the optimal steering wheel adjustment parameter combination is output, and the electronic steering power system is precisely driven to achieve the optimal match of softness while meeting multiple performance requirements.

[0070] 5. Supporting driver participation in fine-tuning, enhancing interactivity and control: This embodiment allows the driver to make controllable fine-tuning based on the second softness range and provide feedback through head-up display / voice / touch, thereby improving driver satisfaction and control confidence.

[0071] 6. Closed-loop feedback mechanism and data update: All steering wheel adjustment results and driver operating behaviors are uploaded to the cloud synchronously, which can be used for subsequent preference modeling and personality learning, enhancing the adaptability and continuous optimization capabilities of the adjustment method.

[0072] like Figure 5 As shown, this embodiment provides an electronic device, including: at least one processor; and a memory communicatively connected to at least one processor; wherein, The memory stores instructions executable by at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the above method. The at least one processor in the electronic device is capable of performing the above method, thereby having at least the same advantages as the above method.

[0073] Optionally, the electronic device also includes interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of a GUI (Graphical User Interface) on an external input / output device (such as a display device coupled to the interface). In other embodiments, if necessary, multiple processors can be used with multiple memories, and / or multiple buses can be used with multiple memories. Similarly, multiple electronic devices can be connected (for example, as a server array, a group of blade servers, or a multi-processor system), with each device providing part of the necessary operations. Figure 5 A processor 301 is taken as an example.

[0074] Memory 302, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the method for adjusting the softness of the steering wheel's handling feel in the embodiments of the present application. Processor 301 executes the software programs, instructions, and modules stored in memory 302 to execute various functional applications and data processing of the device, thereby implementing the aforementioned method for adjusting the softness of the steering wheel's handling feel.

[0075] The memory 302 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data created based on the use of the terminal, etc. Furthermore, the memory 302 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 302 may further include a memory remotely located relative to the processor 301, and these remote memories may be connected to the device via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0076] The electronic device may further include: an input device 303 and an output device 304. The processor 301, the memory 302, the input device 303 and the output device 304 may be connected via a bus or other means. Figure 5 The bus connection is taken as an example.

[0077] The input device 303 can receive input digital or character information, and the output device 304 can include a display device, an auxiliary lighting device (e.g., an LED), a tactile feedback device (e.g., a vibration motor), etc. The display device may include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touch screen.

[0078] This embodiment provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to cause a computer to execute the above method. The computer instructions on the computer-readable storage medium are used to cause a computer to execute the above method, thereby having at least the same advantages as the above method.

[0079] The medium in this application may be any combination of one or more computer-readable media. The medium may be a computer-readable signal medium or a computer-readable storage medium. The medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples of media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0080] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0081] The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, RF (Radio Frequency), etc., or any suitable combination of the foregoing.

[0082] Computer program code for performing the operations of the present application can be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone 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 can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0083] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired connection, such as a coaxial cable, optical fiber, digital subscriber line (DSL), or wireless connection, such as infrared, wireless, or microwave. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device, such as a server or data center, that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, hard disk, or magnetic tape), an optical medium, or a semiconductor medium. It is worth noting that the computer-readable storage medium mentioned in the embodiments of the present application may be a non-volatile storage medium, in other words, a non-transitory storage medium.

[0084] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this application can be achieved. This is not a limitation herein.

[0085] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A method for adjusting the softness of the steering wheel's control feel, characterized in that: include: Collect data related to vehicle steering wheel operation; Understanding and reasoning the data to obtain a first softness range; performing multi-objective optimization on steering wheel adjustment parameters, wherein a second softness range corresponding to the optimized steering wheel adjustment parameters is within the first softness range; wherein the multi-objective optimization includes optimizing steering wheel control comfort, steering wheel control energy efficiency, and steering wheel control responsiveness; A target softness is selected from the second softness range, and a steering wheel adjustment parameter corresponding to the target softness is sent to a steering wheel electronic power assist system.

2. The method for adjusting the softness of the steering wheel's control feel according to claim 1, characterized in that: After understanding and reasoning the data to obtain the first softness range, the method further includes: Calculating the distance between the current softness of the vehicle and the first softness range; If the distance is greater than the set value, it is determined whether to trigger a multi-objective optimization operation of the steering wheel adjustment parameters according to the current driving scene.

