Vehicle control method, electronic equipment and vehicle

Abstract

The invention relates to the technical field of vehicle control, in particular to a vehicle control method, electronic equipment and a vehicle. The method comprises the steps that driving parameters of a vehicle are obtained, and the driving parameters are parameters corresponding to the vehicle driven by a driver; according to the change degree of the driving parameters, a control change index of the driver is determined, and the control change index represents the change degree of vehicle operation of the driver; and according to the control change index, the driving parameters of the vehicle are adjusted, and the vehicle is controlled to run through the adjusted driving parameters. The control change index can be dynamically adjusted according to the driving parameters of the vehicle, so that the vehicle driving can be dynamically adapted and adjusted along with the style change of the driver, the driver does not need to manually adjust, and the driving experience is better.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a vehicle control method, electronic equipment, and vehicle. Background Technology

[0002] Currently, the vehicle's parameters are generally determined based on the driving mode specified by the driver, and the vehicle then drives according to these parameters.

[0003] However, these driving modes require manual switching and adjustment by the driver, which is inconvenient to use, and the relatively fixed driving modes cannot meet the diverse driving needs of drivers. Summary of the Invention

[0004] In view of this, the purpose of this application is to propose a vehicle control method, electronic device and vehicle to solve the technical problem that the current driving mode requires the driver to manually switch and adjust, which is inconvenient to use, and the driving mode is relatively fixed and cannot meet the diverse driving needs of the driver.

[0005] To achieve the above objectives, this application provides a vehicle control method, comprising: Obtain the driving parameters of the vehicle, wherein the driving parameters are parameters corresponding to the driver driving the vehicle; Based on the degree of change of the driving parameters, a driver's control change index is determined, wherein the control change index is a characterization of the degree of change in the driver's operation of the vehicle. Based on the aforementioned control change index, the vehicle's driving parameters are adjusted, and the adjusted driving parameters are used to control the vehicle's movement.

[0006] Based on the same inventive concept, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method described above when executing the computer program.

[0007] Based on the same inventive concept, this application also provides a vehicle including the electronic equipment described above.

[0008] As can be seen from the above, the vehicle control method, electronic equipment, and vehicle provided in this application can acquire the vehicle's driving parameters. These driving parameters correspond to the parameters used by the driver. Therefore, by measuring the degree of change in these driving parameters, a control change index, which characterizes the degree of change in the driver's operation of the vehicle, can be accurately determined. This control change index is related to the driver's driving style. Therefore, based on this control change index, various driving parameters of the vehicle can be accurately adjusted so that all driving parameters conform to the operating style corresponding to the control change index. Thus, when the vehicle is controlled according to the adjusted driving parameters, it better suits the driver's diverse operating styles. Because the control change index can be dynamically adjusted according to the vehicle's driving parameters, the vehicle's driving can dynamically adapt to changes in the driver's style without requiring manual adjustment by the driver, resulting in a better driving experience. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart of a vehicle control method according to an embodiment of this application; Figure 2 This is a structural block diagram of a vehicle control device according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0012] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0013] In related technologies, vehicles generally have multiple preset driving modes (such as Eco, Normal, and Sport) to meet different user driving preferences (such as an economy mode for energy saving or a sport mode for responsiveness). Users can manually switch between these modes via physical buttons, knobs, or the central control screen menu. Based on the selected mode, the vehicle calls a pre-calibrated set of parameters to make overall adjustments to steering assist, throttle response, transmission shift logic, suspension damping (if supported), and energy recovery intensity.

[0014] This approach in related technologies has the following problems: 1. Cumbersome interaction and fragmented experience: Drivers need to actively find and switch modes based on their own feelings or changes in road conditions, disrupting the continuity of driving. For users unfamiliar with the vehicle settings, this function is practically useless.

[0015] Second, the calibration is singular and cannot be personalized: the "sport" or "economy" modes calibrated at the factory are based on engineers' assumptions about typical users and cannot meet the personalized needs of all drivers.

[0016] 3. Conflicts in shared vehicles: When multiple family members with different driving styles share a car, the frequent manual switching is extremely inconvenient, and the driving experience is often poor because they forget to switch.

[0017] Based on the above, the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0018] The vehicle control method proposed in the embodiments of this application is applied to the vehicle controller.

[0019] like Figure 1 As shown, the method includes: Step 101: Obtain the driving parameters of the vehicle, wherein the driving parameters are parameters corresponding to the driver driving the vehicle.

[0020] In practice, the vehicle is equipped with corresponding data acquisition devices (e.g., various sensors located in the chassis area) capable of collecting the vehicle's driving parameters. These driving parameters include at least one of the following: steering wheel angle, longitudinal acceleration, accelerator pedal position, and brake pedal position.

[0021] The data acquisition unit sends these driving parameters to the controller, which is equipped with a filtering unit that can filter these driving parameters, remove interference noise, and obtain accurate driving parameters.

[0022] Step 102: Determine the driver's control change index based on the degree of change of the driving parameters, wherein the control change index is a characterization of the degree of change in the driver's operation of the vehicle.

[0023] In practice, after the controller obtains the driving parameters, it determines the degree of change of each driving parameter. The higher the degree of change, the higher the degree of change in the driver's operation of the vehicle, the more aggressive the corresponding operating style, and therefore the higher the corresponding control change index.

[0024] The controller can pre-store the control change values ​​corresponding to the degree of change of various driving parameters, specifically through tables, function curves, or key-value relationships.

[0025] Once the controller determines the degree of change in the driving parameters, it can retrieve the corresponding control change values ​​from storage. By combining the control change values ​​corresponding to the degree of change in each driving parameter, an accurate control change index that characterizes the degree of change in the driver's operation of the vehicle can be obtained (for example, by directly summing the control change values, or by weighted summation, or by using other algorithms to calculate the control change index).

[0026] The control change index can also be determined using the specific determination process in subsequent embodiments of this application to obtain a more accurate control change index. The specific method for determining the control change index can be selected based on the user's actual needs.

