WO2022037553A1 - 个性化驾驶模式设置方法、系统及车辆 - Google Patents

个性化驾驶模式设置方法、系统及车辆 Download PDF

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Publication number
WO2022037553A1
WO2022037553A1 PCT/CN2021/112889 CN2021112889W WO2022037553A1 WO 2022037553 A1 WO2022037553 A1 WO 2022037553A1 CN 2021112889 W CN2021112889 W CN 2021112889W WO 2022037553 A1 WO2022037553 A1 WO 2022037553A1
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Prior art keywords
personalized
personalized information
information
user
factor
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PCT/CN2021/112889
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English (en)
French (fr)
Inventor
孙成军
彭立龙
代馥光
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长城汽车股份有限公司
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Priority to JP2023509788A priority Critical patent/JP2023538314A/ja
Priority to EP21857647.8A priority patent/EP4201773A4/en
Priority to KR1020237004841A priority patent/KR20230069906A/ko
Publication of WO2022037553A1 publication Critical patent/WO2022037553A1/zh

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/18—Propelling the vehicle
    • B60W30/182—Selecting between different operative modes, e.g. comfort and performance modes
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
    • B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
    • B60R16/037—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for occupant comfort, e.g. for automatic adjustment of appliances according to personal settings, e.g. seats, mirrors, steering wheel
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W40/09—Driving style or behaviour
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/08—Interaction between the driver and the control system
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/08—Interaction between the driver and the control system
    • B60W50/082—Selecting or switching between different modes of propelling
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/08—Interaction between the driver and the control system
    • B60W50/085—Changing the parameters of the control units, e.g. changing limit values, working points by control input
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0002—Automatic control, details of type of controller or control system architecture
    • B60W2050/0004—In digital systems, e.g. discrete-time systems involving sampling
    • B60W2050/0005—Processor details or data handling, e.g. memory registers or chip architecture
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/043—Identity of occupants
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/221—Physiology, e.g. weight, heartbeat, health or special needs
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/30—Driving style
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2556/00—Input parameters relating to data
    • B60W2556/10—Historical data
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the present disclosure relates to the technical field of vehicles, and in particular, to a method, system and vehicle for setting a personalized driving mode.
  • Existing vehicles generally provide several fixed driving modes, such as sport mode, standard mode, economic mode, etc. Each mode is equipped with corresponding driving control parameters. Machine controller) uses the corresponding driving control parameters in this mode to adjust the functions of each system to provide the user with the driving performance of this mode.
  • the present disclosure provides a personalized driving mode setting method, system and vehicle to solve the problem in the prior art that due to the limited driving modes provided by the vehicle, it cannot meet the driving needs of each user, thereby reducing the user's driving experience or performance.
  • the present disclosure provides a method for setting a personalized driving mode, which is applied to a complete machine controller set in a vehicle, and the method includes:
  • the personalized information is processed to generate the target control parameters of the whole machine controller, and the target control parameters are the personalized information of the vehicle. Control parameters in driving mode.
  • the obtaining personalized information set by the user includes any one of the following:
  • the newly added personalized information input by the user on the human-computer interaction interface of the complete machine controller is acquired.
  • the personalized information is processed to generate the target control parameters of the complete machine controller, including:
  • the target control parameter corresponding to the historical personalized information is obtained according to the pre-stored relationship between the historical personalized information and the control parameter.
  • the personalized information is processed to generate the target control parameters of the complete machine controller, including:
  • the personalized information is newly added personalized information
  • feature extraction is performed on the newly added personalized information to obtain first feature data
  • a first target control parameter of the complete machine controller is generated.
  • generating the first target control parameter of the complete machine controller according to the first personalization factor includes:
  • the standard physical characteristic parameter is adjusted to generate the first target control parameter.
  • the method after generating the first target control parameter of the complete machine controller, the method also includes:
  • the association relationship between the newly added personalized information and the first target control parameter is stored as a piece of historical personalized information.
  • the method further includes:
  • the personalized driving behavior data is the driving behavior data generated by the user in the personalized driving mode of the vehicle
  • the complete machine is generated according to the first personalized factor, the second personalized factor, and the preset proportional relationship between the first personalized factor and the second personalized factor A second target control parameter of the controller.
  • the method further includes:
  • the third characteristic data is converted to obtain a third personalized factor
  • the complete machine is generated according to the first personalized factor, the third personalized factor, and the preset proportional relationship between the first personalized factor and the third personalized factor
  • the third target control parameter of the controller is generated according to the first personalized factor, the third personalized factor, and the preset proportional relationship between the first personalized factor and the third personalized factor.
  • the method further includes:
  • the second characteristic data is converted to obtain a second personalized factor
  • the third characteristic data is converted to obtain a third personalized factor
  • the first personalization factor, the second personalization factor, the third personalization factor and the first personalization factor, the second personalization factor and the third personalization factor The preset proportional relationship among the three generates the fourth target control parameter of the complete machine controller.
  • the present disclosure provides a personalized driving mode setting system, which is applied to a complete machine controller set in a vehicle, and the system includes:
  • a first obtaining module configured to obtain the personalized information set by the user when it is detected that the vehicle starts the personalized driving mode
  • the first generation module is used to process the personalized information according to the information type of the personalized information and the pre-configured information processing strategy, and generate the target control parameters of the complete machine controller, the target control parameters are the control parameters of the vehicle in the personalized driving mode.
  • the first acquisition module includes:
  • a first acquisition sub-module configured to acquire the historical personalized information pre-stored by itself when it is detected that the user triggers a selection operation on the historical personalized information
  • the second acquiring sub-module is configured to acquire newly added personalized information sent by the cloud, where the newly added personalized information is sent to the cloud for storage after being input by the user on the mobile terminal;
  • the third acquisition sub-module is used to identify the personalized information two-dimensional code, and acquire the newly added personalized information, and the personalized information two-dimensional code is generated according to the personalized information input by the user on the mobile terminal;
  • the fourth acquisition sub-module is used to acquire the newly added personalized information input by the user on the human-computer interaction interface of the complete machine controller.
  • the first generation module includes:
  • the fifth obtaining sub-module is used to obtain the target control corresponding to the historical personalized information according to the pre-stored relationship between the historical personalized information and the control parameters when the personalized information is historical personalized information parameter.
  • the first generation module includes:
  • a first feature extraction submodule configured to perform feature extraction on the newly added personalized information to obtain first feature data when the personalized information is newly added personalized information
  • a first conversion submodule configured to convert the first feature data according to a preset conversion rule to obtain a first personalized factor
  • a generating sub-module is configured to generate the first target control parameter of the complete machine controller according to the first personalization factor.
  • the generating submodule includes:
  • a first generating subunit configured to generate initial physical characteristic parameters according to the first personalization factor
  • a regression processing subunit used for performing regression processing on the initial physical property parameters to obtain standard physical property parameters
  • the adjustment subunit is configured to adjust the standard physical characteristic parameter according to the physical parameter range of the vehicle to generate the first target control parameter.
  • system further includes:
  • a creation module configured to establish an association relationship between the newly added personalized information and the first target control parameter
  • a saving module configured to save the association relationship between the newly added personalized information and the first target control parameter as a piece of historical personalized information.
  • system further includes:
  • a second acquiring module configured to acquire personalized driving behavior data of the user, where the personalized driving behavior data is the driving behavior data generated by the user in the personalized driving mode of the vehicle;
  • a first feature extraction module configured to perform feature extraction on the personalized driving behavior data to obtain second feature data
  • a first conversion module configured to convert the second characteristic data according to the preset conversion rule to obtain a second personalization factor
  • the second generating module is configured to calculate according to the first personalization factor, the second personalization factor, and a preset ratio between the first personalization factor and the second personalization factor relationship to generate the second target control parameter of the complete machine controller.
  • the system includes:
  • the third obtaining module is used to obtain the statistical data of driving behavior in the area where the user is located;
  • a second feature extraction module configured to perform feature extraction on the driving behavior statistical data to obtain third feature data
  • a second conversion module configured to convert the third feature data according to the preset conversion rule to obtain a third personalization factor
  • a third generating module configured to calculate according to the first personalization factor, the third personalization factor, and a preset ratio between the first personalization factor and the third personalization factor relationship to generate the third target control parameter of the complete machine controller.
  • system further includes:
  • a fourth acquisition module configured to acquire personalized driving behavior data of the user and driving behavior statistical data of the area where the user is located, where the personalized driving behavior data is the driving behavior data generated by the user in the personalized driving mode of the vehicle;
  • a third feature extraction module configured to perform feature extraction on the personalized driving behavior data to obtain second feature data, and perform feature extraction on the driving behavior statistical data to obtain third feature data;
  • a third conversion module configured to convert the second feature data to obtain a second personalized factor according to the preset conversion rule, and convert the third feature data to obtain a third personalized factor
  • the fourth generation module is used for generating according to the first personalization factor, the second personalization factor, the third personalization factor and the first personalization factor, the second personalization factor and the The preset proportional relationship among the third personalization factors generates the fourth target control parameter of the complete machine controller.
  • the present disclosure provides a vehicle including the personalized driving mode setting system described in the second aspect of the present disclosure.
  • the present disclosure includes the following advantages:
  • the whole machine controller when it is detected that the vehicle starts the personalized driving mode, the whole machine controller can obtain the personalized information set by the user, and generate the target according to the personalized information set by the user and the pre-configured information processing strategy Control parameters, on the one hand, the application adds a personalized driving mode to the vehicle, which can provide users with a driving experience that is different from the traditional fixed driving mode.
  • the target control parameters of the personalized driving mode are set according to the user Personalized information generation can meet the actual driving needs of different users and improve the user's driving experience.
