An evaluation method, device and equipment of a throttle brake calibration table and a storage medium

Abstract

The application provides an accelerator and brake calibration table evaluation method, device, equipment and storage medium, and relates to the field of automatic driving. The method comprises the following steps: obtaining effective backflow data of an automatic driving vehicle, the effective backflow data comprising speed, acceleration and accelerator and brake pedal opening degree with a corresponding relationship; dividing the speed into multiple speed intervals, and obtaining two-dimensional data corresponding to each speed interval, the two-dimensional data comprising multiple accelerations and multiple accelerator and brake pedal opening degrees corresponding to each acceleration; and according to the two-dimensional data of a target speed interval in the multiple speed intervals, evaluating a pre-stored standard accelerator and brake calibration table of the automatic driving vehicle, the standard accelerator and brake calibration table comprising standard two-dimensional data under the same speed interval as the target speed interval. The application accurately evaluates the accelerator and brake calibration table through big data, and can effectively reflect the problem of inaccurate accelerator and brake calibration table.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a method for evaluating a throttle and brake calibration table, an apparatus for evaluating a throttle and brake calibration table, a computer device, and a readable storage medium. Background Technology

[0002] Currently, throttle and brake calibration tests are only performed on a specific vehicle of a certain model, and the calibrated result serves as the standard throttle and brake calibration table. However, due to differences in mechanical structure between different vehicles of the same model, the throttle and brake calibration table may not be applicable to other vehicles of the same model. Furthermore, even for the same vehicle, mechanical wear and vehicle aging during use may cause the throttle and brake calibration table to gradually become inaccurate, seriously affecting autonomous driving performance and endangering driving safety. Summary of the Invention

[0003] In view of this, this application provides a method for evaluating a throttle-brake calibration gauge, an apparatus for evaluating a throttle-brake calibration gauge, a computer device, and a readable storage medium, thereby enabling accurate evaluation of the throttle-brake calibration gauge.

[0004] In a first aspect, embodiments of this application provide a method for evaluating a throttle-brake calibration table, applied to a cloud server, the method comprising:

[0005] Acquire valid feedback data from autonomous vehicles. Valid feedback data includes speed, acceleration, and accelerator and brake pedal opening degrees that have corresponding relationships.

[0006] The speed is divided into multiple speed ranges, and two-dimensional data corresponding to each speed range is obtained. The two-dimensional data includes multiple accelerations and multiple accelerator and brake pedal opening degrees corresponding to each acceleration.

[0007] Based on the two-dimensional data of the target speed range in multiple speed ranges, the pre-stored standard throttle and brake calibration table of autonomous vehicles is evaluated. The standard throttle and brake calibration table includes standard two-dimensional data in the same speed range as the target speed range.

[0008] In this embodiment, a large amount of effective feedback data is obtained from the autonomous vehicle. This effective feedback data is the actual accelerator and brake pedal opening degree of the vehicle at different speeds and accelerations.

[0009] Since the throttle and brake calibration tables are only weakly correlated with speed, the speed is segmented, that is, divided into multiple speed ranges. Then, the acceleration and throttle / brake pedal opening degree corresponding to each speed range are determined, resulting in two-dimensional data. It should be noted that one speed range corresponds to multiple accelerations, and each acceleration corresponds to multiple throttle / brake pedal opening degrees. This speed decoupling simplifies the subsequent evaluation process.

[0010] Furthermore, a target speed range is determined from multiple speed ranges, and the two-dimensional data of the target speed range is used to evaluate the standard throttle and brake calibration table in the same speed range as the target speed range to determine whether the standard throttle and brake calibration table is accurate. The standard throttle and brake calibration table is also a table after speed decoupling.

[0011] In this embodiment of the application, during vehicle use, each vehicle generates a large amount of autonomous driving feedback data. The throttle and brake calibration table can be evaluated using big data. Compared to conducting throttle and brake calibration experiments on each vehicle to evaluate the calibration table, this method is more efficient and less costly. By accurately evaluating the throttle and brake calibration table, it is possible to effectively reflect the inaccuracies in the throttle and brake calibration table caused by vehicle differences, vehicle aging, etc.

[0012] The method described in the embodiments of this application may also have the following additional technical features:

[0013] In the above technical solution, optionally, the pre-stored standard throttle and brake calibration table for autonomous vehicles is evaluated based on the two-dimensional data of the target speed range in multiple speed ranges, including: for the target speed range in multiple speed ranges, calculating the average opening degree of multiple throttle and brake pedal opening degrees corresponding to each acceleration in the target speed range; constructing target calibration data for the target speed range based on the multiple accelerations in the target speed range and the average opening degree corresponding to each acceleration; and evaluating the standard throttle and brake calibration table based on the target calibration data of the target speed range.