[0027] Step 103: Adjust the vehicle's driving parameters according to the control change index, and control the vehicle's driving using the adjusted driving parameters.

[0028] In practice, the vehicle is also equipped with various driving actuators (e.g., at least one of a steering wheel power assist unit, throttle response unit, energy recovery unit, and suspension damping control unit), which are all connected to the controller to control the corresponding driving functions.

[0029] After receiving the handling change index, the controller can adjust the vehicle's driving parameters. The specific adjustment process is as follows: the handling change index can be weighted and applied to the corresponding driving parameters to complete the adjustment, resulting in the adjusted driving parameters; alternatively, other adjustment strategies can be employed to obtain the corresponding adjusted driving parameters. The adjusted driving parameters are then sent to the corresponding driving actuators, which in turn control the vehicle's movement according to these adjusted parameters.

[0030] The above method allows for the acquisition of vehicle driving parameters, which correspond to the driver's driving style. Therefore, by analyzing the degree of change in these driving parameters, a control change index can be accurately determined, representing the degree of variation in the driver's vehicle operation. This control change index is related to the driver's driving style. Based on this index, various vehicle driving parameters can be precisely adjusted to align with the corresponding driving style. This ensures that when the vehicle is controlled according to the adjusted driving parameters, it better suits the driver's diverse driving styles. Because the control change index can dynamically adjust based on the vehicle's driving parameters, the vehicle's performance adapts dynamically to changes in the driver's style, eliminating the need for manual adjustments and resulting in a superior driving experience.

[0031] In some embodiments, step 102 includes: Step 1021: Determine the degree of change of the driving parameters, and transform the degree of change into a feature vector to obtain a behavior feature vector.

[0032] In practice, one could statistically analyze the degree of change of driving parameters over a period of time, calculate the average value of this degree of change, and then perform feature vectorization on the average value of this degree of change to obtain the corresponding behavioral feature vector.

[0033] The corresponding driving parameters have at least one type, and the corresponding degree of change also has at least one type. The degree of change of various types is combined to form an array, and the feature vector is transformed to obtain the behavior feature vector.

[0034] Step 1022: Obtain multiple preset cluster center vectors, where each cluster center vector represents a driving control style.

[0035] In practice, based on the actual conditions of the vehicle, cluster center vectors matching the aforementioned behavioral feature vectors are determined in advance for each driving style. Each driving style corresponds to a cluster center vector, and the parameters of this cluster center vector are of the same type as the parameters of the behavioral feature vector, but with different values.

[0036] For example, the mild style corresponds to the first cluster center vector C1, the neutral style corresponds to the second cluster center vector C2, and the radical style corresponds to the third cluster center vector C3.

[0037] Step 1023: Determine the variation range of the behavior feature vector in multiple cluster center vectors, and determine the corresponding driver's control change index based on the variation range, wherein the variation range is the interval with one cluster center vector as the starting point and another cluster center vector as the ending point.

[0038] In practice, the starting and ending points of driver style changes can be selected from multiple cluster center vectors based on the values ​​of the behavioral feature vectors, forming a change range. For example, the driver style change range could be a range from mild to neutral, or from mild to aggressive, or from neutral to aggressive.

[0039] After obtaining the range of change, the driver's control change data can be determined based on the range of change, and then the control change index can be obtained.

[0040] For example, different change ranges and corresponding manipulation change indices corresponding to the values ​​of behavioral feature vectors can be pre-set and stored in a table, function curve or key-value relationship. In this way, the corresponding manipulation change index can be retrieved directly based on the change range and behavioral feature vector (this is only one way to determine the manipulation change index; the manipulation change index can also be determined in the manner described in the following embodiments).

[0041] The above scheme can determine the degree of change based on the driving parameters obtained by the driver operating the vehicle, and then transform them into a more suitable behavioral feature vector for calculation. Based on the behavioral feature vector, the corresponding change range can be determined from multiple preset cluster center vectors. In this way, an accurate control change index can be determined based on the change range, which can more accurately reflect the driver's driving style.

[0042] In some embodiments, step 1021 includes: Step 10211: Extract the steering angular velocity from the driving parameters, and determine the first number of times the steering angular velocity is greater than the steering angular velocity threshold within a first set time period. Combine the steering angular velocity with the first number of times to determine the steering change degree.

[0043] In specific implementation, the driving parameters include: steering angular velocity. The system can count the number of times the steering angular velocity exceeds a steering angular velocity threshold within a first set time period (e.g., 60s, 70s, or 80s, preferably 60s, which can be set according to actual needs). The average steering angular velocity within the first set time period is also calculated. The steering change is obtained by combining the first count with the steering angular velocity or the average steering angular velocity.

[0044] Corresponding steering change The calculation formula is: ; in, As the first weighting coefficient, For the first count, This is the steering angular velocity or the average value of the steering angular velocity. In this formula, the units of each parameter are not included in the calculation; only the specific values ​​are used for calculation.

[0045] And / or, in step 10212, extract the accelerator pedal change rate and acceleration from the driving parameters, and determine the second number corresponding to the second number when the accelerator pedal change rate is greater than the accelerator pedal change threshold and the acceleration is greater than the first acceleration threshold within the second set time period, and determine the accelerator pedal change degree based on the second number.

[0046] In practice, the rate of change of the accelerator pedal can be determined based on the rate of change of the accelerator pedal position (for example, the rate of change of the accelerator pedal is obtained by differentiating the accelerator pedal position).

[0047] The second number of times the driver operates the accelerator pedal to simultaneously meet the following two conditions within a second set time period (e.g., 60s, 70s, or 80s, preferably 60s, which can be set according to actual needs).

[0048] Condition 1: The rate of change of the accelerator pedal is greater than the threshold value for the change of the accelerator pedal. Condition 2: The acceleration is greater than the first acceleration threshold (e.g., 0.3g, 0.4g or 0.5g, preferably 0.3g).

[0049] The value of the second number can be directly used as the accelerator pedal change, or the value of the second number after weighting can be used as the accelerator pedal change (the unit of the second number is not included in the calculation of the accelerator pedal change).

