Acc data-driven optimization method and device, vehicle and storage medium

Abstract

This application relates to an ACC data-driven optimization method, apparatus, vehicle, and storage medium. The method includes: acquiring at least one data source and inputting it into a preset ACC functional model to obtain acceleration function evaluation indicators; calculating the gain values ​​of the acceleration indicator, acceleration time indicator, and steady-state time indicator respectively to obtain the acceleration indicator gain, acceleration time indicator gain, and steady-state time indicator gain; obtaining the comprehensive acceleration gain based on the acceleration indicator gain, acceleration time indicator gain, and steady-state time indicator gain; and performing acceleration optimization based on the comprehensive acceleration gain. This solves the problem in related technologies where ACC acceleration calibration and optimization involves cumbersome data acquisition and cannot cover all scenarios, but can cover a large number of operating conditions, greatly saving optimization time.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a data-driven optimization method, device, vehicle, and storage medium for ACC (Adaptive Cruise Control). Background Technology

[0002] During the development of the ACC function, a large amount of data calibration and parameter tuning optimization are required based on actual and theoretical data.

[0003] In related technologies, calibrating and optimizing ACC acceleration requires extensive testing, obtaining actual data, and performing theoretical derivations. However, data acquisition is cumbersome and cannot cover all scenarios, which urgently needs to be addressed. Summary of the Invention

[0004] This application provides an ACC data-driven optimization method, device, vehicle, and storage medium to solve the problems in related technologies where ACC acceleration calibration and optimization data acquisition is cumbersome and cannot cover all scenarios. It adopts a data-driven approach for optimization and parameter tuning, covering most operating conditions. It automatically associates data and tunes parameters based on evaluation indicators, eliminating the need for extensive testing to acquire data and greatly saving optimization time.

[0005] The first aspect of this application provides an ACC data-driven optimization method, comprising the following steps: acquiring at least one data source; inputting the at least one data source into a preset ACC function model to obtain an acceleration function evaluation index, wherein the acceleration function evaluation index includes an acceleration index, an acceleration time index, and a steady-state time index; calculating the gain values ​​of the acceleration index, the acceleration time index, and the steady-state time index respectively to obtain the acceleration index gain, the acceleration time index gain, and the steady-state time index gain, and obtaining a comprehensive acceleration gain based on the acceleration index gain, the acceleration time index gain, and the steady-state time index gain, and performing acceleration optimization based on the comprehensive acceleration gain.

[0006] Optionally, in some embodiments, after performing acceleration optimization based on the acceleration composite gain, the method further includes: obtaining the previous acceleration optimization result and the current acceleration optimization result; if the previous acceleration optimization result and the current acceleration optimization result are the same, then the current acceleration optimization result is used as the calibration data of the at least one data source.

[0007] Optionally, in some embodiments, calculating the gain values ​​of the acceleration index, the acceleration time index, and the steady-state time index respectively to obtain the acceleration index gain, the acceleration time index gain, and the steady-state time index gain includes: calculating the index gain of the acceleration index based on a preset acceleration index gain strategy to obtain the acceleration index gain, wherein the preset acceleration index gain strategy is: Acceleration index gain = (1 + (a_realmax - a_max) / a_realmax); Where a_realmax is the actual maximum acceleration and a_max is the maximum constrained acceleration.

[0008] Optionally, in some embodiments, the step of calculating the gain values ​​of the acceleration index, the acceleration time index, and the steady-state time index respectively to obtain the acceleration index gain, the acceleration time index gain, and the steady-state time index gain further includes: calculating the index gain of the acceleration time index based on a preset acceleration time index gain strategy to obtain the acceleration time index gain, wherein the preset acceleration time index gain strategy is: Acceleration time index gain = (1 + (Tm - T(m-1)) / T(m-1)); Where Tm is the acceleration time of the mth acceleration, and T(m-1) is the acceleration time of the (m-1)th acceleration.

[0009] Optionally, in some embodiments, calculating the gain values ​​of the acceleration index, the acceleration time index, and the steady-state time index respectively to obtain the acceleration index gain, the acceleration time index gain, and the steady-state time index gain includes: calculating the index gain of the steady-state time index based on a preset steady-state time index gain strategy to obtain the steady-state time index gain, wherein the preset steady-state time index gain strategy is: Steady-state time index gain = (1 + (tm - t(m-1)) / t(m-1)) Where tm is the m-th steady-state time, and t(m-1) is the (m-1)-th steady-state time.