[0014] In this embodiment, each acceleration corresponds to a sequence of accelerator and brake pedal opening degrees. Therefore, it is necessary to calculate the average value of these multiple accelerator and brake pedal opening degrees so that each acceleration corresponds to an average opening degree. This constructs target calibration data that includes multiple accelerations and an average opening degree corresponding to each acceleration, which facilitates subsequent evaluation of the standard accelerator and brake calibration table.

[0015] In any of the above technical solutions, optionally, the average opening degree of multiple accelerator and brake pedal opening degrees corresponding to each acceleration in the target speed range includes: performing acceleration clustering processing on multiple accelerations in the target speed range to obtain clustered accelerations; and calculating the average opening degree of multiple accelerator and brake pedal opening degrees corresponding to each clustered acceleration.

[0016] In this embodiment, before calculating the average opening value of multiple accelerator and brake pedal opening values ​​corresponding to each acceleration, acceleration clustering is performed first. For example, other accelerations that are adjacent to a certain acceleration value are clustered with that acceleration.

[0017] The reason for performing acceleration clustering is that multiple standard accelerations are marked during throttle and brake calibration experiments. If clustering is performed based on these accelerations, it is easy to calculate the difference between the standard throttle and brake pedal opening degree under each acceleration and the actual throttle and brake pedal opening degree under the corresponding actual acceleration.

[0018] In any of the above technical solutions, optionally, the standard throttle and brake calibration table is evaluated based on the target calibration data of the target speed range, including: calculating the deviation value between the target calibration data and the standard two-dimensional data in the standard throttle and brake calibration table; if the deviation value is greater than or equal to a first preset threshold, it is determined that the standard throttle and brake calibration table has a deviation.

[0019] In this embodiment, the deviation between the target calibration data and the standard two-dimensional data in the standard throttle and brake calibration table is used to determine whether there is a deviation in the standard throttle and brake calibration table.

[0020] This application aims to conduct offline evaluation of vehicle throttle and brake calibration tables using a large amount of backflow data, quantify the error between actual vehicle data and standard throttle and brake calibration tables, and effectively reflect the inaccuracy of throttle and brake calibration tables caused by vehicle differences, vehicle aging, etc.

[0021] In any of the above technical solutions, optionally, after determining that there is a deviation in the standard throttle and brake calibration table, the method further includes: adjusting the standard two-dimensional data in the standard throttle and brake calibration table according to the deviation value; or, adjusting the standard throttle and brake calibration table using the target calibration data; and sending the adjusted standard throttle and brake calibration table to the autonomous vehicle.

[0022] In this embodiment, after determining that there is a deviation in the standard throttle and brake calibration table, the standard throttle and brake calibration table can be adjusted according to the deviation value, or the data in the standard throttle and brake calibration table can be directly replaced with the target calibration data to update the standard throttle and brake calibration table. The updated standard throttle and brake calibration table is then sent to the autonomous vehicle, enabling the autonomous vehicle to perform autonomous driving according to the updated calibration table, thereby improving the autonomous driving performance of the vehicle.

[0023] In any of the above technical solutions, optionally, obtaining valid backflow data from autonomous vehicles includes: obtaining backflow data from autonomous vehicles and storing the backflow data in a sliding window in the order of acquisition time; and using the backflow data stored in the sliding window within a preset time period as valid backflow data.

[0024] In this embodiment, the sliding window is a stack that stores historical data. For example, the sliding window records the data of the previous 100 frames starting from the current time. Each loop takes 0.02 seconds, so the sliding window will save all the data in the past 2 seconds.

[0025] By using a sliding window to store more frames of reflow data from autonomous vehicles offline, it becomes easier to filter out effective reflow data.

[0026] Optionally, in any of the above technical solutions, before storing the reflow data into the sliding window in the order of acquisition time, the method further includes: retaining the autonomous driving data in the reflow data.

[0027] In this embodiment, after receiving the feedback data from the autonomous vehicle, a preliminary screening is first performed. This is because the vehicle-side feedback data includes both manual driving data and autonomous driving data. Since the standard throttle and brake calibration table needs to be evaluated using the autonomous driving data, the manual driving data in the feedback data is filtered out to avoid the influence of the manual driving data on the evaluation, and the autonomous driving data in the feedback data is retained.

[0028] In any of the above technical solutions, optionally, the return data stored in the sliding window within a preset time period is used as valid return data, including: determining whether the return data stored in the sliding window within the preset time period is steady-state data; if the return data is steady-state data, then the return data is used as valid return data.

[0029] Optionally, in any of the above technical solutions, a method for determining steady-state data is defined. Specifically, the preset time period includes multiple sub-time periods; determining whether the reflux data stored in the preset time period in the sliding window is steady-state data includes: if the absolute value of the change in the accelerator and brake pedal opening degree in each sub-time period is less than or equal to a second preset threshold, then the reflux data in the preset time period is determined to be steady-state data.