[0050] And / or, in step 10213, extract the brake pedal change rate and acceleration from the driving parameters, and determine the third number corresponding to the third time period when the brake pedal change rate is greater than the brake pedal change threshold and the acceleration is less than the second acceleration threshold. The brake pedal change degree is determined based on the third number.

[0051] In practice, the rate of change of the brake pedal can be determined based on the rate of change of the brake pedal position (for example, the rate of change of the brake pedal is obtained by differentiating the brake pedal position).

[0052] The third count of the number of times the driver operates the brake pedal while simultaneously satisfying the following two conditions within a third set time period (e.g., 60s, 70s, or 80s, preferably 60s, which can be set according to actual needs).

[0053] Condition 1: The rate of change of the brake pedal is greater than the threshold value of the brake pedal change; Condition 2: Small acceleration second acceleration threshold (e.g., -0.3g, -0.4g or -0.5g, preferably -0.3g).

[0054] The value of the third number can be directly used as the brake pedal change, or the value of the third number after weighting can be used as the brake pedal change (wherein, the unit of the third number is not included in the calculation of the brake pedal change).

[0055] Steps 10211 to 10213 above can be performed at least one of them. If multiple steps are to be performed, they can be performed simultaneously or sequentially (the corresponding order can be set arbitrarily, and no specific limitation is made here).

[0056] Step 10214: Determine the behavior feature vector based on at least one of the steering change degree, the accelerator pedal change degree, and the brake pedal change degree.

[0057] In practice, at least one of the steering change degree, accelerator pedal change degree, and brake pedal change degree can be selected according to actual needs, and arranged in a matrix to obtain the behavior feature vector. For example, Preferably, the steering change degree, accelerator pedal change degree, and brake pedal change degree are selected and arranged in a matrix. The resulting behavioral feature vector has three types of data: steering, accelerator pedal, and brake pedal, which can more accurately represent the driver's aggressive operation of the vehicle.

[0058] The above scheme can characterize the aggressiveness of a driver's driving behavior based on at least one of the following three factors: steering angular velocity, accelerator pedal change combined with acceleration, and brake pedal change combined with acceleration. The behavioral feature vector determined based on these factors is more accurate.

[0059] In some embodiments, step 1023 includes: Step 10231: Determine the distance between the behavioral feature vector and each cluster center vector, and determine the starting center vector and the ending center vector from multiple cluster center vectors based on the distance, and determine the change range based on the starting center vector and the ending center vector.

[0060] In practice, since the dimension of the corresponding cluster center vector is the same as the dimension of the behavioral feature vector, the distance between the behavioral feature vector and each cluster center vector can be directly calculated (e.g., Euclidean distance). Then, the most matching start center vector and end center vector can be selected from multiple cluster center vectors based on the distance, thus obtaining an accurate range of variation.

[0061] Step 10232: Determine the change coefficient based on the change range, and combine the difference between the endpoint center vector and the starting point center vector with the change coefficient to determine the undetermined control change index.

[0062] In practice, the coefficients of change corresponding to each range of change can be pre-stored. The storage methods include at least one of the following: tables, function curves, and key-value pairs. After obtaining the range of change, the corresponding coefficients of change can be directly retrieved from storage or calculated.

[0063] Then, the difference between the endpoint center vector and the starting point center vector is determined. This difference can be the difference between the endpoint center vector and the starting point center vector or the difference of squares. This difference is then combined with the obtained coefficient of change according to a predetermined functional relationship to calculate the undetermined control change index. This predetermined functional relationship can be at least one of a weighted summation function, a weighted function, or a summation function.

[0064] Step 10233: Determine the driver's control change index based on the undetermined control change index.

[0065] In practice, the pending control change index can be directly added as the control change index; or, the pending control change index can be weighted and used as the control change index; or, the control change index can be obtained by weighting and summing the pending control change index with the average of the historical control change indices obtained in the previous predetermined time period.

[0066] The above scheme can combine behavioral feature vectors to determine the range of changes that accurately reflects the driver's operating behavior, and then obtain an accurate change coefficient based on this range. This coefficient is then combined with the difference between the determined endpoint center vector and the starting point center vector to obtain an accurate undetermined control change index. This undetermined control change index can be directly used as the control change index, or it can be processed by other operations to obtain the control change index, thereby improving the accuracy of the control change index.

[0067] In some embodiments, step 10231 includes: Step 102311: Determine the distance between the behavioral feature vector and each cluster center vector, and determine the minimum at least two distances.

[0068] In practice, the distance (e.g., Euclidean distance) between the behavioral feature vector and each cluster center vector is calculated using the following formula: ; Where x, y, and z represent the behavioral feature vector H Three elements; This represents the three elements corresponding to the cluster center vector. The units of the parameters in this formula are not involved in the calculation.

[0069] In this way, the number of distances obtained is the same as the number of cluster center vectors. Then, the distances are sorted in ascending order, and at least the first two distances in the sort are selected; or, the distances are sorted in descending order, and at least the last two distances in the sort are selected.

[0070] Step 102312: Determine at least two target cluster center vectors corresponding to the minimum at least two distances.

[0071] In practice, the cluster center vector corresponding to the minimum distance is retrieved and used as the target cluster center vector, thereby obtaining at least two target cluster center vectors.

[0072] For example, the two smallest distances are determined and arranged according to their distance size, and are identified as the first smallest distance and the second smallest distance. Then, the first target cluster center vector corresponding to the first smallest distance and the second target cluster center vector corresponding to the second smallest distance are obtained.

[0073] Step 102313: Based on the at least two target cluster center vectors, determine the starting center vector and the ending center vector from multiple cluster center vectors, and determine the change range based on the starting center vector and the ending center vector.

[0074] In practice, the smallest of the at least two target cluster center vectors can be used as the starting center vector and the largest as the ending center vector; or, the smallest neighboring cluster center vector can be used as the starting center vector and the largest neighboring cluster center vector as the ending center vector; or the starting center vector and ending center vector can be determined by a combination of the above two methods.

