Vehicle transverse acceleration calibration method and device, storage medium and vehicle

Abstract

The invention relates to a vehicle transverse acceleration calibration method and device, a storage medium and a vehicle, and the method comprises the steps: obtaining the current driving environment information of the vehicle and the current speed of the vehicle; under the condition that the driving environment information meets a calibration condition, determining a target correction parameter according to the current vehicle speed; and on the basis of the target correction parameter and the current transverse acceleration of the vehicle, the calibrated transverse acceleration of the vehicle is determined. By means of the technical scheme, automatic calibration of the transverse acceleration of the vehicle is achieved, the transverse acceleration of the vehicle can be automatically calibrated when the current driving environment information meets the calibration condition, transverse acceleration deviation caused by actual manufacturing deviation of the vehicle is automatically compensated, the accuracy of the transverse acceleration is improved, and the driving safety is improved. Therefore, the accuracy degree and the stability of transverse control of the vehicle are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle control technology, and in particular to a method, device, storage medium and vehicle for calibrating vehicle lateral acceleration. Background Art

[0002] When the vehicle is performing lateral control, it needs to refer to the lateral acceleration for control calculation. The lateral acceleration value output by the current vehicle is obtained by converting the IMU measurement value after comparing the installation position of the Inertial Measurement Unit (IMU) with the center of mass position of the vehicle. Due to the physical manufacturing deviation of the vehicle, the lateral acceleration calculated by the IMU will have different deviations on different vehicles, resulting in inaccurate lateral acceleration information and affecting the accuracy of intelligent driving lateral control, thereby bringing adverse effects on intelligent driving lateral control. Summary of the invention

[0003] In order to solve the above technical problems or at least partially solve the above technical problems, at least one embodiment of the present disclosure provides a vehicle lateral acceleration calibration method, device, storage medium and vehicle.

[0004] In a first aspect, the present disclosure provides a method for calibrating a vehicle lateral acceleration, comprising:

[0005] Acquiring the current driving environment information of the vehicle and the current speed of the vehicle;

[0006] When the driving environment information satisfies a calibration condition, determining a target correction parameter according to the current vehicle speed;

[0007] Based on the target correction parameter and a current lateral acceleration of the vehicle, a calibrated lateral acceleration of the vehicle is determined.

[0008] In a second aspect, the present disclosure provides a device for calibrating vehicle lateral acceleration, comprising:

[0009] A data acquisition module, used to acquire the current driving environment information of the vehicle and the current speed of the vehicle;

[0010] a parameter determination module, configured to determine a target correction parameter according to the current vehicle speed when the driving environment information satisfies a calibration condition;

[0011] A calibration module is configured to determine a calibrated lateral acceleration of the vehicle based on the target correction parameter and a current lateral acceleration of the vehicle.

[0012] In a third aspect, the present disclosure provides an electronic device, including: a processor and a memory;

[0013] The processor is used to execute the vehicle lateral acceleration calibration method as described in the first aspect by calling the program or instruction stored in the memory.

[0014] In a fourth aspect, the present disclosure provides a computer-readable storage medium, wherein the computer-readable storage medium stores a program or instruction, wherein the program or instruction enables a computer to execute the vehicle lateral acceleration calibration method as described in the first aspect.

[0015] In a fifth aspect, the present disclosure provides a computer program product, wherein the computer program product is used to execute the vehicle lateral acceleration calibration method as described in the first aspect.

[0016] In a sixth aspect, the present disclosure provides a vehicle, comprising a calibration device for vehicle lateral acceleration as described in the second aspect, or an electronic device as described in the third aspect, or a computer-readable storage medium as described in the fourth aspect, or a computer program product as described in the fifth aspect.

[0017] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has at least the following advantages:

[0018] In the disclosed embodiment, the current driving environment information and the current speed of the vehicle are obtained. When the driving environment information meets the calibration conditions, the target correction parameter is determined according to the current speed, and then the calibrated lateral acceleration of the vehicle is determined based on the target correction parameter and the current lateral acceleration of the vehicle. By adopting the above technical solution, by obtaining the target correction parameter matching the current speed, the current lateral acceleration is corrected by using the target correction parameter to obtain the calibrated lateral acceleration, thereby realizing the automatic calibration of the lateral acceleration of the vehicle, being able to automatically calibrate the lateral acceleration of the vehicle when the current driving environment information meets the calibration conditions, automatically compensating the lateral acceleration deviation caused by the actual manufacturing deviation of the vehicle, improving the accuracy of the lateral acceleration, thereby improving the accuracy and stability of the lateral control of the vehicle, and by determining the corresponding correction parameter for calibrating the lateral acceleration of the vehicle according to the speed, it is possible to select the appropriate correction parameter for calibrating the lateral acceleration according to the speed in real time during the driving process of the vehicle, further ensuring the accuracy of the lateral control. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1 A schematic flow chart of a method for calibrating vehicle lateral acceleration provided by an exemplary embodiment of the present disclosure;

