Performance parameter adjustment method and device, vehicle and storage medium

Abstract

Embodiments of the present application provide a performance parameter adjustment method and device, a vehicle and a storage medium. The method comprises: obtaining a plurality of vehicle data meeting comfort and safety indicators in a preset scenario in a manual driving mode; obtaining a plurality of vehicle data meeting comfort and safety indicators in a preset scenario in an intelligent driving mode; determining, under each performance indicator, a difference coefficient of each kind of vehicle data in the intelligent driving mode relative to each kind of vehicle data in the manual driving mode; determining a plurality of first performance parameters based on a plurality of vehicle data generated by a plurality of vehicles included in a second vehicle cluster in the preset scenario in the manual driving mode; and adjusting the plurality of first performance parameters through the plurality of difference coefficients to obtain a plurality of second performance parameters in the preset scenario in the intelligent driving mode. The intelligent driving is performed using the second performance parameters adjusted according to the difference coefficients, so that the user experience is better.

Description

Technical Field

[0001] The present application relates to the automotive field, and specifically relates to a performance parameter adjustment method, device, vehicle, and readable storage medium. Background Art

[0002] Generally, when setting indicators related to autonomous driving, only a small number of users' driving habits may be collected offline as target performance indicators. Due to the small sample size, the confidence level of the conclusions drawn is low, which may result in the set target performance indicators not being in line with the usage habits of most users, resulting in a lower user experience. Summary of the Invention

[0003] In view of the above problems, the present application proposes a performance parameter adjustment method, device, vehicle and storage medium to improve the above problems.

[0004] In a first aspect, an embodiment of the present application provides a performance parameter adjustment method, the method comprising: obtaining a plurality of vehicle data that meet comfort and safety indicators under a preset scenario in a manual driving mode, the vehicle data being data corresponding to a performance indicator generated by vehicles included in a first vehicle cluster, the performance indicator being the name of a performance parameter, and each of the plurality of vehicle data comprising: driving data of the vehicle itself and relative position information between the vehicle itself and surrounding related vehicles, as well as driving data of surrounding related vehicles; obtaining a plurality of vehicle data that meet comfort and safety indicators under the preset scenario in an intelligent driving mode; under each performance indicator, determining the performance of each vehicle data in the intelligent driving mode relative to each vehicle data in the manual driving mode. a difference coefficient of vehicle data to obtain a plurality of difference coefficients corresponding to a plurality of performance indicators; a plurality of first performance parameters in the preset scenario in the manual driving mode are determined based on a plurality of vehicle data generated by a plurality of vehicles included in the second vehicle cluster in the preset scenario in the manual driving mode, the number of vehicles included in the second vehicle cluster being greater than the number of vehicles included in the first vehicle cluster; the plurality of first performance parameters are adjusted by the plurality of difference coefficients to obtain a plurality of second performance parameters in the preset scenario in the intelligent driving mode, the second performance parameters being used to characterize vehicle data that meets comfort and safety requirements in the preset scenario, the second performance parameters being parameters obtained by adjusting the first performance parameters by the difference coefficients.

[0005] In the second aspect, an embodiment of the present application provides a performance parameter adjustment device, which includes: a human driving data acquisition unit, an intelligent driving data acquisition unit, a difference coefficient determination unit, a first performance parameter determination unit, and a first performance parameter adjustment unit. Among them, the human driving data acquisition unit is used to obtain a plurality of vehicle data that meet the comfort and safety indicators under a preset scenario in manual driving mode, the vehicle data is data corresponding to the performance indicator generated by the vehicles included in the first vehicle cluster, the performance indicator is the name of the performance parameter, and each vehicle data in the plurality of vehicle data includes: the driving data of the vehicle itself and the relative position information between the vehicle itself and the surrounding related vehicles, as well as the driving data of the surrounding related vehicles; the intelligent driving data acquisition unit is used to obtain a plurality of vehicle data that meet the comfort and safety indicators under the preset scenario in intelligent driving mode; the difference coefficient determination unit is used to determine the difference coefficient of each vehicle data in the intelligent driving mode relative to each vehicle data in the manual driving mode under each performance indicator, so as to adjust the performance parameter of the vehicle. A plurality of difference coefficients corresponding to the plurality of performance indicators are obtained; a first performance parameter determination unit is used to determine a plurality of first performance parameters in the preset scenario in the manual driving mode based on a plurality of vehicle data generated by a plurality of vehicles included in a second vehicle cluster in the preset scenario in the manual driving mode, the number of vehicles included in the second vehicle cluster being greater than the number of vehicles included in the first vehicle cluster; a first performance parameter adjustment unit is used to adjust the plurality of first performance parameters by means of the plurality of difference coefficients to obtain a plurality of second performance parameters in the preset scenario in the intelligent driving mode, the second performance parameters being used to characterize vehicle data that meets comfort and safety requirements in the preset scenario, the second performance parameters being parameters obtained by adjusting the first performance parameters by means of the difference coefficients.

[0006] In a third aspect, an embodiment of the present application provides a vehicle comprising one or more processors and a memory; one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to execute the above-mentioned method.

[0007] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which program code is stored, wherein the above method is executed when the program code is run.

[0008] The present invention provides a performance parameter adjustment method, apparatus, vehicle, and storage medium. This method determines the difference coefficient corresponding to each performance indicator based on the driving differences between a first vehicle cluster in manual and intelligent driving modes, while satisfying safety and comfort indicators. The method then adjusts the first performance parameter of a second vehicle cluster under each performance indicator based on the difference coefficient, thereby enhancing the user experience when intelligent driving is performed based on multiple first performance parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A flow chart of a performance parameter adjustment method proposed in one embodiment of the present application is shown;

[0011] Figure 2 A flow chart of a performance parameter adjustment method proposed in another embodiment of the present application is shown;

[0012] Figure 3 A flow chart of a performance parameter adjustment method proposed in another embodiment of the present application is shown;

[0013] Figure 4 A flow chart of a performance parameter adjustment method proposed in another embodiment of the present application is shown;

[0014] Figure 5 A structural block diagram of a performance parameter adjustment method proposed in another embodiment of the present application is shown;

[0015] Figure 6 A structural block diagram of a vehicle for executing the performance parameter adjustment method of an embodiment of the present application in real time is shown;