[0075] For example, the pre-set cluster center vectors include: mild style corresponds to the first cluster center vector C1, neutral style corresponds to the second cluster center vector C2, and radical style corresponds to the third cluster center vector C3.

[0076] The first target cluster center vector corresponding to the first minimum distance obtained above, and the second target cluster center vector corresponding to the second minimum distance.

[0077] If the first target cluster center vector is C1 mild style, the second target cluster center vector is C2 neutral style, the starting center vector of the variation interval is C1 mild style, and the ending center vector is C3 radical style; If the first target cluster center vector is C2 neutral style, the second target cluster center vector is C1 mild style, the starting center vector of the variation interval is C1 mild style, and the ending center vector is C3 radical style; If the first target cluster center vector is C2 neutral style, the second target cluster center vector is C3 radical style, the starting center vector of the change interval is C2 neutral style, and the ending center vector is C3 radical style; If the first target cluster center vector is C3 radical style, the second target cluster center vector is C2 neutral style, the starting center vector of the variation interval is C2 neutral style, and the ending center vector is C3 radical style.

[0078] The above scheme can accurately filter the minimum two distances between the behavioral feature vector and each cluster center vector, thereby determining the minimum two target cluster center vectors that best match the behavioral feature vector. In this way, the starting center vector and the ending center vector can be accurately determined based on the minimum two target cluster center vectors, thereby determining the accurate change range, which is convenient for the subsequent determination of the pending control change index.

[0079] In some embodiments, step 10232 includes: Step 102321: Subtract the sum of squares of all elements in the starting center vector from the sum of squares of all elements in the behavioral feature vector to obtain the first difference.

[0080] Step 102322: Subtract the sum of squares of the elements in the starting point center vector from the sum of squares of the elements in the ending point center vector to obtain the second difference.

[0081] Step 102323: Use the ratio of the first difference to the second difference as the change coefficient.

[0082] In practice, the formula for calculating the coefficient of variation K is as follows: .

[0083] Step 102324: Obtain the initial value of the endpoint center vector and the initial value of the starting point center vector; subtract the initial value of the starting point from the initial value of the endpoint to obtain the third difference; multiply the change coefficient by the third difference to obtain the first product result.

[0084] In practice, the initial values ​​of each cluster center vector are preset. For example, the initial value of the first cluster center vector C1 of the mild style is 0, the initial value of the second cluster center vector C2 of the neutral style is 0.5, and the initial value of the third cluster center vector C3 of the aggressive style is 1.

[0085] The formula for the first product result is K × (initial value at the end point - initial value at the starting point).

[0086] Step 102325: Add the first product result to the initial value of the starting point to obtain the undetermined control change index.

[0087] In practice, the undetermined manipulation change index AI 待 The calculation formula is: AI 待 =Initial value at the starting point + first product result = Initial value at the starting point + K × (Initial value at the ending point - Initial value at the starting point).

[0088] Among them, K and AI 待 All values ​​are within the range of 0 to 1.

[0089] The above scheme can accurately combine the values ​​of each element of the behavioral feature vector, the values ​​of each element of the determined starting point center vector, and the values ​​of each element of the ending point center vector to accurately determine the corresponding change coefficient. Then, by subtracting the starting point initial value from the ending point initial value and multiplying it by the change coefficient, and adding the starting point initial value, an accurate undetermined control change index can be obtained.

[0090] In some embodiments, step 10233 includes: Obtain the control change index of the previous time period, and perform a weighted summation of the control change index of the previous time period and the control change index to be determined to obtain the driver's control change index.

[0091] In practice, the calculation formula for the manipulation change index AI is as follows: ;in, For forgetting weight, This represents the control change index of k-1 in the previous time period. The index represents the undetermined control change.

[0092] The above scheme combines the handling change index from the previous time period with the currently obtained undetermined handling change index, so that the determined handling change index conforms to both historical driving habits and the needs of current driving behavior, resulting in a more accurate handling change index.

[0093] In some embodiments, step 103 includes: Step 1031: retrieve the maximum and minimum steering assist values, multiply the fourth difference between the maximum and minimum steering assist values ​​by the control change index to obtain the steering assist attenuation amount, subtract the steering assist attenuation amount from the maximum steering assist value to obtain the adjusted steering assist, and use the adjusted steering assist to control the vehicle's steering and driving.

[0094] In practice, after obtaining the control change index AI, the controller retrieves the maximum steering assist value from storage. and minimum assist value .

[0095] Corresponding adjustment to the power steering The calculation formula is: .

[0096] This is how the adjusted steering assist is obtained when AI approaches zero. The steering assist is closer to the first cluster center vector C1 corresponding to the milder style, at which point the steering assist is at its maximum and the steering is easier.

[0097] When AI approaches 1, the adjusted steering assist The steering assist is closer to the third cluster center vector C3 corresponding to the aggressive style, at which point the steering assist is minimal and the steering is more stable.

[0098] And / or, in step 1032, retrieve the corresponding accelerator pedal torque response curve according to the control change index, and respond to the vehicle's accelerator pedal request according to the accelerator pedal torque response curve.

[0099] In practice, the accelerator pedal torque response curves corresponding to each control change index are pre-stored. The storage methods are tables, key-value pairs, etc. For example, the accelerator pedal torque response curve corresponding to the control change index is determined by looking up a table, or by using interpolation.

[0100] This allows the accelerator pedal to respond to torque according to the obtained torque response curve, making the torque response more in line with the driver's operating style and making the accelerator pedal more convenient and easy to use.

[0101] And / or, in step 1033, retrieve the maximum energy recovery intensity value, determine the energy recovery attenuation weight according to the control change index, multiply the maximum energy recovery intensity value by the energy recovery attenuation weight to obtain the adjusted energy recovery intensity, and use the adjusted energy recovery intensity to control the vehicle's energy recovery function.