[0030] In this embodiment, the state of the reflux data stored in the sliding window is judged. The steady state refers to the state in which the correspondence between acceleration and the opening and closing degree of the accelerator and brake pedals reaches an approximately linear state.

[0031] When the reflux data is steady-state data, it indicates that the data was generated when the vehicle was running stably. In this state, it is easier to find the correspondence between acceleration and the opening and closing of the accelerator and brake pedals, and the correspondence is more reliable.

[0032] Once the reflux data within the sliding window reaches a steady state, these data are considered valid reflux data. Utilizing valid reflux data to evaluate the standard throttle and brake calibration table improves the accuracy of the evaluation.

[0033] Secondly, embodiments of this application provide an evaluation device for a throttle-brake calibration table, comprising:

[0034] The data acquisition module is used to acquire effective feedback data from autonomous vehicles. The effective feedback data includes speed, acceleration, and accelerator and brake pedal opening degrees that have corresponding relationships.

[0035] The speed division module is used to divide the speed into multiple speed ranges and obtain two-dimensional data corresponding to each speed range. The two-dimensional data includes multiple accelerations and multiple accelerator and brake pedal opening degrees corresponding to each acceleration.

[0036] The evaluation module is used to evaluate the pre-stored standard throttle and brake calibration table of autonomous vehicles based on the two-dimensional data of the target speed range in multiple speed ranges. The standard throttle and brake calibration table includes standard two-dimensional data in the same speed range as the target speed range.

[0037] In this embodiment of the application, during vehicle use, each vehicle generates a large amount of autonomous driving feedback data. The throttle and brake calibration table can be evaluated using big data. Compared to conducting throttle and brake calibration experiments on each vehicle to evaluate the calibration table, this method is more efficient and less costly. By accurately evaluating the throttle and brake calibration table, it is possible to effectively reflect the inaccuracies in the throttle and brake calibration table caused by vehicle differences, vehicle aging, etc.

[0038] Thirdly, embodiments of this application provide a computer device including a processor and a memory, the memory storing programs or instructions that can run on the processor, the programs or instructions implementing the steps of the method as described in the first aspect when executed by the processor.

[0039] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method as described in the first aspect.

[0040] Fifthly, embodiments of this application provide a chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0041] In a sixth aspect, embodiments of this application provide a computer program product stored in a storage medium, which is executed by at least one processor to implement the method as described in the first aspect.

[0042] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0043] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0044] Figure 1 One of the flowcharts for evaluating the brake calibration table according to an embodiment of this application is shown;

[0045] Figure 2 A schematic diagram of the reflux data according to an embodiment of this application is shown;

[0046] Figure 3 A schematic diagram illustrating the return data status according to an embodiment of this application is shown;

[0047] Figure 4 This illustration shows a schematic diagram of two-dimensional data corresponding to the velocity range of 0 to 10 mps according to an embodiment of this application;

[0048] Figure 5 A schematic diagram illustrating the evaluation of the speed range from 0 to 10 mps according to an embodiment of this application is shown;

[0049] Figure 6 A schematic diagram illustrating the evaluation of the speed range of 10 mps to 20 mps according to an embodiment of this application is shown;

[0050] Figure 7 A schematic diagram illustrating the evaluation of speed ranges above 20 mps according to an embodiment of this application is shown;

[0051] Figure 8 This is a second schematic flowchart illustrating the evaluation method of the brake calibration table according to an embodiment of this application;

[0052] Figure 9 A structural block diagram of the evaluation device for the brake calibration table according to an embodiment of this application is shown;

[0053] Figure 10 A structural block diagram of a computer device according to an embodiment of this application is shown. Detailed Implementation

[0054] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0055] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0056] The following description, in conjunction with the accompanying drawings, details the evaluation method, evaluation device, computer equipment, and readable storage medium for the throttle and brake calibration table provided in this application, through specific embodiments and application scenarios.

[0057] This application provides a method for evaluating a throttle-brake calibration table, applied to a cloud server, such as... Figure 1 As shown, the method includes:

[0058] Step 101: Obtain valid feedback data from the autonomous vehicle. Valid feedback data includes speed, acceleration, and accelerator and brake pedal opening and closing degrees that have corresponding relationships.

[0059] In this step, a large amount of feedback data is received from the autonomous vehicle, such as... Figure 2As shown, the reflux data represents the actual accelerator and brake pedal opening and closing degree of the vehicle at different speeds and accelerations. The X-axis represents speed, the Y-axis represents acceleration, and the Z-axis represents the accelerator and brake pedal opening and closing degree.

[0060] After receiving the feedback data from autonomous vehicles, an initial screening is performed. This is because the feedback data from the vehicle includes both manual driving data and autonomous driving data. Since the standard throttle and brake calibration table needs to be evaluated using the autonomous driving data, the manual driving data in the feedback data is filtered out to avoid the influence of the manual driving data on the evaluation. Only the autonomous driving data in the feedback data is retained.