  • FIG. 1 shows a flowchart of steps of a method for setting a personalized driving mode provided by an embodiment of the present disclosure
  • FIG. 2 shows a schematic flowchart of a method for setting a personalized driving mode provided by an embodiment of the present disclosure
  • FIG. 3 shows a structural block diagram of a personalized driving mode setting system provided by an embodiment of the present disclosure
  • Figure 4 schematically shows a block diagram of a computing processing device for performing methods according to the present disclosure.
  • Figure 5 schematically shows a memory unit for holding or carrying program code implementing the method according to the present disclosure.
  • the controller of the whole machine can process the personalized information set by the user according to the pre-configured information processing strategy, and generate the control parameters of the controller of the whole machine, so that , the whole machine controller can generate corresponding driving modes according to the actual needs of users, meet the different needs of different users, and improve the driving experience or performance of users.
  • FIG. 1 is a flowchart of a method for setting a personalized driving mode provided by an embodiment of the present disclosure, and the method for setting a personalized driving mode can be applied to a controller of a whole machine set in a vehicle.
  • the vehicle controller (Vehicle Control Unit, VCU), also known as the vehicle controller, is the core component of the vehicle and plays a vital role in the safe, stable and reliable operation of the vehicle.
  • VCU Vehicle Control Unit
  • the method may specifically include the following steps:
  • Step 11 Obtain the personalized information set by the user when it is detected that the vehicle starts the personalized driving mode.
  • the personalized driving mode also known as the custom driving mode, refers to that in addition to the traditional driving mode that comes with the vehicle, the user can input different personalized parameters according to the actual needs of the individual, so as to generate a complete machine
  • the target control parameter of the controller is the driving mode.
  • Personalized information refers to a collection of personalized parameters set by a user. Generally, different users have different personalized information. Therefore, various personalized driving modes can be obtained according to different personalized information.
  • the personalized information may include but not limited to the following information: individual basic physiological differences, individual personality differences, individual vehicle usage environment differences, individual feeling differences, etc., wherein the individual basic physiological differences can be further subdivided into gender, age, Body type (high, short, standard), weight (heavy, light, standard), etc., individual personality differences can be subdivided into driving styles (sport, comfortable, energy-saving, etc.), and individual vehicle usage environment differences can be subdivided into traffic conditions (urban, suburban, expressway, etc.), urban terrain (plateau, mountain, plain, etc.), region (high temperature area, alpine area, standard), etc., individual differences in feelings can be subdivided into driving experience feedback (accelerator response speed, energy recovery strength, etc.).
  • a vehicle can usually have several conventional driving modes, such as a sport mode, a standard mode, an economy mode, etc., which are equipped with corresponding control parameters when the vehicle is designed and manufactured.
  • a personalized driving mode is added on the basis of the traditional driving mode.
  • the user can choose to start the personalized driving mode on the human-computer interaction interface of the complete machine controller.
  • the complete machine controller can detect the user's starting operation.
  • the user will set Personalized information, at this time, the controller of the whole machine can obtain the personalized information set by the user. It should be noted that this embodiment does not limit how the user activates the personalized driving mode and how the vehicle detects that the personalized driving mode is activated.
  • Step S12 Process the personalized information according to the information type of the personalized information and the preconfigured information processing strategy to generate target control parameters of the complete machine controller.
  • the target control parameter is the control parameter of the vehicle in the personalized driving mode.
  • the personalized information has different types of information
  • the controller of the whole machine can distinguish the types of the personalized information
  • the memory of the controller of the whole machine is also pre-configured with information processing strategies, different types of personalized information Correspondingly, there are different information processing strategies.
  • the controller of the whole machine can first determine the type of the personalized information, and then select the information processing strategy corresponding to the personalized information type according to the pre-configured information processing strategy , and process the personalized information to generate the target control parameters of the whole machine controller in the individualized driving mode.
  • the whole machine controller can use the target control parameters to control the vehicle, thereby adjusting the functions of each system of the vehicle.
  • the controller of the whole machine when it is detected that the vehicle starts the personalized driving mode, the controller of the whole machine can obtain the personalized information set by the user, and generate target control parameters according to the personalized information set by the user and the pre-configured information processing strategy
  • the application adds a personalized driving mode to the vehicle, which can provide users with a driving experience that is different from the traditional fixed driving mode. Information generation can meet the actual driving needs of different users and improve the user's driving experience.
  • the personalized information set by the user may include two types, one is the personalized information previously set by the user, also called historical personalized information, and the historical personalized information is pre-stored in the memory of the controller of the whole machine , and the other is the personalized information that the user inputs to the whole machine controller for the first time after starting the vehicle's personalization mode, and then is obtained by the complete machine controller, also known as newly added personalization information.
  • obtaining the personalized information set by the user may specifically include the following steps:
  • Step S11A in the case of detecting that the user triggers the selection operation on the historical personalized information, obtain the historical personalized information pre-stored by itself.
  • the operation of setting the personalized information by the user may be an operation of selecting historical personalized information pre-stored in the memory of the complete machine controller, that is, the complete machine controller may pre-store the personalized information previously set by the user , so that when the user starts the personalized driving mode again later, he can directly select it from the memory without requiring the user to input it to the controller of the whole machine, which simplifies the steps for the user to set the personalized information.
  • the user can continue to select the new input personalized information option in the human-computer interaction interface, or directly select the historical personalized information option.
  • the stored historical personalized information may be provided for selection by the user.
  • the number of historical personalized information in the memory of the complete machine controller can be multiple, that is to say, the historical personalized information under different driving environments or driving needs of the same user or different users can be saved to meet the needs of users. Diversified needs. Different historical personalized information has different identifiers, which can be user names or other identifiers that can uniquely identify user historical information, such as historical personalized information storage time, personalized information introduction, and so on.
  • the complete machine controller may specifically include the following steps: in the case that the personalized information is historical personalized information, according to the pre-stored relationship between the historical personalized information and the control parameters , and obtain target control parameters corresponding to the historical personalized information.
  • the historical personalized information since the historical personalized information is stored in the complete machine controller, it can indicate that the user has activated the personalized driving mode before. Therefore, the corresponding target control parameters will also be stored in the complete machine controller memory, that is, the complete machine controller.
  • the relationship between historical personalized information and control parameters can be stored in advance. In this way, after the user selects a specific historical personalized information, the controller of the whole machine can directly obtain the target control parameters that match or correspond to the historical personalized information selected by the user according to the relationship between the historical personalized information and the control parameters. That is to say, when the user triggers the selection operation on the historical personalized information, the information processing strategy is: directly according to the pre-stored relationship between the historical personalized information and the control parameters, obtain the corresponding historical personalized information. target control parameters.
  • the user does not need to repeatedly input the personalized information to the controller of the whole machine every time the user starts the personalized driving mode to set the personalized information, but can directly select the historical personality
  • the controller of the whole machine can directly obtain the target control parameters corresponding to the historical personalized information according to the correlation between the historical personalized information and the control parameters. , instead of calculating the target control parameters again according to the personalized information, which also simplifies the calculation process of the whole machine controller and reduces the time for the whole machine controller to obtain the target control parameters.
  • the user may set a certain personalized information for the first time.
  • the personalized information set by the user is obtained, which may specifically include any of the following step:
  • Step S11B Acquire newly-added personalized information sent by the cloud, where the newly-added personalized information is sent to the cloud for storage after being input by the user on the mobile terminal.
  • Step S11C identifying the personalized information two-dimensional code, and acquiring the newly added personalized information, the personalized information two-dimensional code is generated according to the personalized information input by the user on the mobile terminal
  • Step S11D acquiring the newly added personalized information input by the user on the human-computer interaction interface of the complete machine controller.
  • the controller of the whole machine to acquire the personalized information set by the user.
  • the first is to directly obtain the newly added personalized information sent by the cloud.
  • the second is to obtain the newly added personalized information by identifying the QR code of the personalized information
  • the third is to directly obtain the user’s information on the controller of the whole machine.
  • a T-BOX (Telematics BOX, in-vehicle wireless terminal) can also be set in the vehicle, so that the vehicle can communicate with the cloud server, receive messages sent by the cloud server, and send messages to the cloud server.
  • the user can use a mobile terminal (including but not limited to mobile phones, pads, laptops, wearable smart devices, etc.) to download the APP, register an account and bind the vehicle, directly enter personalized information in the APP, and use the mobile terminal.
  • the APP can send the personalized information entered by the user to the cloud server for storage. In this way, after the user selects the new input personalized information option in the human-computer interaction interface of the bound vehicle, if the vehicle is connected to the Internet, it can be accessed through the T-BOX
  • the personalized information saved by the cloud server is directly synchronized to the controller of the whole machine.
  • a QR code identification device can be installed in the vehicle.
  • the user can use a mobile terminal (which can be a mobile phone, pad, laptop, etc.) to download the APP, directly enter the personalized information in the APP, and use the QR code generation function provided by the mobile terminal APP to personalize the user's input.
  • the information generates a two-dimensional code of personalized information, and then the generated two-dimensional code of personalized information is provided to the two-dimensional code recognition device in the vehicle for identification, so that the controller of the whole machine can obtain the personalized information.
  • the human-computer interaction interface of the controller of the whole machine can directly provide the personalized information input interface, so that the user can directly input the newly added information on the interface.
  • Personalized Information Compared with the first two personalized information input methods, the third personalized information input method requires the user to temporarily input all personalized information, which is more time-consuming.