[0016] Figure 7 A storage unit in real time of the present application is shown for storing or carrying program codes for implementing the performance parameter adjustment method according to an embodiment of the present application. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0018] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, and may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0019] In an embodiment of the present application, the inventor proposes a performance parameter adjustment method, device, vehicle and storage medium. In manual driving mode, a plurality of vehicle data that meet the comfort and safety indicators in a preset scenario are obtained, the vehicle data being data corresponding to the performance indicators generated by the vehicles included in the first vehicle cluster, the performance indicators being the names of the performance parameters, and each of the plurality of vehicle data including: the driving data of the vehicle itself and the relative position information between the vehicle itself and surrounding related vehicles, as well as the driving data of surrounding related vehicles; in intelligent driving mode, a plurality of vehicle data that meet the comfort and safety indicators in the preset scenario are obtained; under each performance indicator, the difference coefficient of each vehicle data in the intelligent driving mode relative to each vehicle data in the manual driving mode is determined to obtain a plurality of performance indicators. The method includes: determining a plurality of difference coefficients corresponding to performance indicators; determining a plurality of first performance parameters for the preset scenario in the manual driving mode based on a plurality of vehicle data generated by a plurality of vehicles included in a second vehicle cluster in the preset scenario in the manual driving mode, wherein the number of vehicles included in the second vehicle cluster is greater than the number of vehicles included in the first vehicle cluster; adjusting the plurality of first performance parameters using the plurality of difference coefficients to obtain a plurality of second performance parameters for the preset scenario in the intelligent driving mode, wherein the second performance parameters are used to characterize vehicle data that meets comfort and safety requirements in the preset scenario, and the second performance parameters are parameters obtained by adjusting the first performance parameters using the difference coefficients. A difference coefficient corresponding to each performance indicator is obtained by analyzing the driving differences of the first vehicle cluster in the manual driving mode and the intelligent driving mode while meeting safety and comfort indicators, and adjusting the first performance parameter of the second vehicle cluster under each performance indicator based on the difference coefficients, so that the user has a better experience when performing intelligent driving based on the plurality of first performance parameters.

[0020] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0021] See also Figure 1 , an embodiment of the present application provides a parameter adjustment method, the method comprising:

[0022] Step S110: Obtain multiple vehicle data that meet comfort and safety indicators under preset scenarios in manual driving mode, where the vehicle data is data corresponding to performance indicators generated by vehicles included in the first vehicle cluster, and the performance indicators are names of performance parameters. Each vehicle data in the multiple vehicle data includes: driving data of the vehicle itself and relative position information between the vehicle itself and surrounding related vehicles, as well as driving data of surrounding related vehicles.

[0023] In this embodiment of the present application, before acquiring data from multiple vehicles in manual driving mode, personnel first set up multiple preset scenarios, including following a vehicle, slowing down a preceding vehicle, and turning scenarios, which are not specifically limited here. Personnel then randomly select a preset number of user-driven vehicles offline, and these vehicles form a first vehicle cluster. A second vehicle cluster then operates multiple vehicles in the preset scenarios, collects driving data from a preset number of users in manual driving mode, and classifies this driving data according to multiple performance indicators, thereby generating multiple vehicle data sets.

[0024] Multiple performance indicators correspond to the preset driving scenario. For each performance indicator, the vehicle data corresponding to the first vehicle cluster under that performance indicator is determined. This vehicle data includes driving data of the vehicle itself, relative position information between the vehicle itself and surrounding vehicles, and driving data of surrounding vehicles. For example, if the preset scenario is a preceding vehicle deceleration scenario, the performance indicators under that scenario include deceleration timing, deceleration force, and safe distance after braking. The vehicle data under these indicators is obtained. Since the user manually controls the vehicle in the preset scenario, the performance parameters obtained by the user's driving are considered to meet the comfort and safety indicators for each user.

[0025] The driving modes in a preset driving scenario include a manual driving mode and an automatic driving mode. Second manual driving data corresponding to a preset number of users using the manual driving mode in the preset driving scenario and automatic driving data corresponding to a preset number of users using the automatic driving mode in the preset driving scenario are obtained. Because the preset driving scenario includes multiple performance indicators, by processing the second manual driving data and the automatic driving data, difference coefficients corresponding to each of the multiple performance indicators can be obtained, thereby obtaining multiple difference coefficients. A difference coefficient list is obtained based on the multiple difference coefficients. The difference coefficient is a coefficient representing the difference between the performance indicator data in the second manual driving data and the performance indicator data in the automatic driving data. The difference coefficient list is a list recording the multiple performance indicators and the difference coefficients corresponding to the multiple performance indicators. The second manual driving data is driving data generated by the second vehicle group performing manual driving operations in the preset driving scenario, and the automatic driving data is driving data generated by the second vehicle group performing automatic driving operations in the preset driving scenario.

[0026] Step S120: Obtaining a plurality of vehicle data that meet comfort and safety indicators under the preset scenario in the intelligent driving mode.

[0027] In an embodiment of the present application, multiple users are randomly selected offline to drive vehicles, and a preset number of vehicles driven by each user are used as a first vehicle cluster. When a second vehicle cluster performs intelligent driving operations in a preset scenario, the performance parameters corresponding to the initial multiple performance indicators may not necessarily meet the user's safety or comfort expectations. Therefore, after each completion of the preset scenario, the second vehicle cluster will request user feedback, thereby continuously adjusting the multiple performance parameters until the user's safety and comfort expectations are met. The user feedback determines the collection of driving data for the second vehicle cluster in the preset scenario. When the user's safety and comfort expectations are met, the second vehicle cluster's driving data for the preset scenario is collected in real time using sensors. The collected driving data is then classified to obtain multiple vehicle data corresponding to the multiple performance indicators in the preset scenario. The determined multiple vehicle data are then stored in a preset storage area. After the second vehicle cluster completes the preset scenario, multiple vehicle data that meet the comfort and safety indicators in the intelligent driving mode are retrieved from the preset storage area.

[0028] Step S130: Under each performance indicator, determining a difference coefficient between each vehicle data in the intelligent driving mode and each vehicle data in the manual driving mode, to obtain a plurality of difference coefficients corresponding to a plurality of performance indicators.

[0029] In the embodiments of the present application, under each performance indicator, the vehicle data corresponding to the performance indicator in the intelligent driving mode and the vehicle data corresponding to the performance indicator in the manual driving mode are compared to determine the difference coefficient corresponding to the performance indicator, thereby obtaining a plurality of difference coefficients corresponding to a plurality of performance indicators. The difference coefficient is a coefficient representing the difference between the vehicle data in the manual driving mode and the vehicle data in the intelligent driving mode.

[0030] Step S140: based on the plurality of vehicle data generated by the plurality of vehicles included in the second vehicle cluster in the manual driving mode in the preset scene, a plurality of first performance parameters in the preset scene in the manual driving mode are determined, and the number of vehicles included in the second vehicle cluster is greater than the number of vehicles included in the first vehicle cluster.

[0031] In the embodiments of the present application, the driving data of the plurality of vehicles included in the second vehicle cluster performing the manual driving mode is obtained from the preset storage area, and the plurality of driving data is classified according to the plurality of performance indicators in the preset scene, thereby obtaining the plurality of vehicle data. The preset storage area can be a database and a cloud platform, which is not limited here.