[0061] After initial screening, the replay data is stored in a sliding window according to the acquisition time, so that the sliding window stores the replay data within a preset time period. The sliding window is a stack that saves historical data. For example, if the sliding window records the data of the previous 100 frames starting from the current time, and each loop takes 0.02 seconds, then the sliding window will save all the data within the past 2 seconds.

[0062] By using a sliding window to store more frames of recurrent data from autonomous vehicles offline, it becomes easier to perform steady-state screening of effective recurrent data in the future.

[0063] Furthermore, it is determined whether the reflux data stored in the sliding window within a preset time period is steady-state data. Steady-state refers to a state where the correspondence between acceleration and the accelerator / brake pedal opening degree is approximately linear. When the reflux data is steady-state, it indicates that it was generated when the vehicle was running stably, and the correspondence between acceleration and the accelerator / brake pedal opening degree is approximately linear. In this state, it is easier to find the corresponding pattern between acceleration and the accelerator / brake pedal opening degree, and the corresponding pattern is more reliable. Non-steady-state data makes it difficult to find the corresponding pattern between acceleration and the accelerator / brake pedal opening degree, so non-steady-state data is deleted.

[0064] Once the reflux data within the sliding window reaches a steady state, these data are considered valid reflux data. Utilizing valid reflux data to evaluate the standard throttle and brake calibration table improves the accuracy of the evaluation.

[0065] It should be noted that the method in this application embodiment is more applicable to scenarios where the vehicle can operate stably, such as high-speed scenarios.

[0066] In one embodiment of this application, a method for determining steady-state data is defined. Specifically, the preset time period includes multiple sub-time periods; determining whether the reflux data stored in the preset time period in the sliding window is steady-state data includes: if the absolute value of the change in the accelerator and brake pedal opening degree in each sub-time period is less than or equal to a second preset threshold, then the reflux data in the preset time period is determined to be steady-state data.

[0067] In this embodiment, if the absolute value of the change in the accelerator and brake pedal opening degree in each sub-time period of the preset time period is less than or equal to the second preset threshold, it indicates that the change in the accelerator and brake pedal opening degree in the preset time period is small, and the return data in the preset time period is determined to be steady-state data.

[0068] like Figure 3 As shown, the data in the range 0 to a1 is characterized by large fluctuations, while the data in the range a1 to a2 is characterized by smaller fluctuations and is approximately linear. Therefore, only the data in the range a1 to a2 is retained.

[0069] In another embodiment of this application, a different method for determining steady-state data is defined. Specifically, determining whether the reflux data stored in the sliding window within a preset time period is steady-state data includes: constructing a linear fitting function of acceleration and accelerator / brake pedal opening degree based on the stored reflux data acquired before the preset time period; calculating the least square error between the reflux data within the preset time period and the linear fitting function; if the least square error is less than or equal to a third preset threshold, then determining that the reflux data within the preset time period is steady-state data.

[0070] In this embodiment, a linear fitting function for acceleration and accelerator / brake pedal opening degree is constructed based on the stored reflux data, and the error between the reflux data and the linear fitting function within a preset time period is calculated. If the error is small, the reflux data within the preset time period is determined to be steady-state data.

[0071] For example, a linear function y = kx + b is obtained using the least squares method, where k and b are known coefficients, x represents acceleration, and y represents the opening and closing of the accelerator and brake pedals. Based on this linear function, a corresponding y value can be obtained for each actual x value. Then, the least squares error between the corresponding y value and the actual y value is compared. If the least squares error is less than or equal to a third preset threshold, it indicates that the actual x value and the actual y value are a set of approximately linear data, that is, it is steady-state data.

[0072] By using initial data screening, sliding window analysis, and steady-state data filtering, effective feedback data is obtained, avoiding noisy data and improving the accuracy of subsequent evaluations.

[0073] Step 102: Divide the speed into multiple speed ranges and obtain two-dimensional data corresponding to each speed range. The two-dimensional data includes multiple accelerations and multiple accelerator and brake pedal opening degrees corresponding to each acceleration.

[0074] In this step, based on prior knowledge, it is known that the accelerator and brake calibration table is weakly correlated with speed. That is, under the same acceleration, the higher the speed, the less significant the change in the opening and closing of the accelerator and brake pedals.

[0075] Therefore, after obtaining valid reflux data, speed decoupling processing is performed to transform the reflux data into two-dimensional data. Specifically, the speed is segmented, that is, the speed is divided into multiple speed ranges, for example, the speed range is divided into a speed range of 0 to 10 mps (meters per second), a speed range of 10 mps to 20 mps, and a speed range above 20 mps. Then, the acceleration and accelerator / brake pedal opening degree corresponding to each speed range are determined, that is, the two-dimensional data, such as... Figure 4 The figure shows two-dimensional data of acceleration and accelerator / brake pedal opening for a speed range of 0 to 10 mps. It should be noted that one speed range corresponds to multiple accelerations, and each acceleration corresponds to multiple accelerator / brake pedal openings.