[0102] In practice, the maximum energy recovery intensity value is pre-stored in the controller. Thus, after obtaining the AI-controlled change index, the adjusted energy recovery intensity The calculation formula is: .

[0103] Thus, when AI approaches 0, the adjusted energy recovery intensity is obtained. It is closer to the energy recovery intensity corresponding to the first cluster center vector C1 of the mild style, at which point the energy recovery intensity is the largest and the efficiency is higher.

[0104] When AI approaches 1, the adjusted energy recovery intensity is obtained. It is closer to the energy recovery intensity corresponding to the third cluster center vector C3 of the radical style, at which point the energy recovery intensity is the weakest and the gliding is smoother.

[0105] And / or, in step 1034, retrieve the hard damping reference value and soft damping reference value of the suspension, subtract the soft damping reference value from the hard damping reference value and multiply it by the handling change index to obtain the damping reinforcement value, add the damping reinforcement value to the soft damping reference value to obtain the adjusted damping value, and adjust the vehicle suspension according to the adjusted damping value.

[0106] In practice, the suspension's stiff damping reference values ​​are pre-stored in the controller. and soft damping reference value .

[0107] Thus, after obtaining the AI-controlled change index, the adjusted damping value is obtained. The calculation formula is: .

[0108] When AI approaches 0, the adjusted damping value It is closer to the damping value corresponding to the first cluster center vector C1 of the mild style. At this time, the suspension is soft and prioritizes vibration filtering and comfort.

[0109] When AI approaches 1, the adjusted damping value The damping value is closer to the third cluster center vector C3 corresponding to the aggressive style, at which point the suspension is stiff and the handling stability is improved.

[0110] To ensure a smooth and stable transition without abrupt changes in the adjusted driving parameters (e.g., adjusted steering assist, obtained accelerator pedal torque response curve, adjusted energy recovery intensity, and / or adjusted damping value), the calculated adjusted driving parameters can be subjected to first-order hysteresis filtering to ensure a consistent and natural change in the adjusted driving parameters.

[0111] The above scheme enables effective and reasonable adjustment of at least one of the following parameters based on the handling change index: steering assist, accelerator pedal torque response curve, energy recovery intensity, and suspension damping value. This makes these parameters more consistent with the driver's driving style, and these driving styles can be dynamically adjusted according to the dynamic changes in the driver's driving parameters to adapt to changes in the driver's driving style under different conditions.

[0112] In a preferred embodiment, a corresponding account is also set up for the driver. When the vehicle is powered off, the corresponding driving parameters and the adjusted driving parameters obtained from the dynamic changes of the corresponding driving parameters are associated with and stored with the driver's account. So when the driver drives the vehicle again, he can directly retrieve the corresponding adjusted driving parameters based on the corresponding driving parameters to configure and control the vehicle, without having to go through the above-mentioned complicated adjustment process, which is convenient to use.

[0113] The vehicle control method of this application embodiment is described below with a specific example. The specific process is as follows: I. Data Collection.

[0114] Various sensors are installed in the chassis area to collect various driving parameters sent by these sensors.

[0115] Driving parameters include: steering wheel angle θ, longitudinal acceleration ax, accelerator pedal position P_Acc, and brake pedal position P_brk.

[0116] II. Determination of behavioral characteristics.

[0117] (1) Steering intensity (i.e., steering change).

[0118] Driving parameters include: steering angular velocity (obtained by differentiating the steering wheel angle θ). The system can count the number of times the steering angular velocity exceeds a threshold within a first set time period (e.g., 60s, 70s, or 80s, preferably 60s, which can be set according to actual needs). It also calculates the average of all steering angular velocities exceeding the threshold within the first set time period, and combines the first count with the average steering angular velocity to obtain the steering change degree.

[0119] Corresponding steering change The calculation formula is: ; in, As the first weighting coefficient, For the first count, This is the steering angular velocity or the average value of the steering angular velocity. In this formula, the units of each parameter are not included in the calculation; only the specific values ​​are used for calculation.

[0120] (2) Acceleration intensity (i.e., the degree of change of the accelerator pedal).

[0121] The rate of change of the accelerator pedal can be determined based on the rate of change of the accelerator pedal position (for example, the rate of change of the accelerator pedal position P_Acc is obtained by differentiating the accelerator pedal position P_Acc).

[0122] The second number of times the driver operates the accelerator pedal while simultaneously satisfying the following two conditions within a second set time period (e.g., 60s, 70s, or 80s, preferably 60s, which can be set according to actual needs). .

[0123] Condition 1: The rate of change of the accelerator pedal is greater than the threshold value for the change of the accelerator pedal. Condition 2: The acceleration is greater than the first acceleration threshold (e.g., 0.3g, 0.4g or 0.5g, preferably 0.3g).

[0124] You can directly use the second number The value of is used as the accelerator pedal change degree It could also be the second number. The weighted value is used as the accelerator pedal change (where the second number...). The unit is not included in the calculation of accelerator pedal change.

[0125] (3) Braking intensity (i.e., brake pedal variation).

[0126] The rate of change of the brake pedal can be determined based on the rate of change of the brake pedal position (for example, the rate of change of the brake pedal can be obtained by differentiating the brake pedal position P_Brk).

[0127] The third count is calculated within a set time period (e.g., 60s, 70s, or 80s, preferably 60s, which can be set according to actual needs) when the driver operates the brake pedal and simultaneously meets the following two conditions. .

[0128] Condition 1: The rate of change of the brake pedal is greater than the threshold value of the brake pedal change; Condition 2: Small acceleration second acceleration threshold (e.g., -0.3g, -0.4g or -0.5g, preferably -0.3g).

[0129] You can directly use the third number The value of is used as the brake pedal change degree It could also be the third number. The weighted values ​​are used as the brake pedal change (where the third value is the value of the third value). The unit is not included in the calculation of brake pedal variation.

[0130] The results obtained above are integrated to obtain the driver's corresponding behavioral feature vector H, as shown in the formula: .

[0131] III. Process of determining the handling style.

[0132] (1) Initialization.