[0032] After determining the vehicle data corresponding to each of the plurality of performance indicators, since the vehicle data is the data collected when driving in the preset scene, and from starting to drive in the preset scene to ending to drive in the preset scene, a certain time is required, so the driving data corresponding to the preset driving scene collected is a time domain data. When the driving data corresponding to each of the plurality of performance indicators is transformed, a first reference performance parameter corresponding to each of the plurality of performance indicators is obtained, the first reference performance parameter is also a time domain data, that is, one performance indicator corresponds to at least one first reference performance parameter, and the plurality of first reference performance parameters corresponding to the performance indicator change in the preset driving time corresponding to the preset scene. When calculating the reference performance indicator value corresponding to one performance indicator of the second vehicle cluster in the preset scene, first, for one vehicle in the first vehicle cluster, the plurality of first reference performance parameters corresponding to the performance indicator of the vehicle are obtained, and the preset indicator driving time corresponding to the performance indicator is determined, so that the mean value of the plurality of first reference performance parameters of the performance indicator of the vehicle can be calculated, and the mean value is taken as the first reference performance parameter mean value of the vehicle in the performance indicator. By repeating the above calculation process, the first reference performance parameter mean value of each of the plurality of vehicles included in the second vehicle cluster in the performance indicator can be obtained, that is, the plurality of first reference performance parameter mean values of the second vehicle cluster in the performance indicator are obtained. The mean value of the plurality of reference performance parameter mean values is calculated, thereby obtaining the first performance parameter corresponding to the performance indicator in the preset scene. By repeating the above calculation process, the first performance parameter corresponding to each of the plurality of performance indicators in the preset scene can be obtained.

[0033] Among them, the purpose of the present scheme is to represent the difference between the performance parameters of manual driving and intelligent driving through the difference of a small number of sampled users, and to generate performance parameters that can cover most customers through massive driving data obtained by big data, therefore, the number of vehicles included in the second vehicle cluster is much larger than the number of vehicles included in the first vehicle cluster, for example, the first vehicle cluster can be set to 40 vehicles, and the second vehicle cluster can be set to 100,000 vehicles.

[0034] As a way, a driving scene database can be set in the system of the vehicle in advance, the driving scene database includes a plurality of preset scene corresponding scene data storage areas, each scene data storage area includes a manual driving scene data storage area and an intelligent driving scene data storage area, the manual driving scene data storage area includes a manual driving performance data storage area corresponding to the performance indicators of the preset scene, the intelligent driving scene data storage area includes an intelligent driving performance data storage area corresponding to the performance indicators of the preset scene, and each manual driving scene storage area includes at least one manual driving performance data storage area, and each intelligent driving scene storage area includes at least one automatic driving performance data storage area. When the vehicle drives, the driving condition of the vehicle is monitored in real time, as long as it is monitored that the vehicle is in a preset scene, the driving data corresponding to the preset scene is stored in the scene data storage area corresponding to the driving scene in real time, and at the same time, as long as the vehicle executes the action corresponding to the performance indicators, the vehicle data corresponding to the performance indicators is stored in the performance data storage area corresponding to the performance indicators. When driving data is needed, manual driving data in the manual driving performance data storage area included in the manual driving scene data storage area corresponding to the preset driving scene and intelligent driving data in the intelligent driving performance data storage area included in the intelligent driving scene data storage area can be obtained. By pre-setting the area of manual driving data storage and the area of intelligent driving data storage, and dividing the area corresponding to the data of multiple performance indicators in the area of manual driving data storage and the area corresponding to the data of multiple performance indicators in the area of intelligent driving data storage, when multiple performance indicators corresponding to the performance indicators are needed, the driving data does not need to be classified, but can be directly obtained from the storage area corresponding to the performance indicators.

[0035] Step S150: Adjust the multiple first performance parameters using the multiple difference coefficients to obtain multiple second performance parameters under the preset scenario in the intelligent driving mode. The second performance parameters are used to characterize vehicle data that meets comfort and safety requirements under the preset scenario. The second performance parameters are parameters obtained by adjusting the first performance parameters using the difference coefficients.

[0036] In an embodiment of the present application, after determining multiple difference coefficients corresponding to multiple performance indicators, for each performance indicator, the difference coefficient corresponding to the performance indicator is multiplied by the first performance parameter corresponding to the performance indicator to complete the adjustment of the first performance parameter and obtain the second performance parameter corresponding to the performance indicator. The above process is repeated to obtain multiple second performance parameters corresponding to the multiple performance indicators. The obtained second performance parameters are used as multiple second performance parameters for the preset scenario when executing the intelligent driving mode, so that when the intelligent driving mode is actually used and in the preset scenario, driving is performed according to the multiple second performance parameters. For example, if the performance indicator is following distance, the value of the first performance parameter corresponding to the following distance is determined to be 5 meters, and the difference coefficient corresponding to the following distance is determined to be 1.2, then the second performance parameter corresponding to the following distance is determined to be 7 meters.

[0037] An embodiment of the present application provides a parameter adjustment method. By determining the driving differences between a first vehicle cluster in a manual driving mode and an intelligent driving mode while satisfying safety and comfort indicators, a difference coefficient corresponding to each performance indicator is obtained. The first performance parameter of a second vehicle cluster under each performance indicator is adjusted according to the difference coefficient, thereby providing a better user experience when performing intelligent driving based on multiple first performance parameters.

[0038] See also Figure 2 , an embodiment of the present application provides a parameter adjustment method, the method comprising:

[0039] Step S201: Obtain multiple vehicle data that meet comfort and safety indicators under preset scenarios in manual driving mode, where the vehicle data is data corresponding to performance indicators generated by vehicles included in the first vehicle cluster, and the performance indicators are names of performance parameters. Each vehicle data in the multiple vehicle data includes: driving data of the vehicle itself and relative position information between the vehicle itself and surrounding related vehicles, as well as driving data of surrounding related vehicles.

[0040] For details of step S201 , please refer to the detailed explanation in the above embodiment, so it will not be described in detail in this embodiment.

[0041] Step S202: Under the preset scenario, obtain a plurality of initial vehicle data in the intelligent driving mode.

[0042] In an embodiment of the present application, under a preset scenario, multiple vehicles included in the first vehicle cluster execute an intelligent driving mode, thereby obtaining initial vehicle data corresponding to the first vehicle cluster, and the initial vehicle data is classified according to multiple performance indicators under the preset scenario to obtain multiple initial vehicle data corresponding to multiple performance indicators, wherein one performance indicator corresponds to one initial vehicle data.

[0043] Step S203: Based on the driving data of the vehicle itself and the relative position information between the vehicle itself and surrounding related vehicles, as well as the driving data of surrounding related vehicles included in the multiple initial vehicle data, determine multiple initial performance parameters corresponding to the multiple initial vehicle data, one initial vehicle data corresponds to one initial performance parameter.

[0044] In an embodiment of the present application, for vehicle data corresponding to a performance indicator, the initial performance parameters corresponding to the performance indicator are calculated based on the driving data of the vehicle itself and the relative position information between the vehicle itself and surrounding related vehicles included in the vehicle data, as well as the driving data of surrounding related vehicles, thereby obtaining multiple initial performance parameters corresponding to multiple performance indicators.