  • the first method of obtaining the personalized information set by the user is adopted by the whole machine controller, so that the user can remotely set the personalized information anytime, anywhere, so that the personalized information can be directly obtained after the vehicle starts the personalized driving mode, reducing temporary The time it takes to enter personalized information, the premise is that the vehicle needs to be connected to the Internet; using the second method of obtaining the personalized information set by the user, the user can also remotely set the personalized information anytime, anywhere, only need
  • the QR code of personalized information can be recognized by the QR code recognition device, which can also reduce the time spent temporarily entering personalized information; the third method of obtaining the personalized information set by the user by the controller of the whole machine requires the user to temporarily enter the personalized information. All personalized information, increase the time spent temporarily entering personalized information.
  • the present disclosure can provide a variety of personalized information setting methods to improve the flexibility of personalized information setting.
  • the vehicle is also provided with a HUT (Head Unit, host), and the T-BOX will first synchronize the personalized information saved by the cloud server to the HUT, or the personalized information obtained by the QR code recognition device. First, it is synchronized to the HUT, or the user enters the personalized information on the HUT interface. After the HUT obtains the newly added personalized information, the newly added personalized information is sent to the controller of the whole machine in the form of CAN messages.
  • HUT Head Unit, host
  • the complete machine controller may specifically include the following steps when performing the above step S12:
  • Step S12A if the personalized information is newly added personalized information, perform feature extraction on the newly added personalized information to obtain first feature data.
  • the user sets a certain personalized information for the first time, that is, the personalized information obtained by the controller of the whole machine is newly added personalized information, it can indicate that the user has not activated the personalized driving mode before, or, although he has activated the personalized driving mode Personalized driving mode, but the personalized information is not set. Therefore, the corresponding target control parameters will not be stored in the memory of the whole machine controller.
  • the personalized information Since the personalized information is entered by the user according to their own driving needs, it is the data related to the user and cannot be directly expressed as target control parameters. Therefore, it is necessary to extract the characteristics of the personalized information first to obtain the first characteristic data, so as to facilitate the
  • the personalized information is converted into target control parameters that can be recognized by the whole machine controller.
  • the first feature data may be expressed in the form of a sequence, and each item of the personalized information corresponds to a row in the sequence.
  • Step S12B convert the first feature data to obtain a first personalized factor.
  • the preset conversion rules are obtained according to the designer's long-term experience and experimental data, and the preset conversion rules can be in the form of conversion matrices or calibration conversion tables. limited.
  • the first characteristic data can be converted by using a conversion matrix or a calibration conversion table, so as to obtain the first personalization factor.
  • the first personalization factor can also only reflect user-related data, and cannot be directly used by the vehicle. identify.
  • Step S12C generating a first target control parameter of the complete machine controller according to the first personalization factor.
  • the complete machine controller can generate the first target control parameter of the complete machine controller according to the first personalization factor.
  • step S12C may specifically include the following steps:
  • Step S12C1 generating initial physical characteristic parameters according to the first personalization factor.
  • Step S12C2 performing regression processing on the initial physical property parameters to obtain standard physical property parameters.
  • Step S12C3 adjusting the standard physical characteristic parameter according to the physical parameter range of the vehicle to generate the first target control parameter.
  • the initial physical property parameters refer to physical property parameters that are directly generated according to the first personalization factor and are acceptable to the physical function of the vehicle, such as torque from the motor, accelerator pedal opening, creeping speed, and energy recovery. strength, etc.
  • torque from the motor such as torque from the motor, accelerator pedal opening, creeping speed, and energy recovery. strength, etc.
  • the process of generating the initial physical characteristic parameters according to the first personalization factor reference may be made to the process of generating physical characteristic parameters from the personalization factor in the traditional driving mode design process in the related art, which will not be described in detail here.
  • the generated initial physical property parameters are not necessarily all reasonable or valid. Therefore, it is also necessary to check the validity or rationality of the initial physical property parameters.
  • the initial physical property parameters are subjected to regression processing to obtain standard physical property parameters.
  • the first target control parameter can be accepted by the physical parameters of the vehicle, and at the same time is reasonable and effective, and also conforms to the limitation of the physical parameter range of the vehicle, and can be used as the control parameter of the vehicle in the personalized driving mode.
  • the personalized driving mode setting method in this embodiment may further include the following steps:
  • Step S12D establishing an association relationship between the newly added personalized information and the first target control parameter.
  • Step S12E Save the association between the newly added personalized information and the first target control parameter as a piece of historical personalized information.
  • the controller of the whole machine can associate the newly-added personalized information with the corresponding first target control parameter.
  • associated preservation that is, establishing an association relationship between the newly added personalized information and the first target control parameter, and saving the association relationship between the newly added personalized information and the first target control parameter as a piece of historical personalized information.
  • the personalized driving mode setting method of the present application may further include the following steps:
  • Step S131A obtaining personalized driving behavior data of the user.
  • the personalized driving behavior data is the driving behavior data generated by the user in the personalized driving mode of the vehicle.
  • it can be the performance of driving behavior, the user is accustomed to using the accelerator pedal range of 50-80 km/h, or the user is accustomed to turning on the air conditioner to the second gear during driving.
  • the whole machine controller after the whole machine controller generates the first control parameter, it adjusts the functions of each system of the vehicle according to the first target control parameter, so that the vehicle runs in the personalized mode. At this time, the user can drive the vehicle in the personalized mode. . Since the user will generate driving behavior data in the process of driving the vehicle in the personalized mode, the controller of the whole machine can obtain the personalized driving behavior data of the user.
  • Step S131B Perform feature extraction on the personalized driving behavior data to obtain second feature data.
  • step S131B is similar to the process of the step S12A, and the description of the step S12A can be referred to for the related process.
  • Step S131C Convert the second feature data according to the preset conversion rule to obtain a second personalization factor.
  • step S131C is similar to the process of step S12BA, and the description of step S12B may be referred to for related processes.
  • Step S131D according to the first personalized factor, the second personalized factor, and the preset proportional relationship between the first personalized factor and the second personalized factor, obtain the Describe the second target control parameter of the complete machine controller.
  • the two since the first personalization factor is generated according to the personalized information entered by the user, and the second personalization factor is generated according to the driving behavior data generated by the user in the personalized driving mode, the two may correspond to different Therefore, when generating the first target control parameter of the whole machine controller, the preset proportional relationship between the first personalization factor and the second personalization factor can be preset, and then according to the first The personalization factor, the second personalization factor, and the preset proportional relationship between the first personalization factor and the second personalization factor determine the fusion factor, and finally generate the second target control of the whole machine controller according to the fusion factor. parameter.
  • the controller of the whole machine when generating the target control parameters, can combine the personalized information entered by the user and the driving behavior data generated by the user in the personalized driving mode at the same time, that is, according to the actual driving of the user in the personalized driving mode
  • the behavior data is self-learned, and the second personalized factor obtained by self-learning is used to correct the target control parameters, so that the target control parameters generated by the whole machine controller can be more in line with the actual driving behavior and habits of users, so as to provide users with the most suitable driving modes and personalized driving experience.
  • the data reflected by the newly added personalized information is often limited, it may be different from the best driving behavior or habits in the user's current area. Therefore, in addition to using the user's real driving behavior data in the personalized driving mode to In addition to revising the target control parameters, other data can also be used to revise the target control parameters, such as statistical data of driving behavior in the area where the user is located.
  • the driving behavior statistical data in the area where the user is located may be various driving behavior statistical data collected by the cloud server of all vehicles in the area where the user vehicle is located, for example, including but not limited to the daily maximum speed of vehicles of the same type in different cities.
  • the pedal opening parameter can be adjusted according to the daily maximum speed of the same type of vehicle.
  • the personalized driving mode setting method of the present application may further include the following steps:
  • Step S132A obtaining statistical data of driving behavior in the area where the user is located.
  • Step S132B performing feature extraction on the driving behavior statistical data to obtain third feature data.
  • Step S132C Convert the third feature data according to the preset conversion rule to obtain a third personalization factor.
  • Step S132D according to the first personalized factor, the third personalized factor, and the preset proportional relationship between the first personalized factor and the third personalized factor, generate the Describe the third target control parameter of the complete machine controller.
  • the two may also correspond to different Therefore, when generating the first target control parameter of the whole machine controller, the preset proportional relationship between the first personalization factor and the third personalization factor can be preset, and then according to the first The personalization factor, the third personalization factor, and the preset proportional relationship between the first personalization factor and the third personalization factor determine the fusion factor, and finally generate the third target control of the whole machine controller according to the fusion factor. parameter.
  • the controller of the whole machine when generating the target control parameters, can combine the personalized information entered by the user and the driving behavior statistical data of the user's area at the same time, that is, the target control parameters are adjusted according to the driving behavior statistical data of the user's area. Correction, so that the target control parameters generated by the whole machine controller can refer to the driving behavior or habits of other users in the area where the user is located, so as to further provide the user with the most suitable driving mode and personalized driving experience in the current area.
  • the real driving behavior data of the user in the personalized driving mode and the driving behavior statistical data of the area where the user is located can also be used to correct the target control parameters. Therefore, in step S12C, generate After the first target control parameter of the complete machine controller, the personalized driving mode setting method of the present application may further include the following steps:
  • Step S133A Obtain personalized driving behavior data of the user and driving behavior statistical data of the area where the user is located, where the personalized driving behavior data is the driving behavior data generated by the user in the personalized driving mode of the vehicle.
  • Step S133B perform feature extraction on the personalized driving behavior data to obtain second feature data, and perform feature extraction on the driving behavior statistical data to obtain third feature data.
  • Step S133C convert the second feature data to obtain a second personalized factor, and convert the third feature data to obtain a third personalized factor.
  • Step S133D according to the first personalized factor, the second personalized factor, the third personalized factor and the first personalized factor, the second personalized factor and the third personalized factor
  • the preset proportional relationship among the three personalization factors generates the fourth target control parameter of the complete machine controller.