3. The method for adjusting the softness of the steering wheel's control feel according to claim 1, characterized in that: After performing multi-objective optimization on the steering wheel adjustment parameters and the second softness range corresponding to the optimized steering wheel adjustment parameters is within the first softness range, the method further includes: Determine the safety boundary of softness based on the current vehicle status and driving scenario; If the second softness range exceeds the safety margin, the second softness range is limited according to the safety margin.

4. The method for adjusting the softness of the steering wheel's control feel according to claim 3, characterized in that: Before collecting data related to vehicle steering wheel operation, it also includes: Under different vehicle conditions and driving scenarios, test the vehicle's handling safety index values ​​at different softness levels; Under a vehicle state and a driving scenario, the safety boundary of the softness is determined by comparing the control safety index value with the set index threshold, forming a point set of vehicle state, driving scenario and safety boundary; Based on the point set, constructing a function that maps from the vehicle state and the driving scene to the upper limit of the safety boundary and a function that maps to the lower limit of the safety boundary; Determine the safety boundary of softness based on the current vehicle status and driving scenario, including: The current vehicle state and driving scenario are brought into the function of the upper limit of the safety margin and the function of the lower limit of the safety margin to obtain the safety margin of softness.

5. The method for adjusting the softness of steering wheel control feel according to claim 1, characterized in that: Selecting a target softness from the second softness range includes: providing the second softness range to a driver; and determining a target softness from the second softness range in response to a softness selection operation by the driver; Alternatively, the target softness is selected from the second softness range according to the driver's historical softness preference data.

6. The method for adjusting the softness of steering wheel control feel according to claim 1, characterized in that: By understanding and reasoning the data, a first softness range is obtained, including: Understand driver behavior data, vehicle status data, and environmental perception data to obtain driving behavior and driving scenarios; Recognize the driver's voice and obtain the driver's softness requirements; Understanding the driver's historical operation preference data to obtain the driver's softness preference; A weight-based rule engine is used to obtain the first softness range according to driving behavior, driving scenario, softness requirements and softness preferences.

7. The method for adjusting the softness of steering wheel control feel according to claim 6, characterized in that: A weighted rule engine is used to derive the first softness range based on driving behavior, driving scenario, softness requirements, and softness preferences, including: A rule base is pre-built, wherein the rule base includes upper and lower limits of softness corresponding to different driving behaviors, different driving scenarios, different softness requirements, and different softness preferences; Matching the driving behavior, driving scenario, softness requirement, and softness preference in the rule library to obtain upper and lower softness limits corresponding to the driving behavior, driving scenario, softness requirement, and softness preference, respectively; Determine the weights of driving behavior, driving scenario, softness requirement, and softness preference based on the current driving scenario; performing a weighted average of the softness upper limits corresponding to the driving behavior, driving scenario, softness requirement, and softness preference according to the weights to obtain an upper limit of a first softness range; According to the weights, a weighted average of the softness lower limits corresponding to the driving behavior, driving scenario, softness requirement, and softness preference is performed to obtain a lower limit of the first softness range.

8. The method for adjusting the softness of steering wheel control feel according to claim 1, characterized in that: Performing multi-objective optimization on the steering wheel adjustment parameters, wherein a second softness range corresponding to the optimized steering wheel adjustment parameters is within the first softness range, includes: Traversing the range of the steering wheel adjustment parameters to obtain a plurality of steering wheel adjustment parameter groups with different values; wherein the steering wheel adjustment parameter group includes an output current value of the electric power assist motor, a power assist gain coefficient, an inertia compensation coefficient, and a return-to-center control coefficient; According to each steering wheel adjustment parameter group, the corresponding actual softness is obtained; If the actual softness is within the first softness range, calculating steering wheel control comfort, steering wheel control energy saving, and steering wheel control responsiveness according to the steering wheel adjustment parameter group; If the steering wheel control comfort, the steering wheel control energy saving and the steering wheel control responsiveness all meet the optimization requirements, a second softness range is obtained according to the actual softness.

9. An electronic device, characterized in that: include: at least one processor, and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute the method for adjusting the softness of the steering wheel's control feel according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that The medium stores computer instructions, which are used to enable a computer to execute the method for adjusting the softness of the steering wheel's control feel according to any one of claims 1 to 8.

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