[0045] Step S204: If it is determined based on the user feedback information that the multiple initial performance parameters do not meet the comfort and safety indicators, the multiple initial performance parameters are adjusted multiple times until the adjusted multiple initial performance parameters meet the comfort and safety indicators.

[0046] In an embodiment of the present application, when multiple initial performance parameters are determined, it indicates that the first vehicle cluster has completed driving in the preset scenario. At this point, an engineer receives user feedback. If the feedback indicates that the multiple initial performance parameters do not meet comfort and safety criteria, the engineer adjusts the multiple initial performance parameters and then allows the first vehicle cluster to drive in the preset scenario based on the adjusted multiple initial performance parameters. After the first vehicle cluster completes the preset scenario again, the engineer again receives user feedback. If the feedback still indicates that the multiple adjusted initial performance parameters do not meet comfort and safety criteria, the multiple adjusted initial performance parameters are further adjusted based on the feedback until the final, revised multiple initial performance parameters, as determined based on the user feedback, meet comfort and safety criteria.

[0047] Step S205: Acquire a plurality of vehicle data corresponding to the plurality of initial performance indicators that meet the comfort and safety indicators.

[0048] In the embodiment of the present application, after determining the multiple final revised initial performance parameters that meet the comfort and safety indicators, multiple vehicle data corresponding to the multiple final revised initial performance parameters are obtained.

[0049] Step S206: Under each performance indicator, based on each type of vehicle data under the intelligent driving mode, determine the intelligent driving performance parameter to be processed corresponding to the vehicle data. One type of vehicle data under the intelligent driving mode corresponds to one intelligent driving performance parameter to be processed, and one performance indicator corresponds to one intelligent driving performance parameter to be processed.

[0050] In an embodiment of the present application, for a performance indicator, in the intelligent driving mode, the vehicle data corresponding to the performance indicator is determined, and the vehicle data is processed to obtain the to-be-processed intelligent driving performance parameters corresponding to the performance indicator, thereby obtaining the to-be-processed intelligent driving performance parameters corresponding to each type of vehicle data in the intelligent driving mode.

[0051] Step S207: Under each performance indicator, based on each type of vehicle data in the manual driving mode, determine the human driving performance parameter to be processed corresponding to the vehicle data, one type of vehicle data in the manual driving mode corresponds to one human driving performance parameter to be processed, and one performance indicator corresponds to one human driving performance parameter to be processed.

[0052] In an embodiment of the present application, for a performance indicator, in the manual driving mode, the vehicle data corresponding to the performance indicator is determined, and after processing the vehicle data, the human driving performance parameters to be processed corresponding to the performance indicator are obtained, thereby obtaining the human driving performance parameters to be processed corresponding to each type of vehicle data in the manual driving mode.

[0053] Step S208: Based on the to-be-processed intelligent driving performance parameters corresponding to each type of vehicle data in the intelligent driving mode, and the to-be-processed human driving performance parameters corresponding to each type of vehicle data in the manual driving mode, determine the difference coefficient of each vehicle data in the intelligent driving mode relative to each vehicle data in the manual driving mode, so as to obtain multiple difference coefficients corresponding to multiple performance indicators.

[0054] In an embodiment of the present application, after determining the multiple unprocessed intelligent driving performance parameters generated when the first vehicle cluster executes the intelligent driving mode under the preset scenario, and the multiple unprocessed human driving performance parameters generated when the first vehicle cluster executes the manual driving mode, since the user's risk perception when executing the manual driving operation and the intelligent driving operation is different, generally speaking, the user prefers the manual driving operation to be safer, while the intelligent driving operation has a higher uncertainty. This difference in risk perception will be reflected in the performance parameters corresponding to the performance indicators during the specific operation. Therefore, for the same performance indicator, the unprocessed human driving performance parameters and the unprocessed intelligent driving performance parameters corresponding to the performance indicator are generally different. For the difference coefficient corresponding to a performance indicator, it can be obtained by dividing the unprocessed intelligent driving performance parameters corresponding to the performance indicator by the unprocessed human driving performance parameters, and then the difference coefficients corresponding to each of the multiple performance indicators can be obtained. Among them, the difference coefficient list is the difference between the unprocessed human driving performance parameters and the unprocessed intelligent driving performance parameters, which can reflect the difference in risk perception between the user's performance indicators in the preset scenario when executing manual driving operations and intelligent driving operations.

[0055] For example, if the preset scenario is a following vehicle scenario and the performance indicator is the following distance, when the first vehicle cluster performs manual driving operations to follow the vehicle, it is believed that maintaining a distance of 5 meters from the vehicle in front is a relatively safe following distance. In this case, the corresponding human driving performance parameter to be processed is determined to be 5 meters; if the first vehicle cluster performs intelligent driving operations to follow the vehicle, the system believes that maintaining a distance of 7 meters from the vehicle in front is a relatively safe following distance. In this case, the corresponding intelligent driving performance parameter to be processed is determined to be 7 meters, and the final difference coefficient corresponding to the following distance is determined to be 7 / 5=1.2.

[0056] Step S209: Based on the multiple vehicle data generated by the multiple vehicles included in the second vehicle cluster under the preset scenario in the manual driving mode, determine the multiple first performance parameters under the preset scenario in the manual driving mode, and the number of vehicles included in the second vehicle cluster is greater than the number of vehicles included in the first vehicle cluster.

[0057] Step S210: Adjust the multiple first performance parameters using the multiple difference coefficients to obtain multiple second performance parameters under the preset scenario in the intelligent driving mode. The second performance parameters are used to characterize vehicle data that meets comfort and safety requirements under the preset scenario. The second performance parameters are parameters obtained by adjusting the first performance parameters using the difference coefficients.

[0058] For details of steps S209 to S210, please refer to the detailed explanation in the above embodiment, so they will not be described in detail in this embodiment.

[0059] Step S211: Controlling the vehicle to execute the intelligent driving mode in the preset scenario based on the multiple second performance parameters during actual use.

[0060] In the embodiment of the present application, after determining multiple second performance parameters, when a subsequent vehicle activates intelligent driving, the intelligent driving operation can be executed based on the multiple performance parameters. For example, if the second performance parameter corresponding to the following distance is determined to be 7 meters, then when the vehicle is in autonomous driving mode, it will maintain a following distance of 7 meters from the vehicle in front.

[0061] An embodiment of the present application provides a reference adjustment method, which obtains a difference coefficient corresponding to each performance indicator by calculating the driving difference between a first vehicle cluster in a manual driving mode and an intelligent driving mode while satisfying safety and comfort indicators. The first performance parameter of the second vehicle cluster under each performance indicator is adjusted according to the difference coefficient, so that users have a better experience when performing intelligent driving based on multiple first performance parameters.