[0133] Three cluster center vectors are pre-defined, each corresponding to a driving control style. The dimensions of each cluster center vector and the parameter types corresponding to each dimension are the same as those of the behavior feature vector.

[0134] The three cluster center vectors are: The mild style corresponds to the first cluster center vector C1, and the initial value of the first cluster center vector C1 is set to 0; The neutral style corresponds to the second cluster center vector C2, and the initial value of the second cluster center vector C2 is set to 0.5; The radical style corresponds to the third cluster center vector C3, and the initial value of the third cluster center vector C3 is set to 1.

[0135] (2) Determine the Euclidean distance between the behavioral feature vector and each cluster center vector.

[0136] The formula for calculating Euclidean distance is: ; Where x, y, and z represent the behavioral feature vector H Three elements; This represents the three elements corresponding to the cluster center vector. The units of the parameters in this formula are not involved in the calculation.

[0137] This yields three Euclidean distances, which are then sorted in ascending order.

[0138] (3) Calculate the manipulation change index AI.

[0139] Based on the sorting of the three Euclidean distances obtained above, find the first target cluster center vector corresponding to the first smallest distance, and the second target cluster center vector corresponding to the second smallest distance.

[0140] If the first target cluster center vector is C1 mild style, the second target cluster center vector is C2 neutral style, the starting center vector of the variation interval is C1 mild style, and the ending center vector is C3 radical style; If the first target cluster center vector is C2 neutral style, the second target cluster center vector is C1 mild style, the starting center vector of the variation interval is C1 mild style, and the ending center vector is C3 radical style; If the first target cluster center vector is C2 neutral style, the second target cluster center vector is C3 radical style, the starting center vector of the change interval is C2 neutral style, and the ending center vector is C3 radical style; If the first target cluster center vector is C3 radical style, the second target cluster center vector is C2 neutral style, the starting center vector of the variation interval is C2 neutral style, and the ending center vector is C3 radical style.

[0141] The formula for calculating the coefficient of variation K is: .

[0142] Undetermined manipulation change index AI 待 The calculation formula is: AI 待 =Initial value at the starting point + first product result = Initial value at the starting point + K × (Initial value at the ending point - Initial value at the starting point).

[0143] Among them, K and AI 待 All values ​​are within the range of 0 to 1.

[0144] The formula for calculating the AI ​​index of manipulation change is: ;in, The forgetting weight ranges from 0 to 1, with a preferred value of 0.9. The specific value can be set according to actual needs. This represents the control change index of k-1 in the previous time period.

[0145] IV. Dynamic parameter adjustment.

[0146] (1) Adjustment of steering parameters.

[0147] After obtaining the AI-generated steering change index, the controller retrieves the maximum steering assist value from storage. and minimum assist value .

[0148] Corresponding adjustment to the power steering The calculation formula is: .

[0149] This is how the adjusted steering assist is obtained when AI approaches zero. The steering assist is closer to the first cluster center vector C1 corresponding to the milder style, at which point the steering assist is at its maximum and the steering is easier.

[0150] When AI approaches 1, the adjusted steering assist The steering assist is closer to the third cluster center vector C3 corresponding to the aggressive style, at which point the steering assist is minimal and the steering is more stable.

[0151] (2) Adjusting the accelerator pedal.

[0152] The accelerator pedal torque response curves corresponding to each control change index are pre-stored. The storage methods are tables, key-value pairs, etc. For example, the accelerator pedal torque response curve corresponding to the control change index can be determined by looking up a table, or by using interpolation.

[0153] This allows the accelerator pedal to respond to torque according to the obtained torque response curve, making the torque response more in line with the driver's operating style and making the accelerator pedal more convenient and easy to use.

[0154] (3) Adjustment of energy recovery intensity.

[0155] The maximum intensity value of energy recovery is stored in the controller in advance. Thus, after obtaining the AI-controlled change index, the adjusted energy recovery intensity The calculation formula is: .

[0156] Thus, when AI approaches 0, the adjusted energy recovery intensity is obtained. It is closer to the energy recovery intensity corresponding to the first cluster center vector C1 of the mild style, at which point the energy recovery intensity is the largest and the efficiency is higher.

[0157] When AI approaches 1, the adjusted energy recovery intensity is obtained. It is closer to the energy recovery intensity corresponding to the third cluster center vector C3 of the radical style, at which point the energy recovery intensity is the weakest and the gliding is smoother.

[0158] (4) Adjustment of suspension damping.

[0159] Pre-store the suspension's stiff damping reference values ​​in the controller and soft damping reference value .

[0160] Thus, after obtaining the AI-controlled change index, the adjusted damping value is obtained. The calculation formula is: .

[0161] When AI approaches 0, the adjusted damping value It is closer to the damping value corresponding to the first cluster center vector C1 of the mild style. At this time, the suspension is soft and prioritizes vibration filtering and comfort.

[0162] When AI approaches 1, the adjusted damping value The damping value is closer to the third cluster center vector C3 corresponding to the aggressive style, at which point the suspension is stiff and the handling stability is improved.

[0163] To ensure a smooth and stable transition without abrupt changes in the adjusted driving parameters (e.g., adjusted steering assist, obtained accelerator pedal torque response curve, adjusted energy recovery intensity, and / or adjusted damping value), the calculated adjusted driving parameters can be subjected to first-order hysteresis filtering to ensure a consistent and natural change in the adjusted driving parameters.

[0164] V. Data storage.

[0165] By setting up a corresponding account for the driver, when the vehicle is powered off, the corresponding driving parameters and the adjusted driving parameters obtained from the dynamic changes of the corresponding driving parameters are associated with and stored with the driver's account. This way, when the driver drives the vehicle again, he can directly retrieve the corresponding adjusted driving parameters to configure and control the vehicle, without having to go through the above complicated adjustment process again, making it convenient to use.

[0166] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0167] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0168] Based on the same inventive concept, corresponding to any of the above embodiments, this application also provides a vehicle control device.