[0022] Figure 2 A schematic diagram of a flow chart for determining a correspondence between different speed intervals and correction parameters provided by an exemplary embodiment of the present disclosure;

[0023] Figure 3 A schematic flow chart of a method for calibrating vehicle lateral acceleration provided by another exemplary embodiment of the present disclosure;

[0024] Figure 4 A schematic diagram of the structure of a vehicle lateral acceleration calibration device provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0025] In order to more clearly understand the above-mentioned purposes, features and advantages of the present disclosure, the present disclosure is further described in detail below in conjunction with the accompanying drawings and embodiments. It is understandable that the described embodiments are part of the embodiments of the present disclosure, rather than all of the embodiments, and the specific embodiments described herein are only used to explain the present disclosure, rather than to limit the present disclosure. In the absence of conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other. Based on the described embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in the field belong to the scope of protection of the present disclosure.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0027] Intelligent driving can calculate many useful control dependencies through lateral acceleration conversion, such as the vehicle's motion posture in the lane, centripetal force when turning, and the estimation of the road slope. Therefore, the accuracy of lateral acceleration will directly affect the calculation results of the lateral control host computer. In order to ensure the accuracy of vehicle lateral control, it is necessary to improve the accuracy of lateral acceleration.

[0028] The lateral acceleration value currently output by the whole vehicle is obtained by converting the IMU measurement value after comparing the IMU installation position with the center of mass position of the whole vehicle. In view of the problem in the prior art that the lateral acceleration information is inaccurate due to the manufacturing deviation of the whole vehicle, which in turn affects the lateral control accuracy of intelligent driving, the present disclosure provides a solution for intelligent driving to increase the lateral acceleration self-calibration mechanism, compare the road information perceived and recognized by the vehicle with the motion information measured by the sensor, and combine the whole vehicle information to perform self-learning compensation for the lateral acceleration in the autonomous driving domain controller (ADCU) to improve the accuracy of lateral control. This solution can automatically compensate for the lateral acceleration deviation caused by the actual manufacturing deviation of the vehicle under appropriate conditions, and improve the accuracy and stability of the lateral control of the vehicle's intelligent driving.

[0029] The specific implementation methods, devices, storage media and vehicles of the vehicle lateral acceleration calibration method disclosed in the present invention are explained in detail below with reference to the accompanying drawings.

[0030] Figure 1 A flow chart of a method for calibrating vehicle lateral acceleration provided for an exemplary embodiment of the present disclosure is provided. The method can be executed by a calibration device for vehicle lateral acceleration provided by an embodiment of the present disclosure. The calibration device for vehicle lateral acceleration can be implemented using software and / or hardware and can be integrated in a vehicle, specifically in an ADCU of the vehicle.

[0031] like Figure 1 As shown, the vehicle lateral acceleration calibration method may include the following steps:

[0032] Step 101, obtaining the current driving environment information of the vehicle and the current speed of the vehicle.

[0033] Among them, the driving environment information may include but is not limited to the lane radius, the cross slope angle, the current centripetal acceleration of the vehicle and the current lateral acceleration calculated value of the road where the vehicle is currently located. The lane radius can be determined by taking the inverse of the lane line curvature sensed input. The perception and acquisition of the lane line curvature is a prior art and will not be described in detail here. The cross slope angle (i.e., the lateral slope) of the road can be determined according to the ratio of the lateral height difference of the road divided by the road width. The current centripetal acceleration of the vehicle can be determined according to the current speed of the vehicle and the lane radius, that is, the current centripetal acceleration is equal to the square of the current speed divided by the lane radius. The current lateral acceleration calculated value can be determined according to the current speed of the vehicle and the current yaw angular velocity of the vehicle, that is, the current lateral acceleration calculated value is equal to the product of the current speed and the yaw angular velocity. Among them, the current speed of the vehicle can be obtained by collecting the speed sensor installed at the wheel end of the vehicle, and the yaw angular velocity can be obtained by collecting the yaw angular velocity sensor (Yaw-G sensor) installed on the vehicle.

[0034] In the disclosed embodiment, the driving environment information and current vehicle speed of the vehicle may be collected in real time or periodically during the driving of the vehicle.

[0035] Step 102: When the driving environment information meets the calibration conditions, a target correction parameter is determined according to the current vehicle speed.

[0036] Among them, the setting of calibration conditions is related to driving environment information.