  • steps S131A-S131D are similar to the processes of steps S132A-S132D and steps S133A-S133D, and the related processes can refer to the descriptions of steps S132A-S132D and steps S133A-S133D.
  • the real driving behavior data of the user in the personalized driving mode and the driving behavior statistical data of the area where the user is located are used to modify the target control parameters, which can further provide the user with a suitable driving mode and personalized driving experience .
  • FIG. 2 shows a complete schematic flowchart of a method for setting a personalized driving mode provided by an embodiment of the present disclosure. As shown in Figure 2, the process is as follows:
  • Big data collection and input the driving behavior statistics of the user's area are input, and feature extraction is performed to obtain the third personalized factor Z3).
  • the whole machine controller adjusts the initial physical characteristic parameters (regression processing, and adjusts according to the range of vehicle physical parameters) to obtain target control parameters.
  • the whole machine controller self-learns (feature extraction, and converts the extracted feature data) the user's personalized driving behavior data, and feeds back to obtain the second personalized factor.
  • FIG. 3 shows a structural block diagram of a personalized driving mode setting system 30 according to an embodiment of the present disclosure.
  • the system includes:
  • the first obtaining module 31 is configured to obtain the personalized information set by the user when it is detected that the vehicle starts the personalized driving mode;
  • the first generation module 32 is used to process the personalized information according to the information type of the personalized information and the preconfigured information processing strategy, and generate the target control parameters of the complete machine controller, the target control parameters.
  • the parameters are control parameters of the vehicle in the personalized driving mode.
  • the first acquisition module includes:
  • a first acquisition sub-module configured to acquire the historical personalized information pre-stored by itself when it is detected that the user triggers a selection operation on the historical personalized information
  • the second acquiring sub-module is configured to acquire newly added personalized information sent by the cloud, where the newly added personalized information is sent to the cloud for storage after being input by the user on the mobile terminal;
  • the third acquisition sub-module is used to identify the personalized information two-dimensional code, and acquire the newly added personalized information, and the personalized information two-dimensional code is generated according to the personalized information input by the user on the mobile terminal;
  • the fourth acquisition sub-module is used to acquire the newly added personalized information input by the user on the human-computer interaction interface of the complete machine controller.
  • the first generation module includes:
  • the fifth obtaining sub-module is used to obtain the target control corresponding to the historical personalized information according to the pre-stored relationship between the historical personalized information and the control parameters when the personalized information is historical personalized information parameter.
  • the first generation module includes:
  • a first feature extraction submodule configured to perform feature extraction on the newly added personalized information to obtain first feature data when the personalized information is newly added personalized information
  • a first conversion submodule configured to convert the first feature data according to a preset conversion rule to obtain a first personalized factor
  • a generating sub-module is configured to generate the first target control parameter of the complete machine controller according to the first personalization factor.
  • the generating submodule includes:
  • a first generating subunit configured to generate initial physical characteristic parameters according to the first personalization factor
  • a regression processing subunit used for performing regression processing on the initial physical property parameters to obtain standard physical property parameters
  • the adjustment subunit is configured to adjust the standard physical characteristic parameter according to the physical parameter range of the vehicle to generate the first target control parameter.
  • system further includes:
  • a creation module configured to establish an association relationship between the newly added personalized information and the first target control parameter
  • a saving module configured to save the association relationship between the newly added personalized information and the first target control parameter as a piece of historical personalized information.
  • system further includes:
  • a second acquiring module configured to acquire personalized driving behavior data of the user, where the personalized driving behavior data is the driving behavior data generated by the user in the personalized driving mode of the vehicle;
  • a first feature extraction module configured to perform feature extraction on the personalized driving behavior data to obtain second feature data
  • a first conversion module configured to convert the second characteristic data according to the preset conversion rule to obtain a second personalization factor
  • the second generating module is configured to calculate according to the first personalization factor, the second personalization factor, and a preset ratio between the first personalization factor and the second personalization factor relationship to generate the second target control parameter of the complete machine controller.
  • the system includes:
  • the third obtaining module is used to obtain the statistical data of driving behavior in the area where the user is located;
  • a second feature extraction module configured to perform feature extraction on the driving behavior statistical data to obtain third feature data
  • a second conversion module configured to convert the third feature data according to the preset conversion rule to obtain a third personalization factor
  • a third generating module configured to calculate according to the first personalization factor, the third personalization factor, and a preset ratio between the first personalization factor and the third personalization factor relationship to generate the third target control parameter of the complete machine controller.
  • system further includes:
  • a fourth acquisition module configured to acquire personalized driving behavior data of the user and driving behavior statistical data of the area where the user is located, where the personalized driving behavior data is the driving behavior data generated by the user in the personalized driving mode of the vehicle;
  • a third feature extraction module configured to perform feature extraction on the personalized driving behavior data to obtain second feature data, and perform feature extraction on the driving behavior statistical data to obtain third feature data;
  • a third conversion module configured to convert the second feature data to obtain a second personalized factor according to the preset conversion rule, and convert the third feature data to obtain a third personalized factor
  • the fourth generation module is used for generating according to the first personalization factor, the second personalization factor, the third personalization factor and the first personalization factor, the second personalization factor and the The preset proportional relationship among the third personalization factors generates the fourth target control parameter of the complete machine controller.
  • the device embodiments described above are only illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in One place, or it can be distributed over multiple network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.
  • Various component embodiments of the present disclosure may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof.
  • a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a computing processing device according to embodiments of the present disclosure.
  • DSP digital signal processor
  • the present disclosure can also be implemented as apparatus or apparatus programs (eg, computer programs and computer program products) for performing some or all of the methods described herein.
  • Such a program implementing the present disclosure may be stored on a computer-readable medium, or may be in the form of one or more signals. Such signals may be downloaded from Internet sites, or provided on carrier signals, or in any other form.
  • Figure 4 illustrates a computing processing device that may implement methods in accordance with the present disclosure.
  • the computing processing device traditionally includes a processor 1010 and a computer program product or computer readable medium in the form of a memory 1020 .
  • the memory 1020 may be electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read Only Memory), EPROM, hard disk, or ROM.
  • the memory 1020 has storage space 1030 for program code 1031 for performing any of the method steps in the above-described methods.
  • the storage space 1030 for program codes may include various program codes 1031 for implementing various steps in the above methods, respectively. These program codes can be read from or written to one or more computer program products.
  • These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks. Such computer program products are typically portable or fixed storage units as described with reference to FIG. 5 .
  • the storage unit may have storage segments, storage spaces, etc. arranged similarly to the memory 1020 in the computing processing device of FIG. 4 .
  • the program code may, for example, be compressed in a suitable form.
  • the storage unit includes computer readable code 1031', ie code readable by a processor such as 1010 for example, which when executed by a computing processing device, causes the computing processing device to perform any of the methods described above. of the various steps.
  • a personalized driving mode setting method, a personalized driving mode setting system, an electronic device, and a computer-readable storage medium provided by the present disclosure have been described in detail above. Specific examples are used herein to describe the present disclosure. The principles and implementations of the present disclosure have been described, and the descriptions of the above embodiments are only used to help understand the method of the present disclosure and its core idea; There will be changes in the scope. In summary, the contents of this specification should not be construed as limiting the present disclosure.