[0062] See also Figure 3 , an embodiment of the present application provides a parameter adjustment method, the method comprising:

[0063] Step S301: Obtain multiple vehicle data that meet comfort and safety indicators under preset scenarios in manual driving mode, where the vehicle data is data corresponding to performance indicators generated by vehicles included in the first vehicle cluster, and the performance indicators are the names of performance parameters. Each vehicle data in the multiple vehicle data includes: driving data of the vehicle itself and relative position information between the vehicle itself and surrounding related vehicles, as well as driving data of surrounding related vehicles.

[0064] Step S302: Under the preset scenario, obtain a plurality of initial vehicle data in the intelligent driving mode.

[0065] Step S303: Based on the driving data of the vehicle itself and the relative position information between the vehicle itself and surrounding related vehicles, as well as the driving data of surrounding related vehicles included in the multiple initial vehicle data, determine multiple initial performance parameters corresponding to the multiple initial vehicle data, one initial vehicle data corresponds to one initial performance parameter.

[0066] Step S304: If it is determined based on user feedback information that the multiple initial performance parameters do not meet the comfort and safety indicators, the multiple initial performance parameters are corrected multiple times until the adjusted multiple initial performance parameters meet the comfort and safety indicators, and the corrected multiple initial performance parameters that meet the comfort and safety indicators are used as multiple reference performance parameters.

[0067] Step S305: Acquire a plurality of vehicle data corresponding to the plurality of reference performance parameters.

[0068] Step S306: Based on each type of vehicle data in the intelligent driving mode, determine multiple performance parameters of the multiple vehicles included in the first vehicle cluster under each performance indicator, wherein one vehicle corresponds to one performance parameter.

[0069] In an embodiment of the present application, after obtaining multiple vehicle data obtained after the first vehicle cluster executes the intelligent driving mode, for one vehicle data, the vehicle data is converted to obtain multiple performance parameters of the multiple vehicles included in the first vehicle cluster under the performance indicators corresponding to the vehicle data, so that multiple performance parameters of the multiple vehicles included in the first vehicle cluster under the performance indicators corresponding to each vehicle data can be obtained.

[0070] Step S307: Calculate the mean of multiple performance parameters corresponding to the multiple vehicles under each performance indicator, and use the mean as the unprocessed intelligent driving performance parameter corresponding to the vehicle data to obtain the unprocessed intelligent driving performance parameter corresponding to each vehicle data under the intelligent driving mode.

[0071] In an embodiment of the present application, after obtaining multiple performance parameters corresponding to multiple vehicles included in the first vehicle cluster under each performance indicator, the average of the multiple performance parameters corresponding to the multiple vehicles under one performance indicator is calculated, and the calculated average is used as the to-be-processed intelligent driving performance parameter of the vehicle data corresponding to the performance indicator, thereby obtaining the to-be-processed intelligent driving performance parameter corresponding to each vehicle data under the intelligent driving mode.

[0072] For example, consider the following distance performance metric. The first vehicle cluster includes 40 vehicles. Due to the varying driving habits of each user, engineers set a different following distance for each user when executing the intelligent driving mode. After determining the following distances for all vehicles based on the vehicle data, the average of these distances is used as the corresponding intelligent driving performance parameter to be processed.

[0073] Step S308: Based on each vehicle data in the manual driving mode, determine a plurality of performance parameters of the plurality of vehicles included in the first vehicle cluster under each performance indicator, wherein one vehicle corresponds to one performance parameter.

[0074] In an embodiment of the present application, after obtaining multiple vehicle data obtained after the first vehicle cluster executes the manual driving mode, for one vehicle data, the vehicle data is converted to obtain multiple performance parameters of the multiple vehicles included in the first vehicle cluster under the performance indicators corresponding to the vehicle data, so that multiple performance parameters of the multiple vehicles included in the first vehicle cluster under the performance indicators corresponding to each vehicle data can be obtained.

[0075] Step S309: Calculate the mean of the multiple performance parameters corresponding to the multiple vehicles under each performance indicator, and use the mean as the to-be-processed human driving performance parameter corresponding to the vehicle data to obtain the to-be-processed human driving performance parameter corresponding to each vehicle data in the manual driving mode.

[0076] In an embodiment of the present application, after obtaining the performance parameters corresponding to multiple vehicles included in the first vehicle cluster under each performance indicator, the average of the multiple performance parameters corresponding to multiple vehicles under one performance indicator is calculated, and the calculated average is used as the human driving performance parameter to be processed for the vehicle data corresponding to the performance indicator, so that the human driving performance parameter to be processed corresponding to each vehicle data in the manual driving mode can be obtained.

[0077] For example, consider the performance indicator of following distance. The first vehicle cluster includes 40 vehicles. Due to varying driving habits, each user's corresponding following distance in manual driving mode varies. After determining the following distances for all vehicles based on vehicle data, the average of these distances is used as the human driving performance parameter to be processed.

[0078] Step S310: Based on the to-be-processed intelligent driving performance parameters corresponding to each type of vehicle data in the intelligent driving mode, and the to-be-processed human driving performance parameters corresponding to each type of vehicle data in the manual driving mode, determine the difference coefficient of each vehicle data in the intelligent driving mode relative to each vehicle data in the manual driving mode, so as to obtain multiple difference coefficients corresponding to multiple performance indicators.

[0079] Step S311: Based on the multiple vehicle data generated by the multiple vehicles included in the second vehicle cluster under the preset scenario in the manual driving mode, determine the multiple first performance parameters under the preset scenario in the manual driving mode, and the number of vehicles included in the second vehicle cluster is greater than the number of vehicles included in the first vehicle cluster.

[0080] Step S312: Adjust the multiple first performance parameters using the multiple difference coefficients to obtain multiple second performance parameters under the preset scenario in the intelligent driving mode. The second performance parameters are used to characterize vehicle data that meets comfort and safety requirements under the preset scenario. The second performance parameters are parameters obtained by adjusting the first performance parameters using the difference coefficients.

[0081] Step S313: Controlling the vehicle to execute the intelligent driving mode in the preset scenario based on the multiple second performance parameters during actual use.

[0082] For details of steps S310 to S313 , please refer to the detailed explanation in the above embodiment, so they will not be described in detail in this embodiment.

[0083] Step S314: receiving feedback information generated by the user executing the intelligent driving mode in the preset scenario.

[0084] In an embodiment of the present application, while a user is using the intelligent driving mode, if it is detected that the user is in a preset scenario and the intelligent driving mode is switched to the manual driving mode, it is determined that the user may be dissatisfied with multiple second performance parameters corresponding to the preset scenario. The electronic device then monitors the performance parameters corresponding to the performance indicators of the preset scenario after the user switches to the manual driving mode, and uses the monitored performance parameters as feedback information. For example, if a user is executing an automatic driving mode in a following vehicle scenario and sets the following distance to 5 meters, and the user believes that the following distance is too close and unsafe, the user switches the intelligent driving mode to the manual driving mode, and the electronic device monitors the following distance after the user switches to the manual driving mode. The electronic device uses the following distance and the following distance after the user switches to the manual mode as feedback information.