[0037] Exemplarily, the driving environment information includes the lane radius, the cross slope angle, the current centripetal acceleration and the current lateral acceleration calculated value of the vehicle on the road where the vehicle is currently located. Correspondingly, the calibration conditions include: the curve radius interval, the cross slope angle threshold and the preset values ​​related to the acceleration difference, wherein the curve radius interval can be set to (150, 1800) m, for example. Thus, the driving environment information satisfies the calibration conditions including: the lane radius in the driving environment information is within the preset curve radius interval, and the cross slope angle in the driving environment information is less than the preset cross slope angle threshold (indicating that the current road has no cross slope), and the absolute value of the difference between the current centripetal acceleration and the current lateral acceleration calculated value in the driving environment information is less than the first preset value (indicating that the vehicle is well centered in the lane).

[0038] In this embodiment, when the current driving environment information of the vehicle meets the calibration conditions, the self-learning calibration function of the lateral acceleration is triggered, and the target correction parameter is determined according to the current speed of the vehicle to calibrate the lateral acceleration collected by the vehicle's IMU sensor.

[0039] As an example, a centripetal acceleration can be calculated based on the current vehicle speed and lane radius, and the centripetal acceleration is used as the theoretical lateral acceleration and the lateral acceleration collected by the IMU sensor at different times to form multiple data points for polynomial fitting, and the fitted polynomial parameters are determined as the target correction parameters corresponding to the current vehicle speed.

[0040] As another example, lateral acceleration data may be collected in advance for different vehicle speeds to perform polynomial fitting, obtain the correspondence between different vehicle speeds or different speed ranges and the correction parameters obtained by fitting, and determine the target correction parameter corresponding to the current vehicle speed by querying the correspondence.

[0041] Step 103 : determining a calibrated lateral acceleration of the vehicle based on the target correction parameter and the current lateral acceleration of the vehicle.

[0042] In the disclosed embodiment, after the target correction parameter is determined, the target correction parameter can be used to compensate for the current lateral acceleration of the vehicle to obtain a calibrated lateral acceleration, and then the vehicle is laterally controlled based on the calibrated lateral acceleration to ensure the accuracy of the lateral control and reduce the impact of vehicle manufacturing deviations.

[0043] As an example, the current lateral acceleration of the vehicle may be a lateral acceleration measurement value output by an IMU sensor installed on the vehicle.

[0044] As another example, in order to ensure the smoothness and real-time performance of the lateral acceleration measurement value, the lateral acceleration measurement value output by the IMU sensor can also be subjected to first-order filtering, and the first-order differential equation y(n)=k*x(n)+(1-k)*y(n-1) is used to determine the processed lateral acceleration as the current lateral acceleration of the vehicle. Among them, x(n) represents the lateral acceleration measurement value output by the IMU sensor in real time, y(n) represents the processed lateral acceleration obtained by the first-order filtering, y(n-1) represents the lateral acceleration obtained by the last first-order filtering, and k is a constant, and the value range of k is (0, 1).

[0045] The method for calibrating the lateral acceleration of a vehicle of the disclosed embodiment obtains the current driving environment information of the vehicle and the current speed of the vehicle, and when the driving environment information meets the calibration conditions, determines the target correction parameter according to the current speed, and then determines the calibrated lateral acceleration of the vehicle based on the target correction parameter and the current lateral acceleration of the vehicle. By adopting the above technical solution, by obtaining the target correction parameter matching the current speed, the current lateral acceleration is corrected by using the target correction parameter to obtain the calibrated lateral acceleration, thereby realizing the automatic calibration of the lateral acceleration of the vehicle, being able to automatically calibrate the lateral acceleration of the vehicle when the current driving environment information meets the calibration conditions, automatically compensating the lateral acceleration deviation caused by the actual manufacturing deviation of the vehicle, improving the accuracy of the lateral acceleration, thereby improving the accuracy and stability of the lateral control of the vehicle, and by determining the corresponding correction parameter according to the speed for calibrating the lateral acceleration of the vehicle, being able to select the appropriate correction parameter according to the speed in real time during the driving process of the vehicle to calibrate the lateral acceleration, further ensuring the accuracy of the lateral control.

[0046] In an optional implementation of the present disclosure, when determining the target correction parameter according to the current vehicle speed, the corresponding relationship between different speed intervals and the correction parameter can be obtained first, and then the target speed interval to which the current vehicle speed belongs can be determined according to the current vehicle speed, and then the corresponding relationship between different speed intervals and the correction parameter can be queried to determine the target correction parameter corresponding to the target speed interval, thereby obtaining the target correction parameter corresponding to the current vehicle speed. By pre-fitting and determining the corresponding correction parameters for different speed intervals, the appropriate correction parameter can be directly determined according to the vehicle speed in the actual driving scene, without the need to fit and determine the correction parameter, thereby ensuring the timeliness of the lateral acceleration calibration; and, fitting the corresponding correction parameter for the speed interval reduces the processing volume consumed by fitting compared to fitting for each vehicle speed separately, and also saves time costs.