[0010] Optionally, in some embodiments, obtaining the comprehensive acceleration gain based on the acceleration index gain, the acceleration time index gain, and the steady-state time index gain includes: the comprehensive acceleration gain = the acceleration index gain * the acceleration time index gain * the steady-state time index gain.

[0011] A second aspect of this application provides an ACC data-driven optimization device, comprising: an acquisition module for acquiring at least one data source; an input module for inputting the at least one data source into a preset ACC function model to obtain an acceleration function evaluation index, wherein the acceleration function evaluation index includes an acceleration index, an acceleration time index, and a steady-state time index; and an optimization module for calculating the gain values ​​of the acceleration index, the acceleration time index, and the steady-state time index respectively to obtain the acceleration index gain, the acceleration time index gain, and the steady-state time index gain, and obtaining a comprehensive acceleration gain based on the acceleration index gain, the acceleration time index gain, and the steady-state time index gain, and performing acceleration optimization based on the comprehensive acceleration gain.

[0012] Optionally, in some embodiments, after performing acceleration optimization based on the acceleration composite gain, the optimization module further includes: an acquisition unit, configured to acquire the previous acceleration optimization result and the current acceleration optimization result; and a calibration unit, configured to use the current acceleration optimization result as calibration data of the at least one data source when the previous acceleration optimization result and the current acceleration optimization result are the same.

[0013] Optionally, in some embodiments, the optimization module includes: a first calculation unit, configured to calculate the index gain of the acceleration index based on a preset acceleration index gain strategy, thereby obtaining the acceleration index gain, wherein the preset acceleration index gain strategy is: Acceleration index gain = (1 + (a_realmax - a_max) / a_realmax); Where a_realmax is the actual maximum acceleration and a_max is the maximum constrained acceleration.

[0014] Optionally, in some embodiments, the optimization module further includes: a second calculation unit, configured to calculate the gain of the acceleration time index based on a preset acceleration time index gain strategy, thereby obtaining the acceleration time index gain, wherein the preset acceleration time index gain strategy is: Acceleration time index gain = (1 + (Tm - T(m-1)) / T(m-1)); Where Tm is the acceleration time of the mth acceleration, and T(m-1) is the acceleration time of the (m-1)th acceleration.

[0015] Optionally, in some embodiments, the optimization module is specifically used for: a third calculation unit, used to calculate the index gain of the steady-state time index based on a preset steady-state time index gain strategy, to obtain the steady-state time index gain, wherein the preset steady-state time index gain strategy is: Steady-state time index gain = (1 + (tm - t(m-1)) / t(m-1)) Where tm is the m-th steady-state time, and t(m-1) is the (m-1)-th steady-state time.

[0016] Optionally, in some embodiments, the optimization module includes: the acceleration composite gain = the acceleration index gain * the acceleration time index gain * the steady-state time index gain.

[0017] A third aspect of this application provides a vehicle comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the ACC data-driven optimization method as described in the above embodiments.

[0018] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the ACC data-driven optimization method as described in the above embodiments.

[0019] Therefore, by acquiring at least one data source and inputting it into a preset ACC functional model, acceleration function evaluation indicators are obtained. The gain values ​​of acceleration, acceleration time, and steady-state time indicators are calculated respectively, resulting in acceleration indicator gain, acceleration time indicator gain, and steady-state time indicator gain. Based on these gains, a comprehensive acceleration gain is obtained, and acceleration optimization is performed. This solves the problem in related technologies where ACC acceleration calibration and optimization involve cumbersome data acquisition and cannot cover all scenarios. By adopting a data-driven approach for optimization and parameter tuning, it covers a large number of operating conditions. It automatically associates data and automatically tunes parameters based on evaluation indicators, eliminating the need for extensive testing to acquire data and greatly saving optimization time.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of the ACC data-driven optimization method provided according to an embodiment of this application; Figure 2 This is a schematic diagram of a data source provided according to an embodiment of this application; Figure 3This is a flowchart illustrating the calculation of the ACC evaluation index according to one embodiment of this application; Figure 4 This is a schematic diagram illustrating the principle of an ACC data-driven optimization method according to an embodiment of this application; Figure 5 This is a block diagram of an ACC data-driven optimization device provided according to an embodiment of this application.