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Abstract

一种个性化驾驶模式设置方法、系统及车辆，个性化驾驶模式设置方法包括：在检测到车辆启动个性化驾驶模式的情况下，获取用户设置的个性化信息（S11）；根据个性化信息的信息类型以及预先配置的信息处理策略，对个性化信息进行处理，生成整机控制器的目标控制参数，目标控制参数为车辆在个性化驾驶模式下的控制参数（S12）。个性化驾驶模式设置方法能够提供给用户区别于传统固定驾驶模式的驾车体验，并满足不同用户的实际驾驶需求，提高用户驾驶体验。

Description

个性化驾驶模式设置方法、系统及车辆
相关申请的交叉引用
本公开要求在2020年08月18日提交中国专利局、申请号为202010833415.2、名称为“个性化驾驶模式设置方法、系统及车辆”的中国专利申请的优先权，其全部内容通过引用结合在本公开中。
技术领域
本公开涉及车辆技术领域，特别是涉及一种个性化驾驶模式设置方法、系统及车辆。
背景技术
现有的车辆一般提供几种固定的驾驶模式，例如运动模式，标准模式，经济模式等，每种模式配备相应的驾驶控制参数，当用户选择某种驾驶模式后，VCU(Vehicle Control Unit，整机控制器)使用该模式下对应的驾驶控制参数调整各系统功能，给用户提供该模式的驾驶表现。
然而，车辆的使用人群较为复杂，使用场景也多种多样，每个用户都有不同的驾驶需求，而相关技术中只能提供几种固定的驾驶模式，用户只能从固有的几种驾驶模式中选择，往往难以找到合适自己的驾驶模式，达到最好的驾驶体验或表现。
发明内容
本公开提供了一种个性化驾驶模式设置方法、系统及车辆，以解决现有技术中由于车辆提供的驾驶模式有限，不能满足各个用户的驾驶需求，进而降低用户驾驶体验或表现的问题。
为了解决上述问题，本公开是这样实现的：
第一方面，本公开提供了一种个性化驾驶模式设置方法，应用于车辆中设置的整机控制器，所述方法包括：
在检测到车辆启动个性化驾驶模式的情况下，获取用户设置的个性化信息；
根据所述个性化信息的信息类型以及预先配置的信息处理策略，对所述个性化信息进行处理，生成所述整机控制器的目标控制参数，所述目标控制参数为所述车辆在个性化驾驶模式下的控制参数。
可选地，所述获取用户设置的个性化信息，包括以下任一者：
在检测到用户触发对历史个性化信息的选择操作的情况下，获取自身预存的所述历史个性化信息；
获取云端发送的新增个性化信息，所述新增个性化信息是用户在移动终端输入后，发送到所述云端保存的；
识别个性化信息二维码，获取所述新增个性化信息，所述个性化信息二维码是根据用户在移动终端输入的个性化信息生成的；
获取用户在所述整机控制器的人机交互界面输入的所述新增个性化信息。
可选地，根据所述个性化信息的信息类型以及预先配置的信息处理策略，对所述个性化信息进行处理，生成所述整机控制器的目标控制参数，包括：
在所述个性化信息为历史个性化信息的情况下，根据预先存储的历史个性化信息与控制参数的关联关系，获得与所述历史个性化信息对应的目标控制参数。
可选地，根据所述个性化信息的信息类型以及预先配置的信息处理策略，对所述个性化信息进行处理，生成所述整机控制器的目标控制参数，包括：
在所述个性化信息为新增个性化信息的情况下，对所述新增个性化信息进行特征提取，获得第一特征数据；
根据预设转换规则，对所述第一特征数据进行转换，获得第一个性化因子；
根据所述第一个性化因子，生成所述整机控制器的第一目标控制参数。
可选地，所述根据所述第一个性化因子，生成所述整机控制器的第一目标控制参数，包括：
根据所述第一个性化因子，生成初始物理特性参数；
对所述初始物理特性参数进行回归处理，得到标准物理特性参数；
根据所述车辆的物理参数范围，对所述标准物理特性参数进行调整，生成所述第一目标控制参数。
可选地，在生成所述整机控制器的第一目标控制参数之后，所述方法还 包括：
建立所述新增个性化信息和所述第一目标控制参数之间的关联关系；
将所述新增个性化信息和所述第一目标控制参数之间的关联关系作为一条历史个性化信息进行保存。
可选地，在生成所述整机控制器的第一目标控制参数之后，所述方法还包括：
获取用户的个性化驾驶行为数据，所述个性化驾驶行为数据为车辆在所述个性化驾驶模式下用户产生的驾驶行为数据；
对所述个性化驾驶行为数据进行特征提取，获得第二特征数据；
根据所述预设转换规则，对所述第二特征数据进行转换，获得第二个性化因子；
根据所述第一个性化因子、所述第二个性化因子，以及所述第一个性化因子和所述第二个性化因子两者之间的预设比例关系，生成所述整机控制器的第二目标控制参数。
可选地，在生成所述整机控制器的第一目标控制参数之后，所述方法还包括：
获取用户所在区域的驾驶行为统计数据；
对所述驾驶行为统计数据进行特征提取，获得第三特征数据；
根据所述预设转换规则，对所述第三特征数据进行转换，获得第三个性化因子；
根据所述第一个性化因子、所述第三个性化因子，以及所述第一个性化因子和所述第三个性化因子两者之间的预设比例关系，生成所述整机控制器的第三目标控制参数。
可选地，在生成所述整机控制器的第一目标控制参数之后，所述方法还包括：
获取用户的个性化驾驶行为数据，以及用户所在区域的驾驶行为统计数据，所述个性化驾驶行为数据为车辆在所述个性化驾驶模式下用户产生的驾驶行为数据；
对所述个性化驾驶行为数据进行特征提取，获得第二特征数据，以及，对所述驾驶行为统计数据进行特征提取，获得第三特征数据；
根据所述预设转换规则，对所述第二特征数据进行转换，获得第二个性化因子，以及对所述第三特征数据进行转换，获得第三个性化因子；
根据所述第一个性化因子、所述第二个性化因子、所述第三个性化因子以及所述第一个性化因子、所述第二个性化因子和所述第三个性化因子三者之间的预设比例关系，生成所述整机控制器的第四目标控制参数。
第二方面，本公开提供了一种个性化驾驶模式设置系统，应用于车辆中设置的整机控制器，所述系统包括：
第一获取模块，用于在检测到车辆启动个性化驾驶模式的情况下，获取用户设置的个性化信息；
第一生成模块，用于根据所述个性化信息的信息类型以及预先配置的信息处理策略，对所述个性化信息进行处理，生成所述整机控制器的目标控制参数，所述目标控制参数为所述车辆在个性化驾驶模式下的控制参数。
可选地，所述第一获取模块，包括：
第一获取子模块，用于在检测到用户触发对历史个性化信息的选择操作的情况下，获取自身预存的所述历史个性化信息；
第二获取子模块，用于获取云端发送的新增个性化信息，所述新增个性化信息是用户在移动终端输入后，发送到所述云端保存的；
第三获取子模块，用于识别个性化信息二维码，获取所述新增个性化信息，所述个性化信息二维码是根据用户在移动终端输入的个性化信息生成的；
第四获取子模块，用于获取用户在所述整机控制器的人机交互界面输入的所述新增个性化信息。
可选地，所述第一生成模块，包括：
第五获得子模块，用于在所述个性化信息为历史个性化信息的情况下，根据预先存储的历史个性化信息与控制参数的关联关系，获得与所述历史个性化信息对应的目标控制参数。
可选地，所述第一生成模块，包括：
第一特征提取子模块，用于在所述个性化信息为新增个性化信息的情况下，对所述新增个性化信息进行特征提取，获得第一特征数据；
第一转换子模块，用于根据预设转换规则，对所述第一特征数据进行转换，获得第一个性化因子；
生成子模块，用于根据所述第一个性化因子，生成所述整机控制器的第一目标控制参数。
可选地，所述生成子模块，包括：
第一生成子单元，用于根据所述第一个性化因子，生成初始物理特性参数；
回归处理子单元，用于对所述初始物理特性参数进行回归处理，得到标准物理特性参数；
调整子单元，用于根据所述车辆的物理参数范围，对所述标准物理特性参数进行调整，生成所述第一目标控制参数。
可选地，所述系统还包括：
创建模块，用于建立所述新增个性化信息和所述第一目标控制参数之间的关联关系；
保存模块，用于将所述新增个性化信息和所述第一目标控制参数之间的关联关系作为一条历史个性化信息进行保存。
可选地，所述系统还包括：
第二获取模块，用于获取用户的个性化驾驶行为数据，所述个性化驾驶行为数据为车辆在所述个性化驾驶模式下用户产生的驾驶行为数据；
第一特征提取模块，用于对所述个性化驾驶行为数据进行特征提取，获得第二特征数据；
第一转换模块，用于根据所述预设转换规则，对所述第二特征数据进行转换，获得第二个性化因子；
第二生成模块，用于根据所述第一个性化因子、所述第二个性化因子，以及所述第一个性化因子和所述第二个性化因子两者之间的预设比例关系，生成所述整机控制器的第二目标控制参数。
可选地，所述系统包括：
第三获取模块，用于获取用户所在区域的驾驶行为统计数据；
第二特征提取模块，用于对所述驾驶行为统计数据进行特征提取，获得第三特征数据；
第二转换模块，用于根据所述预设转换规则，对所述第三特征数据进行转换，获得第三个性化因子；
第三生成模块，用于根据所述第一个性化因子、所述第三个性化因子，以及所述第一个性化因子和所述第三个性化因子两者之间的预设比例关系，生成所述整机控制器的第三目标控制参数。
可选地，所述系统还包括：
第四获取模块，用于获取用户的个性化驾驶行为数据，以及用户所在区域的驾驶行为统计数据，所述个性化驾驶行为数据为车辆在所述个性化驾驶模式下用户产生的驾驶行为数据；
第三特征提取模块，用于对所述个性化驾驶行为数据进行特征提取，获得第二特征数据，以及，对所述驾驶行为统计数据进行特征提取，获得第三特征数据；