[0085] As a method, when a user is dissatisfied with a set second performance parameter, they may call after-sales personnel and report their dissatisfaction with the performance parameter corresponding to the specific performance indicator. After-sales personnel will record the performance parameter corresponding to the user's feedback as feedback information. For example, if a user calls after-sales personnel to report that the following distance in a following vehicle scenario is too close, the after-sales personnel will record the value corresponding to the following distance as feedback information.

[0086] Step S315: If the amount of the feedback information exceeds a preset threshold, modify the plurality of second performance parameters based on the feedback information.

[0087] In an embodiment of the present application, if the number of received feedback messages exceeds a preset threshold, a performance indicator corresponding to the received feedback messages is determined, and a second performance parameter corresponding to the performance indicator is corrected. For example, if the preset threshold is set to 50,000, and 100,000 pieces of feedback are received for following distance, and it is determined that the number of feedback messages exceeds the preset threshold, then it is determined that, out of the 100,000 pieces of feedback, 30,000 pieces of feedback determine that the value of the following distance after manual operation is smaller than the set following distance, and serve as feedback information that is too small. 70,000 pieces of feedback determine that the value of the following distance after manual operation is larger than the set following distance, and serve as feedback information that is too large. The feedback information that is too small is discarded, and the values ​​of the following distance determined in the feedback information that is too large are calculated to obtain an average value, which is used as the performance parameter after the correction of the following distance.

[0088] Alternatively, the proportion of feedback information exceeding a preset threshold may be used as a prerequisite for modifying multiple second performance parameters. For example, if the preset threshold is set at 5%, and the total number of users is 1 million, and the number of feedback messages sent by the same user is determined to be 100,000, then the proportion of feedback information is determined to be 10%. If this exceeds the preset threshold, the second performance parameters may be modified based on the feedback information.

[0089] As another approach, if the amount of feedback information does not exceed a preset threshold, the plurality of second performance parameters are not revised.

[0090] For example, steps S301 to S315 may be as follows: Figure 4 As shown, a performance scenario library is pre-built, which includes multiple preset scenarios and performance indicators corresponding to each of the preset scenarios. The performance scenario library is then placed in the data module. At the same time, the input module obtains multiple vehicle data of the second vehicle cluster and determines the first performance parameter corresponding to each vehicle data based on the performance indicator corresponding to each vehicle data. The first performance parameter is then input into the correction module. In the correction module, multiple vehicle data corresponding to the offline test of the first vehicle cluster when executing the intelligent driving mode under the preset scenario and multiple vehicle data corresponding to the manual driving mode are pre-stored. Under each performance indicator, the vehicle data of the intelligent driving mode corresponding to the performance indicator is converted into the intelligent driving performance parameter to be processed, and the vehicle data of the manual driving mode corresponding to the performance indicator is converted into the human driving performance parameter to be processed. The human driving performance parameter to be processed and the intelligent driving performance parameter to be processed under the performance indicator are compared to obtain the difference coefficient corresponding to the performance indicator, thereby obtaining the difference coefficient corresponding to each performance indicator. Each first performance parameter is corrected according to each difference coefficient to obtain the second performance parameter corresponding to each performance indicator. The target vehicle is controlled to perform the intelligent driving operation under the preset scenario based on the obtained second performance parameter. At the same time, feedback information sent by the user during the execution of the intelligent driving operation is received through the feedback module. If it is determined that the amount of feedback information exceeds a preset amount, the multiple second performance parameters are corrected again according to the feedback information.

[0091] A parameter adjustment method provided in an embodiment of the present application obtains a difference coefficient corresponding to each performance indicator based on the driving differences of a first vehicle cluster in a manual driving mode and an intelligent driving mode while satisfying safety and comfort indicators. The first performance parameter of a second vehicle cluster under each performance indicator is adjusted according to the difference coefficient, thereby providing a better user experience when performing intelligent driving based on multiple first performance parameters.

[0092] See also Figure 5, an embodiment of the present application provides a performance parameter adjustment device 400, the device 400 comprising:

[0093] A human driving data acquisition unit 410 is configured to obtain a plurality of vehicle data that meet comfort and safety indicators under preset scenarios in a manual driving mode. The vehicle data is data corresponding to performance indicators generated by vehicles included in the first vehicle cluster. The performance indicators are the names of performance parameters. Each of the plurality of vehicle data includes: driving data of the vehicle itself, relative position information between the vehicle itself and surrounding vehicles, and driving data of surrounding vehicles.

[0094] The intelligent driving data acquisition unit 420 is used to obtain multiple vehicle data that meet the comfort and safety indicators in the preset scenario when in the intelligent driving mode.

[0095] As a method, the intelligent driving data acquisition unit 420 is also used to obtain multiple initial vehicle data under the intelligent driving mode in the preset scenario; based on the driving data of the vehicle itself and the relative position information between the vehicle itself and surrounding related vehicles, as well as the driving data of surrounding related vehicles included in the multiple initial vehicle data, determine multiple initial performance parameters corresponding to the multiple initial vehicle data, and one initial vehicle data corresponds to one initial performance parameter; if it is determined based on user feedback information that the multiple initial performance parameters do not meet the comfort and safety indicators, the multiple initial performance parameters are corrected multiple times until the corrected multiple initial performance parameters meet the comfort and safety indicators, and the corrected multiple initial performance parameters that meet the comfort and safety indicators are used as multiple reference performance parameters; and obtain multiple vehicle data corresponding to the multiple reference performance parameters.

[0096] The difference coefficient determination unit 430 is used to determine the difference coefficient of each vehicle data in the intelligent driving mode relative to each vehicle data in the manual driving mode under each performance indicator, so as to obtain multiple difference coefficients corresponding to multiple performance indicators.

[0097] As a method, the difference coefficient determination unit 430 is also used to determine the to-be-processed intelligent driving performance parameters corresponding to the vehicle data under each performance indicator based on each vehicle data in the intelligent driving mode, one vehicle data in the intelligent driving mode corresponds to one to-be-processed intelligent driving performance parameter, and one performance indicator corresponds to one to-be-processed intelligent driving performance parameter; under each performance indicator, determine the to-be-processed human driving performance parameters corresponding to the vehicle data based on each vehicle data in the manual driving mode, one vehicle data in the manual driving mode corresponds to one to-be-processed human driving performance parameter, and one performance indicator corresponds to one to-be-processed human driving performance parameter; based on the to-be-processed intelligent driving performance parameters corresponding to each vehicle data in the intelligent driving mode and the to-be-processed human driving performance parameters corresponding to each vehicle data in the manual driving mode, determine the difference coefficient of each vehicle data in the intelligent driving mode relative to each vehicle data in the manual driving mode to obtain multiple difference coefficients corresponding to multiple performance indicators.