[0022] Figure 6 This is a structural schematic diagram of a vehicle provided according to an embodiment of this application. Detailed Implementation

[0023] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0024] The following describes an ACC data-driven optimization method, apparatus, vehicle, and storage medium according to embodiments of the present application with reference to the accompanying drawings. Addressing the problems mentioned in the background art regarding the cumbersome data acquisition for ACC acceleration calibration and optimization, and the inability to cover all scenarios, the present application provides an ACC data-driven optimization method. In this method, at least one data source is acquired and input into a preset ACC functional model to obtain acceleration function evaluation indicators. The gain values ​​of the acceleration indicator, acceleration time indicator, and steady-state time indicator are calculated respectively to obtain the acceleration indicator gain, acceleration time indicator gain, and steady-state time indicator gain. Based on these gains, a comprehensive acceleration gain is obtained, and acceleration optimization is performed based on this comprehensive gain. This solves the problem of cumbersome data acquisition for ACC acceleration calibration and optimization, and the inability to cover all scenarios in the related art. By adopting a data-driven approach for optimization and parameter tuning, it covers a vast majority of operating conditions. Data is automatically associated and parameters are automatically tuned based on evaluation indicators, eliminating the need for extensive testing to acquire data and significantly saving optimization time.

[0025] Specifically, Figure 1 This is a flowchart illustrating an ACC data-driven optimization method provided in an embodiment of this application.

[0026] like Figure 1 As shown, the ACC data-driven optimization method includes the following steps: In step S101, at least one data source is obtained.

[0027] Specifically, the data source obtained in the embodiments of this application can be as follows: Figure 2As shown, it includes two parts: initial vehicle speed and expected vehicle speed, covering all acceleration ranges during ACC. It can be based on different data sources, covering all working conditions and scenarios, and the data-driven optimization model can be tuned.

[0028] In step S102, at least one data source is input into the preset ACC function model to obtain acceleration function evaluation indicators, wherein the acceleration function evaluation indicators include acceleration indicators, acceleration time indicators and steady-state time indicators.

[0029] Specifically, in this embodiment of the application, after obtaining at least one data source, the at least one data source is input into a preset ACC function model to obtain acceleration index, acceleration time index and steady-state time index.

[0030] As one possible approach, different data sources are used as variables input to the ACC functional model. The ACC optimizes the acceleration and evaluates the collected data, including: actual acceleration during the process, maximum acceleration, acceleration time (time taken to accelerate to the desired vehicle speed), and steady-state time (time taken to go from maximum speed to steady-state speed).

[0031] In step S103, the gain values ​​of acceleration index, acceleration time index and steady-state time index are calculated respectively to obtain the acceleration index gain, acceleration time index gain and steady-state time index gain. The comprehensive acceleration gain is obtained based on the acceleration index gain, acceleration time index gain and steady-state time index gain, and acceleration optimization is performed based on the comprehensive acceleration gain.

[0032] Specifically, such as Figure 3 As shown, Figure 3 This is a flowchart for calculating the ACC evaluation index according to one embodiment of this application.

[0033] Step S301: Collect data, including: actual acceleration during the process, maximum acceleration, acceleration time (time taken to accelerate to the desired vehicle speed), and steady-state time (time taken to go from maximum speed to steady-state speed).

[0034] Step S302 involves calculating the gain values ​​of the acceleration index, acceleration time index, and steady-state time index, respectively, to obtain the acceleration index gain, acceleration time index gain, and steady-state time index gain. This includes: calculating the index gain of the acceleration index based on a preset acceleration index gain strategy, wherein the preset acceleration index gain strategy is as follows: Acceleration index gain = (1 + (a_realmax - a_max) / a_realmax); Where a_realmax is the actual maximum acceleration and a_max is the maximum constrained acceleration.

[0035] Specifically, obtain the maximum constrained acceleration a_max and the actual maximum acceleration a_realmax within the velocity range of at least one data source x, and calculate the acceleration index gain (1+(a_realmax-a_max) / a_realmax).