第三转换模块，用于根据所述预设转换规则，对所述第二特征数据进行转换，获得第二个性化因子，以及对所述第三特征数据进行转换，获得第三个性化因子；
第四生成模块，用于根据所述第一个性化因子、所述第二个性化因子、所述第三个性化因子以及所述第一个性化因子、所述第二个性化因子和所述第三个性化因子三者之间的预设比例关系，生成所述整机控制器的第四目标控制参数。
第三方面，本公开提供了一种车辆，包括本公开第二方面所述的个性化驾驶模式设置系统。
与现有技术相比，本公开包括以下优点：
在本公开实施例中，在检测到车辆启动个性化驾驶模式的情况下，整机控制器能够获取用户设置的个性化信息，并根据用户设置的个性化信息以及预先配置的信息处理策略生成目标控制参数，一方面，本申请在车辆中新增个性化驾驶模式，可以提供给用户区别于传统固定驾驶模式的驾车体验，另一方面，由于个性化驾驶模式的目标控制参数是根据用户设置的个性化信息生成，能够满足不同用户的实际驾驶需求，提高用户驾驶体验。
上述说明仅是本公开技术方案的概述，为了能够更清楚了解本公开的技术手段，而可依照说明书的内容予以实施，并且为了让本公开的上述和其它目的、特征和优点能够更明显易懂，以下特举本公开的具体实施方式。
附图说明
为了更清楚地说明本公开实施例或相关技术中的技术方案，下面将对实施例或相关技术描述中所需要使用的附图作一简单地介绍，显而易见地，下面描述中的附图是本公开的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1示出了本公开实施例提供的一种个性化驾驶模式设置方法的步骤流程图；
图2示出了本公开实施例提供的一种个性化驾驶模式设置方法的流程示意图；
图3示出了本公开实施例提供的一种个性化驾驶模式设置系统的结构框图；
图4示意性地示出了用于执行根据本公开的方法的计算处理设备的框图；并且
图5示意性地示出了用于保持或者携带实现根据本公开的方法的程序代码的存储单元。
具体实施例
为使本公开的上述目的、特征和优点能够更加明显易懂，下面结合附图和具体实施方式对本公开作进一步详细的说明。
针对相关技术中，车辆只能提供几种固定的驾驶模式，用户只能从固有的几种驾驶模式中选择，往往难以找到合适自己的驾驶模式，达到最好的驾驶体验或表现的技术问题，提出本公开实施例的核心构思：为用户提供个性化信息设置功能，整机控制器可以根据预先配置的信息处理策略对用户设置的个性化信息进行处理，生成整机控制器的控制参数，如此，整机控制器便能够根据用户实际需求生成对应的驾驶模式，满足不同用户的不同需求，提高用户的驾驶体验或表现。
参照图1，图1是本公开一实施例提供的一种个性化驾驶模式设置方法的流程图，该个性化驾驶模式设置方法可以应用于车辆中设置的整机控制器。
本实施例中，整机控制器(Vehicle Control Unit，VCU)，又称为整车控制器，是汽车的核心部件，对汽车的安全、稳定、可靠运行起着至关重要的 作用。
如图1所示，该方法具体可以包括如下步骤：
步骤11，在检测到车辆启动个性化驾驶模式的情况下，获取用户设置的个性化信息。
本实施例中，个性化驾驶模式，又可以称为自定义驾驶模式，是指除了车辆自带的传统驾驶模式之外的，可由用户根据个人实际需求而输入不同个性化参数，从而生成整机控制器的目标控制参数的驾驶模式。
个性化信息是指用户设置的个性化参数的集合，通常情况下，不同用户的个性化信息不同，因此，根据不同的个性化信息可以得到多样化的个性化驾驶模式。示例地，个性化信息可以包括但不限于以下信息：个体基本生理差异、个体性格差异、个体车辆使用环境差异、个体感受差异等，其中，个体基本生理差异又可以细分为性别、年龄段、体型(高、矮、标准)、体重(重、轻、标准)等，个体性格差异又可以细分为驾驶风格(运动、舒适、节能等)，个体车辆使用环境差异又可以细分为交通状况(市区、市郊、高速等)、城市地形(高原、山区、平原等)、地域(高温地区、高寒地区、标准)等，个体感受差异又可以细分为驾驶体验反馈(油门反应快慢、能量回收强弱等)。
车辆通常可以具有几种传统的驾驶模式，例如，运动模式、标准模式、经济模式等，这几种驾驶模式在汽车设计制造时配备对应控制参数。本实施例则在传统驾驶模式的基础上新增个性化驾驶模式。
具体实现时，用户可以在整机控制器的人机交互界面选择启动个性化驾驶模式，此时，整机控制器便可以检测到用户的启动操作，在启动个性化驾驶模式之后，用户会设置个性化信息，此时，整机控制器便能够获取到用户设置的个性化信息。需要说明的是，本实施例对用户如何启动个性化驾驶模式，以及车辆如何检测到启动了个性化驾驶模式不做限定。
步骤S12，根据所述个性化信息的信息类型以及预先配置的信息处理策略，对所述个性化信息进行处理，生成所述整机控制器的目标控制参数。
其中，所述目标控制参数为所述车辆在个性化驾驶模式下的控制参数。
本实施例中，个性化信息具有不同的信息类型，整机控制器可以对个性化信息的类型进行分辨，并且，整机控制器内存中还预先配置有信息处理策 略，不同类型的个性化信息对应有不同的信息处理策略。
因此，在具体实现时，整机控制器在获取用户设置的个性化信息之后，可以首先确定个性化信息的类型，接着再根据预先配置的信息处理策略选择与个性化信息类型对应的信息处理策略，对个性化信息进行处理，从而生成整机控制器在个性化驾驶模式下的目标控制参数，整机控制器使用目标控制参数便可以实现对车辆进行控制，从而调整车辆个系统功能。
本实施例中，在检测到车辆启动个性化驾驶模式的情况下，整机控制器能够获取用户设置的个性化信息，并根据用户设置的个性化信息以及预先配置的信息处理策略生成目标控制参数，一方面，本申请在车辆中新增个性化驾驶模式，可以提供给用户区别于传统固定驾驶模式的驾车体验，另一方面，由于个性化驾驶模式的目标控制参数是根据用户设置的个性化信息生成，能够满足不同用户的实际驾驶需求，提高用户驾驶体验。
在一种实施方式中，用户设置的个性化信息可以包括两种，一种是用户以前设置的个性化信息，也称为历史个性化信息，历史个性化信息预先存储在整机控制器内存中，另一种是用户在启动车辆的个性化模式之后，初次输入到整机控制器，再由整机控制器获取的个性化信息，也称为新增个性化信息。
结合以上实施例，在本申请另一种实施方式中，上述步骤S11中，获取用户设置的个性化信息，具体可以包括以下步骤：
步骤S11A，在检测到用户触发对历史个性化信息的选择操作的情况下，获取自身预存的所述历史个性化信息。
本实施例中，用户设置个性化信息的操作可以是从整机控制器的内存中选择事先预存的历史个性化信息的操作，也就是说，整机控制器可以预存用户以前设置的个性化信息，以便于之后用户再次启动个性化驾驶模式时，可以直接从内存中选择，而不需要用户再输入到整机控制器，简化用户设置个性化信息的步骤。
具体实现时，用户在人机交互界面启动个性化驾驶模式之后，用户可以在人机交互界面中继续选择新输入个性化信息选项，或者直接选择历史个性化信息选项，当选择历史个性化信息选项之后，可以提供存储的历史个性化信息供用户选择。需要说明的是，整机控制器内存中的历史个性化信息的数 量可以有多个，也就是说，可以保存同一用户或者不同用户的不同驾驶环境或者驾驶需求下的历史个性化信息，满足用户多样化需求。对于不同的历史个性化信息，具有不同的标识，该标识可以是用户名称，也可以是其它能够唯一标识用户历史信息的标识，例如历史个性化信息保存时间、个性化信息简介等。
在此基础上，整机控制器在执行上述步骤S12时，具体可以包括步骤：在所述个性化信息为历史个性化信息的情况下，根据预先存储的历史个性化信息与控制参数的关联关系，获得与所述历史个性化信息对应的目标控制参数。
其中，由于整机控制器中存储有历史个性化信息，可以说明用户以前启动过个性化驾驶模式，因此，整机控制器内存中还会存储有对应的目标控制参数，即，整机控制器中可以预先存储历史个性化信息与控制参数的关联关系。如此，当用户选择某个具体的历史个性化信息之后，整机控制器可以根据历史个性化信息与控制参数的关联关系，直接获取与用户选择的历史个性化信息匹配或者对应的目标控制参数。也就是说，当用户触发对历史个性化信息的选择操作的情况下，信息处理策略为：直接根据预先存储的历史个性化信息与控制参数的关联关系，获得与所述历史个性化信息对应的目标控制参数。
采用本实施例的方法，针对同一用户的同一需求，用户在每次启动个性化驾驶模式设置个性化信息时，不用每次都向整机控制器重复输入个性化信息，而可以直接选择历史个性化信息，简化用户操作步骤，并且，由于整机控制器在获取历史个性化信息之后，可以直接根据历史个性化信息与控制参数的关联关系，获得与所述历史个性化信息对应的目标控制参数，而不用根据个性化信息再次计算目标控制参数，同样简化了整机控制器的计算过程，减少了整机控制器获得目标控制参数的时间。
结合以上实施例，在本申请另一种实施方式中，用户可能是第一次设置某个个性化信息，此时，上述步骤S11中，获取用户设置的个性化信息，具体可以包括以下任一步骤：
步骤S11B，获取云端发送的新增个性化信息，所述新增个性化信息是用户在移动终端输入后，发送到所述云端保存的。
步骤S11C，识别个性化信息二维码，获取所述新增个性化信息，所述个性化信息二维码是根据用户在移动终端输入的个性化信息生成的
步骤S11D，获取用户在所述整机控制器的人机交互界面输入的所述新增个性化信息。
本实施例针对用户第一次设置某个个性化信息，提供了另外三种整机控制器获取用户设置的个性化信息的方式。第一种是直接获取云端发送的新增个性化信息具体地，第二种是识别个性化信息二维码的方式获取新增个性化信息，第三种是直接获取用户在整机控制器的人机交互界面输入的新增个性化信息。