[0047] Further, in an optional implementation of the present disclosure, the correspondence between different speed intervals and correction parameters can be obtained by: Figure 2 The embodiment shown determines that Figure 2 As shown, the method of determining the corresponding relationship may include the following steps:

[0048] Step 201 : for a current speed range, obtaining theoretical lateral acceleration and processed lateral acceleration corresponding to different vehicle speeds in the current speed range.

[0049] In the disclosed embodiment, different speed intervals may be preset according to actual needs, and data may be collected for each speed interval to obtain theoretical lateral accelerations and processed lateral accelerations corresponding to different vehicle speeds in the speed interval.

[0050] In an optional embodiment of the present disclosure, for the current speed range, when obtaining the theoretical lateral acceleration corresponding to different vehicle speeds in the current speed range, the vehicle speed, yaw rate and lane radius of the road where the data collection vehicle is located can be obtained, wherein the obtained vehicle speed belongs to the current speed range, the data collection vehicle can be one or more vehicles, the vehicle speed can be obtained by collecting the speed sensor installed on the wheel end of the data collection vehicle, the yaw rate can be obtained by collecting the yaw rate sensor installed on the data collection vehicle, and the lane radius can be determined by taking the inverse of the lane line curvature sensed input, and the perception and acquisition of the lane line curvature is a prior art and will not be described here. Next, the centripetal acceleration of the data collection vehicle is determined according to the vehicle speed and the lane radius, that is, the centripetal acceleration is equal to the ratio of the square of the vehicle speed divided by the lane radius; and the calculated lateral acceleration value of the data collection vehicle is determined according to the vehicle speed and the yaw rate, that is, the calculated lateral acceleration value is equal to the product of the vehicle speed and the yaw rate. Next, the absolute value of the difference between the centripetal acceleration and the lateral acceleration is calculated, and the absolute value is compared with a preset value (referred to as the second preset value for ease of description and distinction); if the absolute value of the difference between the calculated values ​​of the centripetal acceleration and the lateral acceleration is not greater than the second preset value, the centripetal acceleration is determined as the theoretical lateral acceleration when the data collection vehicle is traveling at the corresponding speed; if the absolute value of the difference between the calculated values ​​of the centripetal acceleration and the lateral acceleration is greater than the second preset value, the calculated value of the lateral acceleration is determined as the theoretical lateral acceleration when the data collection vehicle is traveling at the corresponding speed.

[0051] The second preset value can be measured experimentally. Experimental tests show that when the offset and heading angle of the vehicle in the lane are both small enough, it means that the vehicle is well centered in the lane with a small error. At this time, the calculated centripetal acceleration and lateral acceleration of the vehicle are relatively close. The absolute value of the difference between the centripetal acceleration and the lateral acceleration can be calculated, and the second preset value can be determined based on the absolute value. For example, the absolute value can be directly used as the second preset value, or a number slightly larger than the absolute value can be used as the second preset value.

[0052] In this embodiment, the centripetal acceleration and lateral acceleration at different speeds are calculated, and the absolute value of the difference between the two is compared with a second preset value to determine the theoretical lateral acceleration at different speeds. This can ensure the accuracy of the determined theoretical lateral acceleration, thereby helping to ensure the accuracy of the fitting results and making the determined correction parameters more accurate.

[0053] In this embodiment, when obtaining the processed lateral acceleration corresponding to different vehicle speeds in the current speed range, the lateral acceleration collected by the IMU sensor installed on the data acquisition vehicle can be used as the processed lateral acceleration for fitting, or the lateral acceleration collected by the IMU sensor can be subjected to first-order filtering, and the lateral acceleration obtained by processing the first-order differential equation can be used as the processed lateral acceleration to ensure the smoothness and real-time performance of the obtained processed lateral acceleration.

[0054] It should be noted that, in this embodiment, in order to obtain sufficient data for curve fitting, the data collection vehicle can be made to travel at a certain speed for a period of time, and the yaw angular velocity and lane radius at different times at the speed can be collected to determine the theoretical lateral acceleration at different times at the same speed, and the processed lateral acceleration at the corresponding time can be obtained at the same time. The theoretical lateral acceleration and the processed lateral acceleration corresponding to the same time are used to form data points for curve fitting to determine the correction parameters corresponding to each speed interval.

[0055] Step 202: Generate multiple data points based on the theoretical lateral acceleration and the processed lateral acceleration corresponding to different vehicle speeds, wherein one data point consists of one theoretical lateral acceleration and one processed lateral acceleration.

[0056] In this embodiment, for the current speed interval, the theoretical lateral acceleration and processed lateral acceleration corresponding to each vehicle speed in the current speed interval can be used to form a data point. When forming the data point, if only one theoretical lateral acceleration and one processed lateral acceleration are obtained for one vehicle speed, one data point is constructed for one vehicle speed, thereby obtaining multiple data points in the speed interval; if the theoretical lateral acceleration and processed lateral acceleration at different times are obtained for one vehicle speed, one data point is constructed using the theoretical lateral acceleration and processed lateral acceleration at the same time, thereby obtaining multiple data points for one vehicle speed, thereby obtaining multiple data points in the speed interval.