[0098] Optionally, the difference coefficient determination unit 430 is also used to determine, based on each vehicle data under the intelligent driving mode, multiple performance parameters of the multiple vehicles included in the first vehicle cluster under each performance indicator, wherein one vehicle corresponds to one performance parameter; calculate the mean of the multiple performance parameters of the multiple vehicles under each performance indicator, and use the mean as the to-be-processed intelligent driving performance parameter corresponding to the vehicle data, so as to obtain the to-be-processed intelligent driving performance parameter corresponding to each vehicle data under the intelligent driving mode; based on each vehicle mode under the manual driving mode, determine the multiple performance parameters of the multiple vehicles included in the multiple vehicle clusters under each performance indicator, wherein one vehicle corresponds to one performance parameter; calculate the mean of the multiple performance parameters of the multiple vehicle data under each performance indicator, and use the mean as the to-be-processed human driving performance parameter corresponding to the vehicle data, so as to obtain the to-be-processed manual driving performance parameter corresponding to each vehicle data under the manual driving mode.

[0099] a first performance parameter determining unit 440 configured to determine a plurality of first performance parameters in the preset scenario in the manual driving mode based on a plurality of vehicle data generated by a plurality of vehicles included in a second vehicle cluster in the preset scenario in the manual driving mode, wherein the number of vehicles included in the second vehicle cluster is greater than the number of vehicles included in the first vehicle cluster;

[0100] The first performance parameter adjustment unit 450 is used to adjust the multiple first performance parameters through the multiple difference coefficients to obtain multiple second performance parameters under the preset scenario in the intelligent driving mode. The second performance parameters are used to characterize vehicle data that meets comfort and safety requirements under the preset scenario. The second performance parameters are the parameters obtained by adjusting the first performance parameters through the difference coefficients.

[0101] As a manner, the first performance parameter adjustment unit 450 is further configured to control the vehicle to perform the intelligent driving mode in the preset scene based on the plurality of second performance parameters in actual use.

[0102] Optionally, the first performance parameter adjustment unit 450 is further configured to receive feedback information generated by a user in the preset scene when performing the intelligent driving mode, and correct the plurality of second performance parameters based on the feedback information if the number of the feedback information exceeds a preset threshold.

[0103] It should be noted that the device embodiments in the present application correspond to the foregoing method embodiments, and the specific principles of the device embodiments can be referred to the content in the foregoing method embodiments, which will not be described here.

[0104] The following will be described in combination with Figure 6 A vehicle provided by the present application will be described.

[0105] Please refer to Figure 6 Based on the foregoing data processing method and device, the present application further provides another vehicle 500 which can perform the foregoing data processing method. The vehicle 500 comprises one or more (only one is shown in the figure) processors 502, a memory 504 and a network module 506 which are coupled with each other. The memory 504 stores a program which can perform the content in the foregoing embodiments, and the processor 502 can execute the program stored in the memory 504.

[0106] The processor 502 can include one or more processing cores. The processor 502 connects various parts within the vehicle 500 through various interfaces and lines, performs various functions of the vehicle 500 and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 504, and calling data stored in the memory 504. Alternatively, the processor 502 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 502 can be integrated with a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU is mainly responsible for processing operating systems, user interfaces, and application programs; the GPU is responsible for rendering and drawing display content; and the modem is responsible for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 502, but can be implemented by a separate communication chip.

[0107] The memory 504 can include a random access memory (RAM) and can also include a read-only memory (ROM). The memory 504 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 504 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing each of the methods described below, etc. The data storage area can also store data created by the electronic device 500 in use (such as a phone book, audio and video data, chat record data, etc.).

[0108] The network module 506 is used to receive and transmit electromagnetic waves, realize the mutual conversion between electromagnetic waves and electrical signals, and thus communicate with a communication network or other devices, such as communicating with an audio playback device. The network module 506 may include various existing circuit components for performing these functions, such as an antenna, a radio frequency transceiver, a digital signal processor, an encryption / decryption chip, a subscriber identity module (SIM) card, a memory, etc. The network module 506 can communicate with various networks such as the Internet, an intranet, a wireless network, or communicate with other devices via a wireless network. The above-mentioned wireless network may include a cellular telephone network, a wireless local area network, or a metropolitan area network. For example, the network module 506 can exchange information with a base station.

[0109] Please refer to Figure 7 , which shows a block diagram of a computer-readable storage medium provided in an embodiment of the present application. The computer-readable storage medium 600 stores program code, which can be called by a processor to execute the method described in the above method embodiment.

[0110] The computer-readable storage medium 600 can be an electronic memory such as a flash memory, an EEPROM (Electrically Erasable Programmable Read-Only Memory), an EPROM, a hard disk, or a ROM. Alternatively, the computer-readable storage medium 600 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 600 has storage space for program code 610 for executing any of the method steps described above. These program codes can be read from or written to one or more computer program products. The program code 610 can be compressed, for example, in a suitable form.

[0111] Embodiments of the present application provide a performance parameter adjustment method, device, vehicle, and storage medium. The method includes: obtaining multiple vehicle data that meet comfort and safety indicators under preset scenarios in manual driving mode; obtaining multiple vehicle data that meet comfort and safety indicators under preset scenarios in intelligent driving mode; determining a difference coefficient for each type of vehicle data in the intelligent driving mode relative to each type of vehicle data in the manual driving mode under each performance indicator; determining multiple first performance parameters based on multiple vehicle data generated under preset scenarios when multiple vehicles included in a second vehicle cluster execute manual driving mode; and adjusting the multiple first performance parameters using multiple difference coefficients to obtain multiple second performance parameters under preset scenarios in intelligent driving mode. The difference coefficient corresponding to each performance indicator is obtained by comparing the driving differences between the first vehicle cluster in manual driving mode and the intelligent driving mode while meeting safety and comfort indicators, and adjusting the first performance parameter of the second vehicle cluster under each performance indicator based on the difference coefficient, so that the user has a better experience when performing intelligent driving based on the multiple first performance parameters.

[0112] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are protected by the present invention.

Claims

1. A performance parameter adjustment method, characterized in that: The method comprises: Acquiring a plurality of vehicle data that meet comfort and safety indicators under a preset scenario in a manual driving mode, wherein the vehicle data is data corresponding to a performance indicator generated by vehicles included in a first vehicle cluster, the performance indicator being the name of a performance parameter, and each of the plurality of vehicle data includes: driving data of the vehicle itself, relative position information between the vehicle itself and surrounding vehicles, and driving data of surrounding vehicles; Obtaining multiple vehicle data that meet comfort and safety indicators under the preset scenario in the intelligent driving mode; Under each performance indicator, determining a difference coefficient between each vehicle data in the intelligent driving mode and each vehicle data in the manual driving mode to obtain a plurality of difference coefficients corresponding to the plurality of performance indicators; determining a plurality of first performance parameters in the preset scenario in the manual driving mode based on a plurality of vehicle data generated by a plurality of vehicles included in a second vehicle cluster in the preset scenario in the manual driving mode, wherein the number of vehicles included in the second vehicle cluster is greater than the number of vehicles included in the first vehicle cluster; The multiple first performance parameters are adjusted using the multiple difference coefficients to obtain multiple second performance parameters under the preset scenario in the intelligent driving mode. The second performance parameters are used to characterize vehicle data that meets comfort and safety requirements under the preset scenario. The second performance parameters are the parameters obtained by adjusting the first performance parameters using the difference coefficients.