[0169] refer to Figure 2 The device includes: The acquisition module 201 is configured to acquire driving parameters of the vehicle, wherein the driving parameters are parameters corresponding to the driver driving the vehicle; The control change index determination module 202 is configured to determine the driver's control change index based on the degree of change of the driving parameters, wherein the control change index is a characterization of the degree of change in the driver's operation of the vehicle. The adjustment control module 203 is configured to adjust the vehicle's driving parameters according to the control change index, and control the vehicle's driving using the adjusted driving parameters.

[0170] In some embodiments, the manipulation change index determination module 202 is specifically configured as follows: Determine the degree of change of the driving parameters, and transform the degree of change into a feature vector to obtain a behavior feature vector; Obtain multiple preset cluster center vectors, where each cluster center vector represents a driving control style; The variation range of the behavioral feature vector in multiple cluster center vectors is determined, and the corresponding driver's control change index is determined based on the variation range, wherein the variation range is the interval with one cluster center vector as the starting point and another cluster center vector as the ending point.

[0171] In some embodiments, the manipulation change index determination module 202 is further configured to: The steering angular velocity is extracted from the driving parameters, and the number of times the steering angular velocity exceeds a steering angular velocity threshold within a first set time period is determined. The steering angular velocity is then combined with the first number of times to determine the degree of steering change; and / or, Extract the accelerator pedal change rate and acceleration from the driving parameters, and determine when the accelerator pedal change rate is greater than the accelerator pedal change threshold and the acceleration is greater than the first acceleration threshold within a second set time period. Then, determine the accelerator pedal change degree based on the second count; and / or, The brake pedal change rate and acceleration are extracted from the driving parameters, and a third number is determined when the brake pedal change rate is greater than the brake pedal change threshold and the acceleration is less than the second acceleration threshold within a third set time period. The brake pedal change degree is determined based on the third number. The behavior feature vector is determined based on at least one of the steering change degree, the accelerator pedal change degree, and the brake pedal change degree.

[0172] In some embodiments, the manipulation change index determination module 202 is further configured to: Determine the distance between the behavioral feature vector and each cluster center vector, and determine the starting center vector and the ending center vector from multiple cluster center vectors based on the distance, and determine the change range based on the starting center vector and the ending center vector; The change coefficient is determined based on the change range, and the difference between the center vector of the endpoint and the center vector of the starting point is combined with the change coefficient to determine the undetermined control change index. Based on the undetermined control change index, the driver's control change index is determined.

[0173] In some embodiments, the manipulation change index determination module 202 is further configured to: Determine the distance between the behavioral feature vector and each cluster center vector, and determine the minimum two distances; Determine at least two target cluster center vectors corresponding to at least two minimum distances; Based on the at least two target cluster center vectors, a starting center vector and an ending center vector are determined from multiple cluster center vectors, and a change range is determined based on the starting center vector and the ending center vector.

[0174] In some embodiments, the manipulation change index determination module 202 is further configured to: The first difference is obtained by subtracting the sum of squares of the elements in the starting center vector from the sum of squares of the elements in the behavioral feature vector. The second difference is obtained by subtracting the sum of squares of the elements in the starting point center vector from the sum of squares of the elements in the ending point center vector. The ratio of the first difference to the second difference is used as the coefficient of variation; Obtain the initial value of the endpoint center vector and the initial value of the starting point center vector; subtract the initial value of the starting point from the initial value of the endpoint to obtain the third difference; multiply the change coefficient by the third difference to obtain the first product result. The first product result is added to the initial value to obtain the undetermined control change index.

[0175] In some embodiments, the manipulation change index determination module 202 is further configured to: Obtain the control change index of the previous time period, and perform a weighted summation of the control change index of the previous time period and the control change index to be determined to obtain the driver's control change index.

[0176] In some embodiments, the adjustment control module 203 is specifically configured as follows: The maximum and minimum steering assist values ​​are retrieved. The fourth difference between the maximum and minimum steering assist values ​​is multiplied by the control change index to obtain the steering assist attenuation. The maximum steering assist value is subtracted from the steering assist attenuation to obtain the adjusted steering assist. The adjusted steering assist is then used to control the vehicle's steering; and / or, The corresponding accelerator pedal torque response curve is retrieved based on the control change index, and the vehicle's accelerator pedal request is responded to based on the accelerator pedal torque response curve; and / or, Retrieve the maximum energy recovery intensity value, determine the energy recovery attenuation weight based on the control change index, multiply the maximum energy recovery intensity value by the energy recovery attenuation weight to obtain the adjusted energy recovery intensity, and use the adjusted energy recovery intensity to control the vehicle's energy recovery function; and / or, The hard damping reference value and soft damping reference value of the suspension are retrieved. The soft damping reference value is subtracted from the hard damping reference value and then multiplied by the handling change index to obtain the damping reinforcement value. The damping reinforcement value is added to the soft damping reference value to obtain the adjusted damping value. The vehicle suspension is then adjusted according to the adjusted damping value.

[0177] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0178] The apparatus of the above embodiments is used to implement the corresponding method in any of the foregoing embodiments and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0179] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the methods described in any of the above embodiments.

[0180] Figure 3 This embodiment illustrates a more specific hardware structure of an electronic device. The device may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0181] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0182] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0183] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0184] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0185] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0186] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0187] The electronic devices described above are used to implement the corresponding methods in any of the foregoing embodiments and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0188] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to perform the methods described in any of the above embodiments.

[0189] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random-access memory (SRAM), dynamic random-access memory (DRAM), other types of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital video disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0190] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to perform the methods described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0191] Based on the same concept, corresponding to any of the above embodiments, this application also provides a computer program product, including computer program instructions, which, when run on a computer, cause the computer to perform the method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0192] Based on the same inventive concept, this application also provides a vehicle including the device or electronic device described in the above embodiments. The beneficial effects of embodiments having corresponding devices or electronic devices will not be elaborated further here.