[0036] Step S303 involves calculating the gain values ​​of the acceleration index, acceleration time index, and steady-state time index, respectively, to obtain the acceleration index gain, acceleration time index gain, and steady-state time index gain. It also includes: calculating the acceleration time index gain based on a preset acceleration time index gain strategy, wherein the preset acceleration time index gain strategy is as follows: Acceleration time index gain = (1 + (Tm - T(m-1)) / T(m-1)); Where Tm is the acceleration time of the mth acceleration, and T(m-1) is the acceleration time of the (m-1)th acceleration.

[0037] Specifically, the acceleration time of the mth time is set as Tm, and the acceleration time of the (m-1)th time is set as T(m-1). The acceleration time index gain is calculated as (1+(Tm-T(m-1)) / T(m-1)).

[0038] Step S304 involves calculating the gain values ​​of the acceleration index, acceleration time index, and steady-state time index, respectively, to obtain the gain of the acceleration index, acceleration time index, and steady-state time index. This includes: calculating the gain of the steady-state time index based on a preset steady-state time index gain strategy, wherein the preset steady-state time index gain strategy is as follows: Steady-state time index gain = (1 + (tm - t(m-1)) / t(m-1)); Where tm is the m-th steady-state time, and t(m-1) is the (m-1)-th steady-state time.

[0039] Step S305: Obtain the comprehensive acceleration gain based on the acceleration index gain, acceleration time index gain, and steady-state time index gain, including: Comprehensive acceleration gain = Acceleration index gain * Acceleration time index gain * Steady-state time index gain.

[0040] Specifically, let the steady-state time of the data source x be tm and the steady-state time of the (m-1)th iteration be t(m-1). Calculate the steady-state time index gain as (1+(tm-t(m-1)) / t(m-1)).

[0041] Optionally, in some embodiments, after performing acceleration optimization based on the acceleration composite gain, the method further includes: obtaining the previous acceleration optimization result and the current acceleration optimization result; if the previous acceleration optimization result and the current acceleration optimization result are the same, then the current acceleration optimization result is used as calibration data for at least one data source.

[0042] Specifically, such as Figure 4 As shown in the embodiment of this application, at least one data source is obtained and input into the ACC functional model to obtain evaluation indicators. Based on these indicators, the acceleration is optimized to obtain the optimization result. The optimization precision is retained to two decimal places. Acceleration optimization for this data source ends when two consecutive accelerations are consistent, and its final value is used as the acceleration calibration data corresponding to that data source. Once all data sources have completed acceleration parameter optimization and the entire acceleration optimization and calibration process is complete, the corresponding acceleration can be retrieved for different acceleration processes by looking up a table and substituting it into the ACC functional model.

[0043] According to the ACC data-driven optimization method proposed in this application, by acquiring at least one data source and inputting it into a preset ACC functional model, acceleration function evaluation indicators are obtained. The gain values ​​of acceleration, acceleration time, and steady-state time indicators are calculated respectively to obtain acceleration indicator gain, acceleration time indicator gain, and steady-state time indicator gain. Based on the acceleration indicator gain, acceleration time indicator gain, and steady-state time indicator gain, a comprehensive acceleration gain is obtained, and acceleration optimization is performed based on the comprehensive acceleration gain. This solves the problem in related technologies where ACC acceleration calibration and optimization data acquisition is cumbersome and cannot cover all scenarios. By adopting a data-driven approach for optimization and parameter tuning, it covers most operating conditions. It automatically associates data and automatically tunes parameters based on evaluation indicators, eliminating the need for extensive testing to acquire data and greatly saving optimization time.

[0044] Next, the ACC data-driven optimization apparatus proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0045] Figure 5 This is a block diagram of the ACC data-driven optimization device according to an embodiment of this application.

[0046] like Figure 5 As shown, the ACC data-driven optimization device 10 includes: an acquisition module 100, an input module 200, and an optimization module 300.

[0047] The acquisition module 100 is used to acquire at least one data source.

[0048] The input module 200 is used to input at least one data source into the preset ACC function model to obtain acceleration function evaluation indicators, wherein the acceleration function evaluation indicators include acceleration indicators, acceleration time indicators and steady-state time indicators.