[0076] By decoupling speed, the three-dimensional data is decoupled into two-dimensional data, which makes it possible to conduct subsequent evaluations according to different speeds and corresponding to the two-dimensional standard throttle and brake calibration table, thus simplifying the evaluation process.

[0077] Step 103: Based on the two-dimensional data of the target speed range in multiple speed ranges, evaluate the pre-stored standard throttle and brake calibration table of autonomous vehicles. The standard throttle and brake calibration table includes standard two-dimensional data in the same speed range as the target speed range.

[0078] In this step, any one of the multiple speed ranges is determined as a target speed range, and the two-dimensional data of the target speed range is used to evaluate the standard throttle and brake calibration table under the same speed range as the target speed range to determine whether the standard throttle and brake calibration table is accurate. The standard throttle and brake calibration table is also a table after speed decoupling.

[0079] Specifically, for any target speed range among multiple speed ranges, calculate the average opening degree of multiple accelerator and brake pedal opening degrees corresponding to each acceleration in the target speed range; construct target calibration data for the target speed range based on multiple accelerations in the target speed range and the average opening degree corresponding to each acceleration; and evaluate the standard accelerator and brake calibration table based on the target calibration data of the target speed range.

[0080] In this embodiment, each acceleration corresponds to a sequence of accelerator and brake pedal opening degrees. Therefore, it is necessary to calculate the average value of these multiple accelerator and brake pedal opening degrees so that each acceleration corresponds to an average opening degree. This constructs two-dimensional target calibration data that includes multiple accelerations and an average opening degree corresponding to each acceleration, which facilitates subsequent evaluation of the standard accelerator and brake calibration table.

[0081] It is worth noting that before calculating the average opening degree of multiple accelerator and brake pedal opening degrees corresponding to each acceleration within the target speed range, acceleration clustering processing needs to be performed on the multiple accelerations within the target speed range. For example, in Figure 4 Multiple acceleration points are marked, and then other accelerations near the marked acceleration points are clustered with the marked acceleration points, such as... Figure 5 As shown, the acceleration after clustering is obtained, as well as the opening and closing degrees of multiple accelerator and brake pedals corresponding to each clustered acceleration.

[0082] The reason for performing acceleration clustering is that multiple standard accelerations are marked during throttle and brake calibration experiments. If clustering is performed based on these accelerations, it is easy to calculate the difference between the standard throttle and brake pedal opening degree under each acceleration and the actual throttle and brake pedal opening degree under the corresponding actual acceleration.

[0083] After clustering, the average opening degree of multiple accelerator and brake pedal opening degrees corresponding to each clustered acceleration is calculated. The first curve of the two-dimensional target calibration data, composed of multiple clustered accelerations and the average opening degree corresponding to each clustered acceleration, is shown below. Figure 5 As shown.

[0084] It should be noted that when evaluating the standard throttle and brake calibration table based on the target calibration data for the target speed range, the deviation between the target calibration data and the standard two-dimensional data in the standard throttle and brake calibration table is calculated. The standard two-dimensional data in the standard throttle and brake calibration table is also two-dimensional data after speed decoupling. For example, the deviation is compared between the target calibration data corresponding to the speed range of 0 to 10 mps and the standard two-dimensional data in the standard throttle and brake calibration table corresponding to the speed range of 0 to 10 mps. The second curve formed by the standard two-dimensional data in the standard throttle and brake calibration table is shown below. Figure 5 As shown. If the deviation value is greater than or equal to the first preset threshold, it is determined that there is a deviation in the standard throttle and brake calibration table.

[0085] The above describes a method for evaluating the throttle and brake calibration table in the speed range of 0 to 10 mps. This method can be used to evaluate the throttle and brake calibration table for different speed ranges.

[0086] For example, Figure 5 This is a schematic diagram illustrating the evaluation of the speed range from 0 to 10 mps. Figure 6 This is a schematic diagram illustrating the evaluation in the speed range of 10mps to 20mps. Figure 7 This is a schematic diagram illustrating the evaluation in the speed range above 20 mps. (Example:) Figure 5 and Figure 6 As shown, for the first autonomous vehicle, in the speed range of 0 to 20 mps, the deviation between the first curve corresponding to the feedback data of the first autonomous vehicle and the second curve of the standard throttle-brake calibration table is large, indicating that the first autonomous vehicle has a significant braking underperformance problem. Figure 5 and Figure 6 In the data, the deviation between the third curve corresponding to the feedback data of the second autonomous vehicle and the second curve of the standard throttle and brake calibration table is small, indicating that the standard throttle and brake calibration table of the second autonomous vehicle is more accurate. Figure 7 As shown, in the speed range above 20 mps, the first and third curves corresponding to the feedback data of the two autonomous vehicles have a certain deviation from the second curve of the standard throttle and brake calibration table. That is, the standard throttle and brake calibration table corresponding to the speed range above 20 mps is not accurate enough.