[0193] It is understood that before using the technical solutions of the various embodiments in this application, users will be informed of the type, scope of use, and usage scenarios of the personal information involved in an appropriate manner, and user authorization will be obtained.

[0194] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose, based on the prompt message, whether to provide personal information to the software or hardware such as electronic devices, applications, servers, or storage media performing the operations described in this application.

[0195] As an optional but not limited implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0196] It is understood that the above notification and user authorization process is merely illustrative and does not limit the implementation of this application. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this application.

[0197] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0198] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0199] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0200] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A vehicle control method, characterized in that, include: Obtain the driving parameters of the vehicle, wherein the driving parameters are parameters corresponding to the driver driving the vehicle; Based on the degree of change of the driving parameters, a driver's control change index is determined, wherein the control change index is a characterization of the degree of change in the driver's operation of the vehicle. Based on the aforementioned control change index, the vehicle's driving parameters are adjusted, and the adjusted driving parameters are used to control the vehicle's movement.

2. The method according to claim 1, characterized in that, Determining the driver's control change index based on the degree of change of the driving parameters includes: Determine the degree of change of the driving parameters, and transform the degree of change into a feature vector to obtain a behavior feature vector; Obtain multiple preset cluster center vectors, where each cluster center vector represents a driving control style; The variation range of the behavioral feature vector in multiple cluster center vectors is determined, and the corresponding driver's control change index is determined based on the variation range, wherein the variation range is the interval with one cluster center vector as the starting point and another cluster center vector as the ending point.

3. The method according to claim 2, characterized in that, The process of determining the degree of change of the driving parameters and transforming the degree of change into a feature vector to obtain a behavioral feature vector includes: The steering angular velocity is extracted from the driving parameters, and the number of times the steering angular velocity exceeds a steering angular velocity threshold within a first set time period is determined. The steering angular velocity is then combined with the first number of times to determine the degree of steering change; and / or, Extract the accelerator pedal change rate and acceleration from the driving parameters, and determine when the accelerator pedal change rate is greater than the accelerator pedal change threshold and the acceleration is greater than the first acceleration threshold within a second set time period. Then, determine the accelerator pedal change degree based on the second count; and / or, The brake pedal change rate and acceleration are extracted from the driving parameters, and a third number is determined when the brake pedal change rate is greater than the brake pedal change threshold and the acceleration is less than the second acceleration threshold within a third set time period. The brake pedal change degree is determined based on the third number. The behavior feature vector is determined based on at least one of the steering change degree, the accelerator pedal change degree, and the brake pedal change degree.

4. The method according to claim 2, characterized in that, The step of determining the variation range of the behavioral feature vector among multiple cluster center vectors, and determining the corresponding driver's control change index based on the variation range, includes: Determine the distance between the behavioral feature vector and each cluster center vector, and determine the starting center vector and the ending center vector from multiple cluster center vectors based on the distance, and determine the change range based on the starting center vector and the ending center vector; The change coefficient is determined based on the change range, and the difference between the center vector of the endpoint and the center vector of the starting point is combined with the change coefficient to determine the undetermined control change index. Based on the undetermined control change index, the driver's control change index is determined.

5. The method according to claim 4, characterized in that, The process of determining the distance between the behavioral feature vector and each cluster center vector, determining the starting center vector and the ending center vector from multiple cluster center vectors based on the distance, and determining the variation range based on the starting center vector and the ending center vector includes: Determine the distance between the behavioral feature vector and each cluster center vector, and determine the minimum two distances; Determine at least two target cluster center vectors corresponding to at least two minimum distances; Based on the at least two target cluster center vectors, a starting center vector and an ending center vector are determined from multiple cluster center vectors, and a change range is determined based on the starting center vector and the ending center vector.

6. The method according to claim 4, characterized in that, The step of determining the change coefficient based on the change range, and combining the difference between the endpoint center vector and the starting point center vector with the change coefficient to determine the undetermined control change index includes: The first difference is obtained by subtracting the sum of squares of the elements in the starting center vector from the sum of squares of the elements in the behavioral feature vector. The second difference is obtained by subtracting the sum of squares of the elements in the starting point center vector from the sum of squares of the elements in the ending point center vector. The ratio of the first difference to the second difference is used as the coefficient of variation; Obtain the initial value of the endpoint center vector and the initial value of the starting point center vector; subtract the initial value of the starting point from the initial value of the endpoint to obtain the third difference; multiply the change coefficient by the third difference to obtain the first product result. The first product result is added to the initial value to obtain the undetermined control change index.

7. The method according to claim 4, characterized in that, Determining the driver's handling change index based on the undetermined handling change index includes: Obtain the control change index of the previous time period, and perform a weighted summation of the control change index of the previous time period and the control change index to be determined to obtain the driver's control change index.

8. The method according to claim 1, characterized in that, The step of adjusting the vehicle's driving parameters according to the control change index and controlling the vehicle's driving using the adjusted driving parameters includes: The maximum and minimum steering assist values ​​are retrieved. The fourth difference between the maximum and minimum steering assist values ​​is multiplied by the control change index to obtain the steering assist attenuation. The maximum steering assist value is subtracted from the steering assist attenuation to obtain the adjusted steering assist. The adjusted steering assist is then used to control the vehicle's steering; and / or, The corresponding accelerator pedal torque response curve is retrieved based on the control change index, and the vehicle's accelerator pedal request is responded to based on the accelerator pedal torque response curve; and / or, Retrieve the maximum energy recovery intensity value, determine the energy recovery attenuation weight based on the control change index, multiply the maximum energy recovery intensity value by the energy recovery attenuation weight to obtain the adjusted energy recovery intensity, and use the adjusted energy recovery intensity to control the vehicle's energy recovery function; and / or, The hard damping reference value and soft damping reference value of the suspension are retrieved. The soft damping reference value is subtracted from the hard damping reference value and then multiplied by the handling change index to obtain the damping reinforcement value. The damping reinforcement value is added to the soft damping reference value to obtain the adjusted damping value. The vehicle suspension is then adjusted according to the adjusted damping value.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 8.

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

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