[0049] The optimization module 300 is used to calculate the gain values ​​of the acceleration index, acceleration time index, and steady-state time index respectively, to obtain the acceleration index gain, acceleration time index gain, and steady-state time index gain, and to obtain the comprehensive acceleration gain based on the acceleration index gain, acceleration optimization is performed based on the comprehensive acceleration gain.

[0050] Optionally, in some embodiments, after acceleration optimization based on the acceleration composite gain, the optimization module 300 further includes an acquisition unit and a calibration unit.

[0051] The acquisition unit is used to acquire the previous acceleration optimization result and the current acceleration optimization result.

[0052] The calibration unit is used to use the current acceleration optimization result as calibration data from at least one data source when the previous acceleration optimization result and the current acceleration optimization result are the same.

[0053] Optionally, in some embodiments, the optimization module 300 includes: a first computing unit.

[0054] The first calculation unit is used to calculate the index gain of the acceleration index based on a preset acceleration index gain strategy, thereby obtaining the acceleration index gain. The preset acceleration index gain strategy is as follows: Acceleration index gain = (1 + (a_realmax - a_max) / a_realmax); Where a_realmax is the actual maximum acceleration and a_max is the maximum constrained acceleration.

[0055] Optionally, in some embodiments, the optimization module 300 further includes a second computing unit.

[0056] The second calculation unit is used to calculate the gain of the acceleration time index based on a preset acceleration time index gain strategy, thereby obtaining the acceleration time index gain. The preset acceleration time index gain strategy is as follows: Acceleration time index gain = (1 + (Tm - T(m-1)) / T(m-1)); Where Tm is the acceleration time of the mth acceleration, and T(m-1) is the acceleration time of the (m-1)th acceleration.

[0057] Optionally, in some embodiments, the optimization module 300 includes a third computing unit.

[0058] The third calculation unit is used to calculate the steady-state time index gain based on a preset steady-state time index gain strategy, thereby obtaining the steady-state time index gain. The preset steady-state time index gain strategy is as follows: Steady-state time index gain = (1 + (tm - t(m-1)) / t(m-1)) Where tm is the m-th steady-state time, and t(m-1) is the (m-1)-th steady-state time.

[0059] Optionally, in some embodiments, the optimization module 300 is specifically used for: Acceleration composite gain = Acceleration index gain * Acceleration time index gain * Steady-state time index gain.

[0060] It should be noted that the foregoing explanation of the ACC data-driven optimization method embodiment also applies to the ACC data-driven optimization device of this embodiment, and will not be repeated here.

[0061] According to the ACC data-driven optimization device proposed in this application embodiment, by acquiring at least one data source and inputting it into a preset ACC functional model, acceleration function evaluation indicators are obtained. The gain values ​​of acceleration indicators, acceleration time indicators, and steady-state time indicators are calculated respectively to obtain acceleration indicator gain, acceleration time indicator gain, and steady-state time indicator gain. Based on the acceleration indicator gain, acceleration time indicator gain, and steady-state time indicator gain, a comprehensive acceleration gain is obtained, and acceleration optimization is performed based on the comprehensive acceleration gain. This solves the problem in related technologies where ACC acceleration calibration and optimization data acquisition is cumbersome and cannot cover all scenarios. The device uses a data-driven approach for optimization and parameter tuning, covering most operating scenarios. It automatically associates data and automatically tunes parameters based on evaluation indicators, eliminating the need for extensive testing to acquire data and greatly saving optimization time.

[0062] Figure 6 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include: The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0063] When the processor 602 executes the program, it implements the ACC data-driven optimization method provided in the above embodiments.

[0064] Furthermore, the vehicle also includes: Communication interface 603 is used for communication between memory 601 and processor 602.

[0065] The memory 601 is used to store computer programs that can run on the processor 602.

[0066] The memory 601 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0067] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0068] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0069] The processor 602 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0070] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the ACC data-driven optimization method described above.