[0087] In this embodiment of the application, during vehicle use, each vehicle generates a large amount of autonomous driving feedback data. The throttle and brake calibration table can be evaluated using big data. Compared to conducting throttle and brake calibration experiments on each vehicle to evaluate the calibration table, this method is more efficient and less costly. By accurately evaluating the throttle and brake calibration table, it is possible to effectively reflect the inaccuracies in the throttle and brake calibration table caused by vehicle differences, vehicle aging, etc.

[0088] As a refinement and extension of the above embodiments, this invention provides another method for evaluating throttle-brake calibration tables, such as... Figure 8 As shown, the method includes:

[0089] Step 801: Obtain valid feedback data from the autonomous vehicle. Valid feedback data includes speed, acceleration, and accelerator and brake pedal opening and closing degrees that have corresponding relationships.

[0090] Step 802: Divide the speed into multiple speed ranges and obtain two-dimensional data corresponding to each speed range. The two-dimensional data includes multiple accelerations and multiple accelerator and brake pedal opening degrees corresponding to each acceleration.

[0091] Step 803: Based on the two-dimensional data of the target speed range in multiple speed ranges, evaluate the pre-stored standard throttle and brake calibration table of autonomous vehicles. The standard throttle and brake calibration table includes standard two-dimensional data in the same speed range as the target speed range.

[0092] Step 804: Adjust the standard two-dimensional data in the standard throttle and brake calibration table according to the deviation value, or adjust the standard throttle and brake calibration table using the target calibration data;

[0093] Step 805: Send the adjusted standard throttle and brake calibration table to the autonomous vehicle.

[0094] In this embodiment, after determining that there is a deviation in the standard throttle and brake calibration table, the standard throttle and brake calibration table can be adjusted according to the deviation value, or the data in the standard throttle and brake calibration table can be directly replaced with the target calibration data to update the standard throttle and brake calibration table.

[0095] For example, in the standard throttle and brake calibration table, an acceleration of -2 m / s² can be achieved when the throttle and brake opening degree is 0.25%. 2 However, the feedback data from the vehicle shows that the acceleration can only reach -2m / s² when the throttle / brake engagement is 0.27%. 2 The reason might be worn brake pads, requiring more braking to achieve the same desired acceleration. This allows us to obtain the deviation between the standard throttle-brake calibration table and the feedback data from the vehicle. The standard throttle-brake calibration table is then updated based on this deviation. Alternatively, the acceleration in the standard throttle-brake calibration table can be directly reduced by 2 m / s². 2 The corresponding throttle and brake engagement has been updated to 0.27%.

[0096] The updated standard throttle and brake calibration table is then sent to the autonomous vehicle, enabling it to drive automatically according to the updated calibration table, thus improving the vehicle's autonomous driving performance.

[0097] As a specific implementation of the evaluation method for throttle and brake calibration gauges, embodiments of this application provide an evaluation device for throttle and brake calibration gauges. For example... Figure 9 As shown, the evaluation device 900 for the throttle and brake calibration table includes: a data acquisition module 901, a speed division module 902, and an evaluation module 903.

[0098] The data acquisition module 901 is used to acquire effective feedback data of autonomous vehicles. The effective feedback data includes speed, acceleration and accelerator and brake pedal opening degree with corresponding relationships.

[0099] The speed division module 902 is used to divide the speed into multiple speed intervals and obtain two-dimensional data corresponding to each speed interval. The two-dimensional data includes multiple accelerations and multiple accelerator and brake pedal opening degrees corresponding to each acceleration.

[0100] The evaluation module 903 is used to evaluate the pre-stored standard throttle and brake calibration table of the autonomous vehicle based on the two-dimensional data of the target speed range among the multiple speed ranges. The standard throttle and brake calibration table includes standard two-dimensional data under the same speed range as the target speed range.

[0101] In this embodiment, a large amount of effective feedback data is obtained from the autonomous vehicle. This effective feedback data is the actual accelerator and brake pedal opening degree of the vehicle at different speeds and accelerations.

[0102] Since the throttle and brake calibration tables are only weakly correlated with speed, the speed is segmented, that is, divided into multiple speed ranges. Then, the acceleration and throttle / brake pedal opening degree corresponding to each speed range are determined, resulting in two-dimensional data. It should be noted that one speed range corresponds to multiple accelerations, and each acceleration corresponds to multiple throttle / brake pedal opening degrees. This speed decoupling simplifies the subsequent evaluation process.