[0057] Step 203, performing a polynomial fitting based on multiple data points to obtain correction parameters corresponding to the current speed range.

[0058] In this embodiment, after obtaining a plurality of data points of the current speed interval, a polynomial fitting may be performed using the plurality of data points to obtain correction parameters corresponding to the current speed interval.

[0059] Among them, a polynomial can be expressed as a 0 =ξ*A+β, A represents the processed lateral acceleration in the data point, a 0 represents the theoretical lateral acceleration, ξ and β are the correction parameters that need to be determined by fitting. A polynomial fit is performed based on multiple data points to calculate A and a0 The values ​​of the correction parameters ξ and β can be obtained by the fitting relationship.

[0060] It should be noted that the embodiment of the present disclosure uses a first-order polynomial fitting to determine the correction parameters for only an example and is not intended to limit the present disclosure. Depending on the requirements for lateral acceleration control accuracy, a second-order polynomial, a third-order polynomial, etc. may be used for fitting to obtain corresponding correction parameters.

[0061] In an optional implementation of the present disclosure, in order to ensure the accuracy of the determined correction parameters and thus improve the accuracy of the lateral control, the fitting result can be evaluated. When the evaluation shows that the obtained correction parameters meet the fitting completion condition, the correction parameters are determined as the final correction parameters corresponding to the speed interval for automatic calibration of the lateral acceleration. Specifically, a polynomial fitting can be performed based on multiple data points to obtain the initial correction parameters corresponding to the current speed interval, and test data points corresponding to multiple moments within a preset continuous time are obtained, and a test data point consists of a theoretical test lateral acceleration and a processed test lateral acceleration; then, based on the initial correction parameters and the processed test lateral acceleration corresponding to each moment, the lateral acceleration prediction value corresponding to each moment is determined. It can be understood that the initial correction parameters are obtained by fitting a polynomial, so the fitting relationship of the lateral acceleration can be determined using the initial correction parameters, and the fitting relationship is also a linear polynomial. Then, the processed test lateral accelerations at different moments are substituted into the fitting relationship for calculation, and the lateral acceleration prediction value corresponding to each moment can be obtained. Next, based on the theoretical test lateral acceleration and lateral acceleration predicted value corresponding to each moment, the evaluation index result corresponding to each moment is determined according to the preset evaluation index. When the evaluation index result corresponding to each moment within the preset continuous time length meets the fitting completion conditions, the initial correction parameter is determined as the correction parameter corresponding to the current speed range.

[0062] Among them, the evaluation indicators may include but are not limited to the sum of squares due to error (SSE), the coefficient of determination (R-Square), and the root mean square error (RMSE). The closer the SSE is to 0, the better the fit; R-Square calculates the square value of the correlation coefficient between the theoretical test lateral acceleration and the lateral acceleration prediction value at the same time, and the closer it is to 1, the better the fit; the closer the RMSE is to 0, the better the fit. Based on this, for example, the fitting completion conditions can be preset as SSE not greater than 0.1, RMSE not greater than 0.05, and R-Square not less than 0.95.

[0063] In this embodiment, according to the calculation method of each evaluation index, the evaluation index result corresponding to each moment can be determined, and then each evaluation index result is compared with the preset fitting completion condition. If the evaluation index result corresponding to each moment within the preset continuous duration meets the fitting completion condition, it is determined that the initial correction parameter obtained by fitting has a good fitting effect and is relatively accurate. The initial correction parameter is used as the final correction parameter of the current speed interval and a corresponding relationship is established with the current speed interval. If there is at least one moment where the evaluation index result does not meet the fitting completion condition, a polynomial fitting is performed again to obtain a new correction parameter until the evaluation index result corresponding to each moment within the preset continuous duration meets the fitting completion condition. It should be noted that when there are multiple evaluation indicators, the evaluation index result corresponding to each evaluation indicator needs to be in an acceptable range before the evaluation index result at the corresponding moment is determined to meet the fitting completion condition.

[0064] In this embodiment, by acquiring test data points, the correction parameters obtained by fitting are evaluated according to preset evaluation indicators to obtain the evaluation indicator results at each moment. When the evaluation indicator results corresponding to each moment in the continuous time length meet the fitting completion conditions, the correction parameters at this time are determined as the final correction parameters of the current speed range. In this way, the accuracy of the determined correction parameters is guaranteed, providing data support for achieving accurate lateral acceleration calibration.

[0065] Step 204: construct a correspondence between different speed intervals and correction parameters according to each speed interval and its corresponding correction parameter.

[0066] In this embodiment, after the correction parameter corresponding to each speed interval is obtained, the corresponding relationship between different speed intervals and the correction parameter can be constructed according to each speed interval and its corresponding correction parameter.