具体地，针对第一种方式，车辆中还可以设置T-BOX(Telematics BOX，车载无线终端)，使得车辆可以与云端服务器进行通信，接收云端服务器发送的消息，以及向云端服务器发送消息。
具体实现时，用户可以使用移动终端(包括但不限于是手机、pad、笔记本电脑、穿戴式智能设备等)下载APP，注册账号并绑定车辆后，直接在APP录入个性化信息，通过移动终端APP可以将用户录入的个性化信息发送到云端服务器保存，如此，用户在绑定车辆的人机交互界面中选择新输入个性化信息选项后，如果该车辆处于联网状态，便可以通过T-BOX将云端服务器保存的个性化信息直接同步至整机控制器中。
具体地，针对第二种方式，考虑到有的车辆中未设置T-BOX，或者T-BOX功能损坏，或者设置了T-BOX，但是车辆不能够正常联网，此时，车辆不能采用T-BOX获取云端发送的新增个性化信息的方式，因此，在此种情况下，可以在车辆中设置二维码识别装置。
具体实现时，用户可以使用移动终端(可以是手机、pad、笔记本电脑等)下载APP，直接在APP中录入个性化信息，使用移动终端APP提供的二维码生成功能，将用户录入的个性化信息生成个性化信息二维码，然后再将生成的个性化信息二维码提供给车辆中的二维码识别装置进行识别，从而使得整机控制器获得个性化信息。
具体地，针对第三种方式，用户则可以在启动个性化驾驶模式之后，整机控制器的人机交互界面可以直接提供个性化信息录入界面，如此用户便可以直接在该界面输入的新增个性化信息。相较于前两种个性化信息录入方式， 第三种个性化信息录入方式需要用户临时录入所有个性化信息，更加耗时。
采用第一种整机控制器获取用户设置的个性化信息的方式，使得用户可以随时随地，并且远程设置个性化信息，使得在车辆启动个性化驾驶模式之后便能够直接获取个性化信息，减少临时录入个性化信息花费的时间，前提条件是车辆需要处于联网状态；采用第二种整机控制器获取用户设置的个性化信息的方式，用户同样可以随时随地，并且远程设置个性化信息，只需要通过二维码识别装置识别个性化信息二维码即可，同样可以减少临时录入个性化信息花费的时间；采用第三种整机控制器获取用户设置的个性化信息的方式，需要用户临时录入所有个性化信息，增加临时录入个性化信息花费的时间。总的来说，在用户是初次设置某个个性化信息时，本公开可以提供多种个性化信息设置方式，提高个性化信息设置的灵活性。
在一种实施方式中，车辆中还设置有HUT(Head Unit，主机)，T-BOX会将云端服务器保存的个性化信息首先同步至HUT中，或者二维码识别装置识别获得的个性化信息首先同步至HUT中，或者用户在HUT界面输入个性化信息，在HUT获取新增个性化信息之后，再将新增个性化信息以CAN报文形式发给整机控制器。
结合以上实施例，如果用户是初次设置某个个性化信息，整机控制器在执行上述步骤S12时，具体可以包括以下步骤：
步骤S12A，在所述个性化信息为新增个性化信息的情况下，对所述新增个性化信息进行特征提取，获得第一特征数据。
本实施例中，如果用户是初次设置某个个性化信息，即整机控制器获取的个性化信息为新增个性化信息，可以说明用户以前没有启动过个性化驾驶模式，或者，虽然启动过个性化驾驶模式，但是没有设置该个性化信息，因此，整机控制器内存中不会存储有对应的目标控制参数。
由于个性化信息是用户根据自身驾驶需求录入的，是与用户相关的数据，并不能直接表达成目标控制参数，因此，首先需要对个性化信息进行特征提取，获得第一特征数据，以便于将个性化信息转换为整机控制器能够识别的目标控制参数。其中，第一特征数据可以表达为数列的形式，并且，个性化信息中的每一项分别对应数列中的一行。
步骤S12B，根据预设转换规则，对所述第一特征数据进行转换，获得第 一个性化因子。
本实施例中，预设转换规则是根据设计人员长期经验以及实验数据得到的，预设转换规则可以是转换矩阵的形式，或者标定转换表的形式，本申请对预设转换规则的内容不做限定。
因此，具体实现时，可以使用转换矩阵或者标定转换表对第一特征数据进行转换，从而获得第一个性化因子，第一个性化因子同样只能反映用户相关数据，并不能被车辆直接识别。
步骤S12C，根据所述第一个性化因子，生成所述整机控制器的第一目标控制参数。
本实施例中，在获得第一个性化因子之后，整机控制器便可以根据第一个性化因子，生成整机控制器的第一目标控制参数。
此外，考虑到每个车辆均存在客观物理参数范围限制，即，每个车辆的控制参数范围不能无限制选择，根据第一个性化因子生成的第一目标控制参数必须在车辆的物理参数范围内，因此，在一种实施方式中，上述步骤S12C，具体可以包括以下步骤：
步骤S12C1，根据所述第一个性化因子，生成初始物理特性参数。
步骤S12C2，对所述初始物理特性参数进行回归处理，得到标准物理特性参数。
步骤S12C3，根据所述车辆的物理参数范围，对所述标准物理特性参数进行调整，生成所述第一目标控制参数。
本实施例中，初始物理特性参数是指根据第一个性化因子直接生成的，车辆物理功能可接受的物理特性参数，例如，电机发出的扭矩、加速踏板开度、蠕行车速、能量回收强度等。根据第一个性化因子，生成初始物理特性参数的具体过程可参考相关技术中，传统驾驶模式设计过程中个性化因子生成物理特性参数的过程，此处不进行详述。
此外，再考虑到根据第一个性化因子，生成的初始物理特性参数并不一定都是合理的或者有效的，因此，还需要对初始物理特性参数进行有效性或者合理性校验，即对初始物理特性参数进行回归处理，得到标准物理特性参数。
在得到标准物理特性参数之后，再考虑到每个车辆均存在客观物理参数 范围限制，最后再根据车辆的物理参数范围进行调整，生成第一目标控制参数。此时的第一目标控制参数既能够被车辆物理参数接受，同时还是合理有效的，并且也符合车辆的物理参数范围限制，可以作为车辆在个性化驾驶模式下的控制参数。
在一种实施方式中，在生成整机控制器的第一目标控制参数之后，本实施例中的个性化驾驶模式设置方法还可以包括以下步骤：
步骤S12D，建立所述新增个性化信息和所述第一目标控制参数之间的关联关系。
步骤S12E，将所述新增个性化信息和所述第一目标控制参数之间的关联关系作为一条历史个性化信息进行保存。
本实施例中，整机控制器在每次获得新增个性化信息，并根据新增个性化信息生成第一目标控制参数之后，均可以将该新增个性化信息与对应第一目标控制参数进行关联保存，即建立新增个性化信息和第一目标控制参数之间的关联关系，并将新增个性化信息和第一目标控制参数之间的关联关系作为一条历史个性化信息进行保存，如此，能够记录并完善历史个性化信息，以便于后续同一用户有同一驾驶需求的情况下，可以直接选择该历史个性化信息，使得整机控制器直接输出目标控制参数，简化用户使用个性化驾驶模式的过程。
此外，考虑到新增个性化信息均为用户根据预设项输入的个性化信息，例如，驾驶风格一项，用户录入的是舒适模式，可以看出预设项通常录入的是一个比较宽泛的内容，因此反映的数据往往是有限的，不能准确反映用户真实驾驶行为或者习惯，因此，为了能够更加准确反映用户真实驾驶行为或者习惯，在一种实施方式中，在步骤S12C中，生成整机控制器的第一目标控制参数之后，本申请的个性化驾驶模式设置方法还可以包括以下步骤：
步骤S131A，获取用户的个性化驾驶行为数据。
其中，所述个性化驾驶行为数据为车辆在所述个性化驾驶模式下用户产生的驾驶行为数据。例如可以是驾驶行为表现，用户习惯使用的油门踏板范围是50-80km/h，或者用户驾驶过程中习惯把空调开成第二档。
本实施例中，整机控制器生成第一控制参数之后，根据第一目标控制参数调整车辆各系统功能，使车辆运行在个性化模式下，此时，用户便可以在 个性化模式下驾驶车辆。由于在个性化模式下驾驶车辆的过程中，用户会产生驾驶行为数据，因此，整机控制器便可以获取用户的个性化驾驶行为数据。
步骤S131B，对所述个性化驾驶行为数据进行特征提取，获得第二特征数据。
该步骤S131B的过程与步骤S12A的过程类似，相关过程参照步骤S12A的描述即可。
步骤S131C，根据所述预设转换规则，对所述第二特征数据进行转换，获得第二个性化因子。
同样地，该步骤S131C的过程与步骤S12BA的过程类似，相关过程参照步骤S12B的描述即可。
步骤S131D，根据所述第一个性化因子、所述第二个性化因子，以及所述第一个性化因子和所述第二个性化因子两者之间的预设比例关系，获得所述整机控制器的第二目标控制参数。
本实施例中，由于第一个性化因子是根据用户录入的个性化信息生成的，第二个性化因子是根据用户在个性化驾驶模式下产生的驾驶行为数据生成的，两者可以对应不一样的权重，因此，在生成整机控制器的第一目标控制参数时，可以预先设置第一个性化因子和第二个性化因子两者之间的预设比例关系，再根据第一个性化因子、第二个性化因子，以及第一个性化因子和第二个性化因子两者之间的预设比例关系确定融合因子，最后根据融合因子生成整机控制器的第二目标控制参数。
示例地，第一个性化因子Z1和第二个性化因子Z2两者之间的预设比例关系可以为2：1，此时融合因子Z的计算方法可以为Z＝2Z1+Z2。需要说明的是，本申请只是给出了第一个性化因子Z1和第二个性化因子Z2两者之间的一个示例性预设比例关系，不应该理解为对第一个性化因子Z1和第二个性化因子Z2两者之间预设比例关系的限定，第一个性化因子Z1和第二个性化因子Z2两者之间预设比例关系是根据实际情况设定的。