[0103] Furthermore, a target speed range is determined from multiple speed ranges, and the two-dimensional data of the target speed range is used to evaluate the standard throttle and brake calibration table in the same speed range as the target speed range to determine whether the standard throttle and brake calibration table is accurate. The standard throttle and brake calibration table is also a table after speed decoupling.

[0104] In this embodiment of the application, during vehicle use, each vehicle generates a large amount of autonomous driving feedback data. The throttle and brake calibration table can be evaluated using big data. Compared to conducting throttle and brake calibration experiments on each vehicle to evaluate the calibration table, this method is more efficient and less costly. By accurately evaluating the throttle and brake calibration table, it is possible to effectively reflect the inaccuracies in the throttle and brake calibration table caused by vehicle differences, vehicle aging, etc.

[0105] Furthermore, the evaluation module 903 is specifically used for: calculating the average opening value of multiple accelerator and brake pedal opening values ​​corresponding to each acceleration in the target speed range for multiple speed ranges; constructing target calibration data for the target speed range based on the multiple accelerations in the target speed range and the average opening value corresponding to each acceleration; and evaluating the standard accelerator and brake calibration table based on the target calibration data for the target speed range.

[0106] Furthermore, the evaluation module 903 is specifically used to: perform acceleration clustering on multiple accelerations in the target speed range to obtain the clustered accelerations; and calculate the average opening value of multiple accelerator and brake pedal opening degrees corresponding to each clustered acceleration.

[0107] Furthermore, the evaluation module 903 is specifically used to: calculate the deviation value between the target calibration data and the standard two-dimensional data in the standard throttle and brake calibration table; if the deviation value is greater than or equal to the first preset threshold, it is determined that there is a deviation in the standard throttle and brake calibration table.

[0108] Furthermore, the device also includes: an adjustment module for adjusting the standard two-dimensional data in the standard throttle and brake calibration table according to the deviation value; or, adjusting the standard throttle and brake calibration table using the target calibration data; and a transmission module for sending the adjusted standard throttle and brake calibration table to the autonomous vehicle.

[0109] Furthermore, the data acquisition module 901 is specifically used to: acquire backflow data from autonomous vehicles and store the backflow data in a sliding window in the order of acquisition time; and use the backflow data stored in the sliding window within a preset time period as valid backflow data.

[0110] Furthermore, the device also includes a filtering module for retaining autonomous driving data in the reflow data.

[0111] Furthermore, the data acquisition module 901 is specifically used to: determine whether the return data stored in the sliding window within a preset time period is steady-state data; if the return data is steady-state data, then the return data is taken as valid return data.

[0112] Furthermore, the preset time period includes multiple sub-time periods; the data acquisition module 901 is specifically used to: if the absolute value of the change in the opening and closing degree of the accelerator and brake pedals in each sub-time period is less than or equal to the second preset threshold, then the return flow data in the preset time period is determined to be steady-state data.

[0113] Furthermore, the data acquisition module 901 is specifically used to: construct a linear fitting function of acceleration and accelerator / brake pedal opening degree based on the stored reflux data acquired before a preset time period; calculate the minimum square error between the reflux data within the preset time period and the linear fitting function; if the minimum square error is less than or equal to a third preset threshold, then determine the reflux data within the preset time period as steady-state data.

[0114] The throttle and brake calibration table evaluation device 900 in this application embodiment can be a computer device or a component within the computer device, such as an integrated circuit or a chip. The computer device can be a terminal or other devices besides a terminal. For example, the computer device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle computer device, mobile internet device (MID), robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc., and can also be a server, network attached storage (NAS), personal computer (PC), etc. This application embodiment does not specifically limit the specific device.

[0115] The throttle-brake calibration table evaluation device 900 provided in this application embodiment can achieve... Figure 1 The various processes implemented in the evaluation method of the throttle and brake calibration table will not be repeated here to avoid repetition.

[0116] This application also provides a computer device, such as... Figure 10 As shown, the computer device 1000 includes a processor 1001 and a memory 1002. The memory 1002 stores a program or instructions that can run on the processor 1001. When the program or instructions are executed by the processor 1001, they implement the various steps of the above-described throttle and brake calibration table evaluation method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0117] It should be noted that the computer devices in the embodiments of this application include the mobile computer devices and non-mobile computer devices described above.

[0118] The memory 1002 can be used to store software programs and various data. The memory 1002 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback function, image playback function, etc.). Furthermore, the memory 1002 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 1002 in this embodiment includes, but is not limited to, these and any other suitable types of memory.

[0119] Processor 1001 may include one or more processing units; optionally, processor 1001 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 1001.