[0067] The vehicle lateral acceleration calibration method of the disclosed embodiment obtains, for a current speed range, theoretical lateral accelerations and processed lateral accelerations corresponding to different vehicle speeds in the current speed range, and generates multiple data points based on the theoretical lateral accelerations and processed lateral accelerations corresponding to different vehicle speeds, wherein one data point consists of a theoretical lateral acceleration and a processed lateral acceleration, and then performs a polynomial fitting based on the multiple data points to obtain correction parameters corresponding to the current speed range, and finally constructs a corresponding relationship between different speed ranges and correction parameters based on each speed range and its corresponding correction parameter, thereby completing the determination of the correction parameters for different speed ranges and constructing the corresponding relationship, providing data support for lateral acceleration self-learning calibration, and ensuring the timeliness of lateral acceleration calibration.

[0068] In an optional embodiment of the present disclosure, Figure 3As shown, based on the above embodiment, step 103 may include the following sub-steps:

[0069] Step 301, obtaining a lateral acceleration measurement value of the vehicle.

[0070] For example, the lateral acceleration collected by an IMU sensor installed on the vehicle may be obtained as the lateral acceleration measurement value.

[0071] Step 302: Perform first-order filtering on the lateral acceleration measurement value to obtain the current lateral acceleration of the vehicle.

[0072] In this embodiment, in order to ensure the smoothness and real-time performance of the lateral acceleration, the lateral acceleration measurement value can be subjected to first-order filtering. The lateral acceleration measurement value is subjected to first-order filtering using the first-order differential equation y(n)=k*x(n)+(1-k)*y(n-1), and the obtained y(n) is used as the current lateral acceleration of the vehicle.

[0073] Step 303: construct a fitting relationship of the lateral acceleration based on the target correction parameter and a preset polynomial.

[0074] The preset polynomial may be a first-order polynomial, a second-order polynomial, etc., and needs to be consistent with the polynomial used when fitting the correction parameters. For example, when fitting the correction parameters of each speed interval, a first-order polynomial is used for fitting, and the preset polynomial here is also a first-order polynomial.

[0075] In this embodiment, based on the determined target correction parameter and the preset polynomial, a fitting relationship of the lateral acceleration can be constructed.

[0076] For example, assuming that the preset polynomial is y=ξ*x+β, and the determined target correction parameters are ξ=1.12 and β=0.26, the constructed fitting relationship of the lateral acceleration is y=1.12*x+0.26.

[0077] Step 304: input the current lateral acceleration into the fitting equation for calculation to obtain the calibrated lateral acceleration.

[0078] In this embodiment, after the fitting relationship of the lateral acceleration is determined, the current lateral acceleration obtained can be substituted into the fitting relationship as the value of x for calculation, and the result y obtained by the calculation is the calibrated lateral acceleration.

[0079] The vehicle lateral acceleration calibration method of the present embodiment obtains the lateral acceleration measurement value of the vehicle, performs first-order filtering on the lateral acceleration measurement value to obtain the current lateral acceleration of the vehicle, and constructs a fitting relationship of the lateral acceleration based on the target correction parameter and a preset polynomial, and then inputs the current lateral acceleration into the fitting relationship for calculation to obtain the calibrated lateral acceleration. Therefore, the current lateral acceleration of the vehicle is obtained by performing first-order filtering on the lateral acceleration measurement value, which can ensure the smoothness and real-time performance of the obtained current lateral acceleration, thereby helping to ensure the smoothness and accuracy of the calibrated lateral acceleration and the accuracy of lateral control.

[0080] In order to implement the above embodiments, the present disclosure further provides a calibration device for vehicle lateral acceleration, which can be implemented by software and / or hardware and can be integrated in a vehicle.

[0081] Figure 4 A schematic diagram of the structure of a vehicle lateral acceleration calibration device provided by an embodiment of the present disclosure, as shown in FIG. Figure 4 As shown, the vehicle lateral acceleration calibration device 40 may include: a data acquisition module 410 , a parameter determination module 420 and a calibration module 430 .

[0082] The data acquisition module 410 is used to acquire the current driving environment information of the vehicle and the current speed of the vehicle;

[0083] A parameter determination module 420, configured to determine a target correction parameter according to the current vehicle speed when the driving environment information satisfies a calibration condition;

[0084] The calibration module 430 is configured to determine a calibrated lateral acceleration of the vehicle based on the target correction parameter and the current lateral acceleration of the vehicle.

[0085] Optionally, the driving environment information includes a lane radius, a cross slope angle, a current centripetal acceleration and a current lateral acceleration calculated value of the vehicle on the road where the vehicle is currently located;

[0086] The driving environment information satisfies the calibration conditions, including:

[0087] The lane radius is within a preset curve radius range; and the cross slope angle is less than a preset cross slope angle threshold; and the absolute value of the difference between the current centripetal acceleration and the current lateral acceleration calculated value is less than a first preset value.