2. The method according to claim 1, characterized in that The obtaining of a plurality of vehicle data that meets comfort and safety indicators in the preset scenario in the intelligent driving mode includes: Under the preset scenario, obtaining a plurality of initial vehicle data under the intelligent driving mode; Determining a plurality of initial performance parameters corresponding to the plurality of initial vehicle data based on the driving data of the vehicle and the relative position information between the vehicle and surrounding related vehicles, as well as the driving data of the surrounding related vehicles, wherein each initial vehicle data corresponds to one initial performance parameter; If it is determined based on the user feedback information that the multiple initial performance parameters do not meet the comfort and safety indicators, adjusting the multiple initial performance parameters multiple times until the adjusted multiple initial performance parameters meet the comfort and safety indicators; A plurality of vehicle data corresponding to the plurality of initial performance indicators that meet the comfort and safety indicators are obtained.

3. The method according to claim 1, characterized in that Under each performance indicator, determining a difference coefficient of each vehicle data in the intelligent driving mode relative to each vehicle data in the manual driving mode to obtain multiple difference coefficients corresponding to multiple performance indicators includes: Under each performance indicator, based on each type of vehicle data in the intelligent driving mode, determine the to-be-processed intelligent driving performance parameter corresponding to the vehicle data, where each type of vehicle data in the intelligent driving mode corresponds to one to-be-processed intelligent driving performance parameter, and each performance indicator corresponds to one to-be-processed intelligent driving performance parameter; Under each performance indicator, based on each type of vehicle data in the manual driving mode, determine the human driving performance parameter to be processed corresponding to the vehicle data, where each type of vehicle data in the manual driving mode corresponds to one human driving performance parameter to be processed, and each performance indicator corresponds to one human driving performance parameter to be processed; Based on the unprocessed intelligent driving performance parameters corresponding to each type of vehicle data in the intelligent driving mode, and the unprocessed human driving performance parameters corresponding to each type of vehicle data in the manual driving mode, the difference coefficient of each vehicle data in the intelligent driving mode relative to each vehicle data in the manual driving mode is determined to obtain multiple difference coefficients corresponding to multiple performance indicators.

4. The method according to claim 3, characterized in that Under each performance indicator, based on each type of vehicle data in the intelligent driving mode, determining the to-be-processed intelligent driving performance parameter corresponding to the vehicle data includes: Determining, based on each type of vehicle data in the intelligent driving mode, a plurality of performance parameters of the plurality of vehicles included in the first vehicle cluster under each performance indicator, wherein one performance parameter corresponds to one vehicle; Calculating the mean of the multiple performance parameters corresponding to the multiple vehicles under each performance indicator, and using the mean as the to-be-processed intelligent driving performance parameter corresponding to the vehicle data, so as to obtain the to-be-processed intelligent driving performance parameter corresponding to each vehicle data in the intelligent driving mode; The method of determining, under each performance indicator, based on each type of vehicle data in the manual driving mode, a to-be-processed human driving performance parameter corresponding to the vehicle data includes: Determining, based on each vehicle data in the manual driving mode, a plurality of performance parameters of the plurality of vehicles included in the first vehicle cluster under each performance indicator, wherein one performance parameter corresponds to one vehicle; Calculate the mean of multiple performance parameters corresponding to the multiple vehicles under each performance indicator, and use the mean as the to-be-processed human driving performance parameter corresponding to the vehicle data to obtain the to-be-processed human driving performance parameter corresponding to each vehicle data in the manual driving mode.

5. The method according to claim 1, wherein If the preset scenario is a following vehicle scenario, the multiple vehicle data include following vehicle distance data, own vehicle speed data, and preceding vehicle speed data.

6. The method according to claim 1, characterized in that The adjusting the plurality of first performance parameters by using the plurality of difference coefficients to obtain the plurality of second performance parameters under the preset scenario in the intelligent driving mode includes: The vehicle is controlled to execute the intelligent driving mode in the preset scenario based on the multiple second performance parameters during actual use.

7. The method according to claim 6, characterized in that After controlling the vehicle to execute the intelligent driving mode in the preset scenario based on the plurality of second performance parameters during actual use, the method further includes: receiving feedback information generated by the user executing the intelligent driving mode in the preset scenario; If the amount of the feedback information exceeds a preset threshold, the plurality of second performance parameters are modified based on the feedback information.

8. A performance parameter adjustment device, characterized in that: The device comprises: a human driving data acquisition unit, configured to obtain a plurality of vehicle data satisfying comfort and safety indicators under a preset scenario in a manual driving mode, wherein the vehicle data is data corresponding to a performance indicator generated by vehicles included in the first vehicle cluster, the performance indicator being the name of a performance parameter, and each of the plurality of vehicle data comprising: driving data of the vehicle itself, relative position information between the vehicle itself and surrounding vehicles, and driving data of surrounding vehicles; An intelligent driving data acquisition unit, configured to obtain a plurality of vehicle data satisfying comfort and safety indicators under the preset scenario in the intelligent driving mode; a difference coefficient determination unit, configured to determine, under each performance indicator, a difference coefficient of each type of vehicle data in the intelligent driving mode relative to each type of vehicle data in the manual driving mode, to obtain a plurality of difference coefficients corresponding to a plurality of performance indicators; a first performance parameter determination unit configured to determine a plurality of first performance parameters in the preset scenario in the manual driving mode based on a plurality of vehicle data generated by a plurality of vehicles included in a second vehicle cluster in the preset scenario in the manual driving mode, wherein the number of vehicles included in the second vehicle cluster is greater than the number of vehicles included in the first vehicle cluster; A first performance parameter adjustment unit is used to adjust the multiple first performance parameters using the multiple difference coefficients to obtain multiple second performance parameters under the preset scenario in the intelligent driving mode, wherein the second performance parameters are used to characterize vehicle data that meets comfort and safety requirements under the preset scenario, and the second performance parameters are parameters obtained by adjusting the first performance parameters using the difference coefficients.

9. A vehicle, characterized in that: The method comprises one or more processors and a memory, wherein one or more programs are stored in the memory and configured to execute the method according to any one of claims 1 to 7 by the one or more processors.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program codes, wherein the program codes include instructions for executing the method according to any one of claims 1 to 7.

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