[0120] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described throttle and brake calibration table evaluation method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0121] This application also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above-described throttle and brake calibration table evaluation method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0122] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0123] This application also provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the above-described throttle and brake calibration table evaluation method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0124] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0125] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for evaluating a throttle and brake calibration gauge, characterized in that, include: Acquire valid feedback data from autonomous vehicles, including speed, acceleration, and accelerator and brake pedal opening degrees that have corresponding relationships; The speed is divided into multiple speed ranges, and two-dimensional data corresponding to each speed range is obtained. The two-dimensional data includes multiple accelerations and multiple accelerator and brake pedal opening degrees corresponding to each acceleration. Based on the two-dimensional data of the target speed range among the multiple speed ranges, the pre-stored standard throttle and brake calibration table of the autonomous vehicle is evaluated. The standard throttle and brake calibration table includes standard two-dimensional data under the same speed range as the target speed range. The acquisition of valid feedback data from autonomous vehicles includes: Acquire backflow data from the autonomous vehicle and store the backflow data in a sliding window in the order of acquisition time; Determine whether the reflux data stored in the sliding window within a preset time period is steady-state data; If the reflux data is steady-state data, then the reflux data is considered as valid reflux data; The preset time period includes multiple sub-time periods; determining whether the reflux data within the preset time period stored in the sliding window is steady-state data includes: If the absolute value of the change in the accelerator and brake pedal opening degree in each sub-time period is less than or equal to the second preset threshold, then the reflux data in the preset time period is determined to be steady-state data.

2. The method according to claim 1, characterized in that, The evaluation of the pre-stored standard throttle and brake calibration table of the autonomous vehicle based on two-dimensional data of the target speed range among the multiple speed ranges includes: For a target speed range among the multiple speed ranges, calculate the average opening value of multiple accelerator and brake pedal opening degrees corresponding to each acceleration in the target speed range; Based on the multiple accelerations in the target velocity range and the average opening degree corresponding to each acceleration, target calibration data for the target velocity range is constructed. The standard throttle and brake calibration table is evaluated based on the target calibration data for the target speed range.

3. The method according to claim 2, characterized in that, The calculation of the average opening value of multiple accelerator and brake pedal opening degrees corresponding to each acceleration in the target speed range includes: Multiple accelerations within the target velocity range are subjected to acceleration clustering processing to obtain the clustered accelerations; Calculate the average opening value of multiple accelerator and brake pedal opening values ​​corresponding to the acceleration after each clustering process.

4. The method according to claim 2, characterized in that, The evaluation of the standard throttle and brake calibration table based on the target calibration data for the target speed range includes: Calculate the deviation between the target calibration data and the standard two-dimensional data in the standard throttle and brake calibration table; If the deviation value is greater than or equal to the first preset threshold, then it is determined that there is a deviation in the standard throttle and brake calibration table.

5. The method according to claim 4, characterized in that, After determining that the standard throttle and brake calibration table has a deviation, the method further includes: Adjust the standard two-dimensional data in the standard throttle and brake calibration table according to the deviation value; or, adjust the standard throttle and brake calibration table using the target calibration data; The adjusted standard throttle and brake calibration table is sent to the autonomous vehicle.

6. The method according to claim 1, characterized in that, Before storing the reflow data into the sliding window in the order of acquisition time, the method further includes: The autonomous driving data in the reflowed data is retained.

7. A testing device for a throttle and brake calibration gauge, characterized in that, include: The data acquisition module is used to acquire effective feedback data of autonomous vehicles. The effective feedback data includes speed, acceleration and accelerator and brake pedal opening degree with corresponding relationships. The speed division module is used to divide the speed into multiple speed intervals and obtain two-dimensional data corresponding to each speed interval. The two-dimensional data includes multiple accelerations and multiple accelerator and brake pedal opening degrees corresponding to each acceleration. The evaluation module is used to evaluate the pre-stored standard throttle and brake calibration table of the autonomous vehicle based on the two-dimensional data of the target speed range among the multiple speed ranges. The standard throttle and brake calibration table includes standard two-dimensional data under the same speed range as the target speed range. The data acquisition module is specifically used for: Acquire backflow data from the autonomous vehicle and store the backflow data in a sliding window in the order of acquisition time; Determine whether the reflux data stored in the sliding window within a preset time period is steady-state data; If the reflux data is steady-state data, then the reflux data is considered as valid reflux data; The preset time period includes multiple sub-time periods; determining whether the reflux data within the preset time period stored in the sliding window is steady-state data includes: If the absolute value of the change in the accelerator and brake pedal opening degree in each sub-time period is less than or equal to the second preset threshold, then the reflux data in the preset time period is determined to be steady-state data.

8. A computer device, characterized in that, It includes a processor and a memory, the memory storing a program or instructions that run on the processor, the program or instructions being executed by the processor to implement the steps of the evaluation method for the throttle and brake calibration table as described in any one of claims 1 to 6.

9. A readable storage medium having a program or instructions stored thereon, characterized in that, When the program or instructions are executed by the processor, they implement the steps of the evaluation method for the throttle and brake calibration table as described in any one of claims 1 to 6.

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