[0088] Optionally, the parameter determination module 420 is further configured to:

[0089] Obtain the corresponding relationship between different speed ranges and correction parameters;

[0090] Determining, according to the current vehicle speed, a target speed interval to which the current vehicle speed belongs;

[0091] The corresponding relationship is queried to determine the target correction parameter corresponding to the target speed range.

[0092] Further optionally, the vehicle lateral acceleration calibration device 40 further includes:

[0093] An acceleration acquisition module, for acquiring, for a current speed interval, theoretical lateral accelerations and processed lateral accelerations corresponding to different vehicle speeds in the current speed interval;

[0094] A data point construction module, used for generating a plurality of data points based on the theoretical lateral acceleration and the processed lateral acceleration corresponding to the different vehicle speeds, wherein one data point is composed of one theoretical lateral acceleration and one processed lateral acceleration;

[0095] A fitting module, used for performing a polynomial fitting based on the multiple data points to obtain a correction parameter corresponding to the current speed interval;

[0096] The relationship building module is used to build the corresponding relationship according to each speed interval and its corresponding correction parameter.

[0097] Further optionally, the acceleration acquisition module is further used for:

[0098] Acquiring a vehicle speed, a yaw angular velocity, and a lane radius of a road of a data collection vehicle, wherein the vehicle speed belongs to the current speed range;

[0099] Determining the centripetal acceleration of the data collection vehicle according to the vehicle speed and the lane radius;

[0100] Determining a calculated value of the lateral acceleration of the data collection vehicle according to the vehicle speed and the yaw rate;

[0101] When the absolute value of the difference between the calculated value of the centripetal acceleration and the calculated value of the lateral acceleration is not greater than a second preset value, determining the centripetal acceleration as a theoretical lateral acceleration when the data collection vehicle is traveling at the vehicle speed;

[0102] When the absolute value of the difference between the centripetal acceleration and the calculated lateral acceleration is greater than the second preset value, the calculated lateral acceleration is determined as a theoretical lateral acceleration when the data collection vehicle travels at the vehicle speed.

[0103] Optionally, the fitting module is further used to:

[0104] Performing a polynomial fitting based on the multiple data points to obtain initial correction parameters corresponding to the current speed range;

[0105] Acquire test data points corresponding to a plurality of moments within a preset continuous time period, wherein one test data point is composed of a theoretical test lateral acceleration and a processed test lateral acceleration;

[0106] Determine the predicted lateral acceleration value corresponding to each moment based on the initial correction parameter and the processed test lateral acceleration corresponding to each moment;

[0107] Based on the theoretical test lateral acceleration and the lateral acceleration prediction value corresponding to each moment, determining the evaluation index result corresponding to each moment according to the preset evaluation index;

[0108] When the evaluation index results corresponding to each moment within the preset continuous time length all meet the fitting completion condition, the initial correction parameter is determined as the correction parameter corresponding to the current speed interval.

[0109] Optionally, the calibration module 430 is further configured to:

[0110] A data acquisition module, used to acquire the current driving environment information of the vehicle and the current speed of the vehicle;

[0111] a parameter determination module, configured to determine a target correction parameter according to the current vehicle speed when the driving environment information satisfies a calibration condition;

[0112] A calibration module is configured to determine a calibrated lateral acceleration of the vehicle based on the target correction parameter and a current lateral acceleration of the vehicle.

[0113] The vehicle lateral acceleration calibration device provided in the embodiment of the present disclosure can be applied to the vehicle, can execute the vehicle lateral acceleration calibration method provided in the embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method. For the contents not described in detail in the embodiment of the device of the present disclosure, reference can be made to the description in any method embodiment of the present disclosure.

[0114] The disclosed embodiment also provides an electronic device, including a processor and a memory; the processor calls the program or instructions stored in the memory to execute the steps of each embodiment of the vehicle lateral acceleration calibration method as described above, which will not be repeated here to avoid repeated description.

[0115] The embodiments of the present disclosure also provide a computer-readable storage medium, which is non-transitory and stores programs or instructions. The programs or instructions enable a computer to execute the steps of the embodiments of the vehicle lateral acceleration calibration method as described above. To avoid repeated description, they are not repeated here.

[0116] The embodiments of the present disclosure also provide a computer program product, which is used to execute the steps of the various embodiments of the vehicle lateral acceleration calibration method as described above.

[0117] The embodiments of the present disclosure also provide a vehicle, comprising the calibration device for vehicle lateral acceleration described in the embodiments of the present disclosure, or the electronic device described in the embodiments of the present disclosure, or the computer-readable storage medium described in the embodiments of the present disclosure, or the computer program product described in the embodiments of the present disclosure.