[0071] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0072] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0073] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0074] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0075] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0076] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. An ACC data-driven optimization method, characterized by, Includes the following steps: Obtain at least one data source; The at least one data source is input into a preset ACC function model to obtain acceleration function evaluation indicators, wherein the acceleration function evaluation indicators include acceleration indicators, acceleration time indicators, and steady-state time indicators; and The gain values ​​of the acceleration index, the acceleration time index, and the steady-state time index are calculated respectively to obtain the acceleration index gain, the acceleration time index gain, and the steady-state time index gain. The comprehensive acceleration gain is obtained based on the comprehensive acceleration gain. Acceleration optimization is performed based on the comprehensive acceleration gain. Based on a preset acceleration index gain strategy, the index gain of the acceleration index is calculated to obtain the acceleration index gain, wherein the preset acceleration index gain strategy is: Acceleration index gain = (1 + (a_realmax - a_max) / a_realmax); Where a_realmax is the actual maximum acceleration and a_max is the maximum constrained acceleration; Based on a preset acceleration time indicator gain strategy, the indicator gain of the acceleration time indicator is calculated to obtain the acceleration time indicator gain, wherein the preset acceleration time indicator gain strategy is: Acceleration time index gain = (1 + (Tm - T(m-1)) / T(m-1)); Where Tm is the time of the m-th acceleration, and T(m-1) is the time of the (m-1)-th acceleration; Based on a preset steady-state time index gain strategy, the index gain of the steady-state time index is calculated to obtain the steady-state time index gain, wherein the preset steady-state time index gain strategy is: Steady-state time index gain = (1 + (tm - t(m-1)) / t(m-1)); Where tm is the m-th steady-state time, and t(m-1) is the (m-1)-th steady-state time. The acceleration composite gain = acceleration index gain * acceleration time index gain * steady-state time index gain.

2. The method according to claim 1, characterized in that, After performing acceleration optimization based on the aforementioned acceleration composite gain, the process also includes: Get the previous acceleration optimization result and the current acceleration optimization result; If the previous acceleration optimization result is the same as the current acceleration optimization result, then the current acceleration optimization result is used as the calibration data of the at least one data source.

3. An ACC data-driven optimization device, characterized in that, include: The acquisition module is used to acquire at least one data source; An input module is used to input the at least one data source into a preset ACC function model to obtain acceleration function evaluation indicators, wherein the acceleration function evaluation indicators include acceleration indicators, acceleration time indicators, and steady-state time indicators; and The optimization module is used to calculate the gain values ​​of the acceleration index, the acceleration time index, and the steady-state time index respectively, to obtain the acceleration index gain, the acceleration time index gain, and the steady-state time index gain, and to obtain the comprehensive acceleration gain based on the acceleration index gain, the acceleration time index gain, and the steady-state time index gain, and to perform acceleration optimization based on the comprehensive acceleration gain. Based on a preset acceleration index gain strategy, the index gain of the acceleration index is calculated to obtain the acceleration index gain, wherein the preset acceleration index gain strategy is: Acceleration index gain = (1 + (a_realmax - a_max) / a_realmax); Where a_realmax is the actual maximum acceleration and a_max is the maximum constrained acceleration; Based on a preset acceleration time indicator gain strategy, the indicator gain of the acceleration time indicator is calculated to obtain the acceleration time indicator gain, wherein the preset acceleration time indicator gain strategy is: Acceleration time index gain = (1 + (Tm - T(m-1)) / T(m-1)); Where Tm is the time of the m-th acceleration, and T(m-1) is the time of the (m-1)-th acceleration; Based on a preset steady-state time index gain strategy, the index gain of the steady-state time index is calculated to obtain the steady-state time index gain, wherein the preset steady-state time index gain strategy is: Steady-state time index gain = (1 + (tm - t(m-1)) / t(m-1)); Where tm is the m-th steady-state time, and t(m-1) is the (m-1)-th steady-state time. The acceleration composite gain = acceleration index gain * acceleration time index gain * steady-state time index gain.

4. The apparatus according to claim 3, characterized in that, After performing acceleration optimization based on the comprehensive acceleration gain, the optimization module further includes: The acquisition unit is used to acquire the previous acceleration optimization result and the current acceleration optimization result; A calibration unit is used to use the current acceleration optimization result as calibration data of the at least one data source when the previous acceleration optimization result and the current acceleration optimization result are the same.

5. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the ACC data-driven optimization method as described in any one of claims 1-2.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the ACC data-driven optimization method as described in any one of claims 1-2.

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