[0118] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0119] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for calibrating vehicle lateral acceleration, characterized in that: The method comprises: Acquiring the current driving environment information of the vehicle and the current speed of the vehicle; When the driving environment information satisfies a calibration condition, determining a target correction parameter according to the current vehicle speed; Based on the target correction parameter and a current lateral acceleration of the vehicle, a calibrated lateral acceleration of the vehicle is determined.

2. The method according to claim 1, characterized in that The driving environment information includes the lane radius, the cross slope angle, the current centripetal acceleration and the current lateral acceleration calculated value of the vehicle; The driving environment information satisfies the calibration conditions, including: The lane radius is within a preset curve radius range; Furthermore, the transverse slope angle is less than a preset transverse slope angle threshold; Furthermore, an absolute value of a difference between the current centripetal acceleration and the current calculated lateral acceleration is smaller than a first preset value.

3. The method according to claim 1, characterized in that The determining of the target correction parameter according to the current vehicle speed includes: Obtain the corresponding relationship between different speed ranges and correction parameters; Determining, according to the current vehicle speed, a target speed interval to which the current vehicle speed belongs; The corresponding relationship is queried to determine the target correction parameter corresponding to the target speed range.

4. The method according to claim 3, characterized in that The corresponding relationship is obtained in the following way: For a current speed range, obtaining theoretical lateral accelerations and processed lateral accelerations corresponding to different vehicle speeds in the current speed range; Based on the theoretical lateral accelerations and the processed lateral accelerations corresponding to the different vehicle speeds, a plurality of data points are generated, wherein one of the data points is composed of one of the theoretical lateral accelerations and one of the processed lateral accelerations; Performing a polynomial fitting based on the multiple data points to obtain a correction parameter corresponding to the current speed range; The corresponding relationship is constructed according to each speed interval and its corresponding correction parameter.

5. The method according to claim 4, characterized in that Obtaining theoretical lateral accelerations corresponding to different vehicle speeds in the current speed range, including: Acquiring a vehicle speed, a yaw angular velocity, and a lane radius of a road of a data collection vehicle, wherein the vehicle speed belongs to the current speed range; Determining the centripetal acceleration of the data collection vehicle according to the vehicle speed and the lane radius; Determining a calculated value of the lateral acceleration of the data collection vehicle according to the vehicle speed and the yaw rate; When the absolute value of the difference between the calculated value of the centripetal acceleration and the calculated value of the lateral acceleration is not greater than a second preset value, determining the centripetal acceleration as a theoretical lateral acceleration when the data collection vehicle is traveling at the vehicle speed; When the absolute value of the difference between the centripetal acceleration and the calculated lateral acceleration is greater than the second preset value, the calculated lateral acceleration is determined as a theoretical lateral acceleration when the data collection vehicle travels at the vehicle speed.

6. The method according to claim 4, characterized in that The performing a polynomial fitting based on the multiple data points to obtain the correction parameter corresponding to the current speed range includes: Performing a polynomial fitting based on the multiple data points to obtain initial correction parameters corresponding to the current speed range; Acquire test data points corresponding to a plurality of moments within a preset continuous time period, wherein one test data point is composed of a theoretical test lateral acceleration and a processed test lateral acceleration; Determine the predicted lateral acceleration value corresponding to each moment based on the initial correction parameter and the processed test lateral acceleration corresponding to each moment; Based on the theoretical test lateral acceleration and the lateral acceleration prediction value corresponding to each moment, determining the evaluation index result corresponding to each moment according to the preset evaluation index; When the evaluation index results corresponding to each moment within the preset continuous time length all meet the fitting completion condition, the initial correction parameter is determined as the correction parameter corresponding to the current speed interval.

7. The method according to any one of claims 1 to 6, characterized in that: The determining, based on the target correction parameter and the current lateral acceleration of the vehicle, a calibrated lateral acceleration of the vehicle comprises: obtaining a lateral acceleration measurement of the vehicle; Performing first-order filtering on the lateral acceleration measurement value to obtain the current lateral acceleration of the vehicle; Based on the target correction parameter and a preset polynomial, a fitting relationship of the lateral acceleration is constructed; The current lateral acceleration is input into the fitting relationship to perform calculation to obtain the calibrated lateral acceleration.

8. A device for calibrating vehicle lateral acceleration, characterized in that: include: A data acquisition module, used to acquire the current driving environment information of the vehicle and the current speed of the vehicle; a parameter determination module, configured to determine a target correction parameter according to the current vehicle speed when the driving environment information satisfies a calibration condition; A calibration module is configured to determine a calibrated lateral acceleration of the vehicle based on the target correction parameter and a current lateral acceleration of the vehicle.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or an instruction, wherein the program or the instruction enables a computer to execute the vehicle lateral acceleration calibration method according to any one of claims 1 to 7.

10. A vehicle, characterized in that: The device for calibrating the vehicle lateral acceleration as claimed in claim 8 or the computer-readable storage medium as claimed in claim 9.

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