本实施例中，整机控制器在生成目标控制参数时，可以同时结合用户录入的个性化信息以及用户在个性化驾驶模式下产生的驾驶行为数据，即根据用户在个性化驾驶模式下真实驾驶行为数据进行自学习，并利用自学习获得的第二个性化因子对目标控制参数进行修正，使得整机控制器生成的目标控 制参数能够更加符合用户实际驾驶行为和习惯，从而为用户提供最合适的驾驶模式以及个性化的驾驶感受。
此外，进一步考虑到新增个性化信息反映的数据往往是有限的，可能与用户当前所在区域的最佳驾驶行为或者习惯存在差别，因此，除了使用用户在个性化驾驶模式下真实驾驶行为数据对目标控制参数进行修正之外，还可以使用其它数据对目标控制参数进行修正，例如用户所在区域的驾驶行为统计数据。
其中，用户所在区域的驾驶行为统计数据可以是云端服务器采集用户车辆所在区域的所有车辆的各种驾驶行为统计数据，例如，包括但不限于不同城市同类型车辆的日最高车速。通常情况下，可以根据同类型车辆的日最高车速调整踏板开度参数。
因此，在另一种实施方式中，在步骤S12C中，生成整机控制器的第一目标控制参数之后，本申请的个性化驾驶模式设置方法还可以包括以下步骤：
步骤S132A，获取用户所在区域的驾驶行为统计数据。
步骤S132B，对所述驾驶行为统计数据进行特征提取，获得第三特征数据。
步骤S132C，根据所述预设转换规则，对所述第三特征数据进行转换，获得第三个性化因子。
步骤S132D，根据所述第一个性化因子、所述第三个性化因子，以及所述第一个性化因子和所述第三个性化因子两者之间的预设比例关系，生成所述整机控制器的第三目标控制参数。
本实施例中，同样地，由于第一个性化因子是根据用户录入的个性化信息生成的，第三个性化因子是根据用户所在区域的驾驶行为统计数据生成的，两者也可以对应不一样的权重，因此，在生成整机控制器的第一目标控制参数时，可以预先设置第一个性化因子和第三个性化因子两者之间的预设比例关系，再根据第一个性化因子、第三个性化因子，以及第一个性化因子和第三个性化因子两者之间的预设比例关系确定融合因子，最后根据融合因子生成整机控制器的第三目标控制参数。
本实施例中，整机控制器在生成目标控制参数时，可以同时结合用户录入的个性化信息以及用户所在区域的驾驶行为统计数据，即根据用户所在区 域的驾驶行为统计数据对目标控制参数进行修正，使得整机控制器生成的目标控制参数能够参考用户所在区域的其它用户的驾驶行为或者习惯，从而进一步为用户提供当前所在区域下，最合适的驾驶模式以及个性化的驾驶感受。
此外，在另一种实施方式中，也可以同时使用用户在个性化驾驶模式下真实驾驶行为数据和用户所在区域的驾驶行为统计数据，对目标控制参数进行修正，因此，在步骤S12C中，生成整机控制器的第一目标控制参数之后，本申请的个性化驾驶模式设置方法还可以包括以下步骤：
步骤S133A，获取用户的个性化驾驶行为数据，以及用户所在区域的驾驶行为统计数据，所述个性化驾驶行为数据为车辆在所述个性化驾驶模式下用户产生的驾驶行为数据。
步骤S133B，对所述个性化驾驶行为数据进行特征提取，获得第二特征数据，以及，对所述驾驶行为统计数据进行特征提取，获得第三特征数据。
步骤S133C，根据所述预设转换规则，对所述第二特征数据进行转换，获得第二个性化因子，以及对所述第三特征数据进行转换，获得第三个性化因子。
步骤S133D，根据所述第一个性化因子、所述第二个性化因子、所述第三个性化因子以及所述第一个性化因子、所述第二个性化因子和所述第三个性化因子三者之间的预设比例关系，生成所述整机控制器的第四目标控制参数。
上述步骤S131A-S131D的过程与步骤S132A-S132D以及步骤S133A-S133D的过程类似，相关过程参照步骤S132A-S132D以及步骤S133A-S133D的描述即可。
本实施例中，同时使用用户在个性化驾驶模式下真实驾驶行为数据和用户所在区域的驾驶行为统计数据，对目标控制参数进行修正，可以进一步为用户提供合适的驾驶模式以及个性化的驾驶感受。
参照图2，图2示出了本公开实施例提供的一种个性化驾驶模式设置方法的完整流程示意图。如图2所示，流程如下：
1、个性化信息录入，并进行特征提取，获得第一个性化因子Z1。
2、大数据采集并录入(用户所在区域的驾驶行为统计数据录入，并进行特征提取，获得第三个性化因子Z3)。
3、将第一个性化因子和第三个性化因子进行特征融合，生成融合因子Z。
4、根据融合因子Z，生成初始物理特性参数。
5、整机控制器对初始物理特性参数进行调整(回归处理，并根据车辆物理参数范围进行调整)，获得目标控制参数。
6、将目标控制参数输入到整机控制器的标定数据接口，调整车辆驾驶表现(各系统功能)。
7、记录用户的个性化驾驶行为数据(驾驶表现数据和车辆行驶状态统计)。
8、整机控制器自学习(特征提取，并对提取得到的特征数据进行转换)用户的个性化驾驶行为数据，并反馈获得第二个性化因子。
9、将第一个性化因子、第二个性化因子和第三个性化因子进行特征融合，生成融合因子Z，并返回步骤4。
基于相同的技术构思，请参考图3，图3示出了本公开实施例的一种个性化驾驶模式设置系统30的结构框图，所述系统应用于车辆中设置的整机控制器，所述系统包括：
第一获取模块31，用于在检测到车辆启动个性化驾驶模式的情况下，获取用户设置的个性化信息；
第一生成模块32，用于根据所述个性化信息的信息类型以及预先配置的信息处理策略，对所述个性化信息进行处理，生成所述整机控制器的目标控制参数，所述目标控制参数为所述车辆在个性化驾驶模式下的控制参数。
可选地，所述第一获取模块，包括：
第一获取子模块，用于在检测到用户触发对历史个性化信息的选择操作的情况下，获取自身预存的所述历史个性化信息；
第二获取子模块，用于获取云端发送的新增个性化信息，所述新增个性化信息是用户在移动终端输入后，发送到所述云端保存的；
第三获取子模块，用于识别个性化信息二维码，获取所述新增个性化信息，所述个性化信息二维码是根据用户在移动终端输入的个性化信息生成的；
第四获取子模块，用于获取用户在所述整机控制器的人机交互界面输入的所述新增个性化信息。
可选地，所述第一生成模块，包括：
第五获得子模块，用于在所述个性化信息为历史个性化信息的情况下， 根据预先存储的历史个性化信息与控制参数的关联关系，获得与所述历史个性化信息对应的目标控制参数。
可选地，所述第一生成模块，包括：
第一特征提取子模块，用于在所述个性化信息为新增个性化信息的情况下，对所述新增个性化信息进行特征提取，获得第一特征数据；
第一转换子模块，用于根据预设转换规则，对所述第一特征数据进行转换，获得第一个性化因子；
生成子模块，用于根据所述第一个性化因子，生成所述整机控制器的第一目标控制参数。
可选地，所述生成子模块，包括：
第一生成子单元，用于根据所述第一个性化因子，生成初始物理特性参数；
回归处理子单元，用于对所述初始物理特性参数进行回归处理，得到标准物理特性参数；
调整子单元，用于根据所述车辆的物理参数范围，对所述标准物理特性参数进行调整，生成所述第一目标控制参数。
可选地，所述系统还包括：
创建模块，用于建立所述新增个性化信息和所述第一目标控制参数之间的关联关系；
保存模块，用于将所述新增个性化信息和所述第一目标控制参数之间的关联关系作为一条历史个性化信息进行保存。
可选地，所述系统还包括：
第二获取模块，用于获取用户的个性化驾驶行为数据，所述个性化驾驶行为数据为车辆在所述个性化驾驶模式下用户产生的驾驶行为数据；
第一特征提取模块，用于对所述个性化驾驶行为数据进行特征提取，获得第二特征数据；
第一转换模块，用于根据所述预设转换规则，对所述第二特征数据进行转换，获得第二个性化因子；
第二生成模块，用于根据所述第一个性化因子、所述第二个性化因子，以及所述第一个性化因子和所述第二个性化因子两者之间的预设比例关系， 生成所述整机控制器的第二目标控制参数。
可选地，所述系统包括：
第三获取模块，用于获取用户所在区域的驾驶行为统计数据；
第二特征提取模块，用于对所述驾驶行为统计数据进行特征提取，获得第三特征数据；
第二转换模块，用于根据所述预设转换规则，对所述第三特征数据进行转换，获得第三个性化因子；
第三生成模块，用于根据所述第一个性化因子、所述第三个性化因子，以及所述第一个性化因子和所述第三个性化因子两者之间的预设比例关系，生成所述整机控制器的第三目标控制参数。
可选地，所述系统还包括：
第四获取模块，用于获取用户的个性化驾驶行为数据，以及用户所在区域的驾驶行为统计数据，所述个性化驾驶行为数据为车辆在所述个性化驾驶模式下用户产生的驾驶行为数据；
第三特征提取模块，用于对所述个性化驾驶行为数据进行特征提取，获得第二特征数据，以及，对所述驾驶行为统计数据进行特征提取，获得第三特征数据；
第三转换模块，用于根据所述预设转换规则，对所述第二特征数据进行转换，获得第二个性化因子，以及对所述第三特征数据进行转换，获得第三个性化因子；
第四生成模块，用于根据所述第一个性化因子、所述第二个性化因子、所述第三个性化因子以及所述第一个性化因子、所述第二个性化因子和所述第三个性化因子三者之间的预设比例关系，生成所述整机控制器的第四目标控制参数。
对于系统实施例而言，由于其与方法实施例基本相似，所以描述的比较简单，相关之处参见方法实施例的部分说明即可。
以上所描述的装置实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例 方案的目的。本领域普通技术人员在不付出创造性的劳动的情况下，即可以理解并实施。
本公开的各个部件实施例可以以硬件实现，或者以在一个或者多个处理器上运行的软件模块实现，或者以它们的组合实现。本领域的技术人员应当理解，可以在实践中使用微处理器或者数字信号处理器(DSP)来实现根据本公开实施例的计算处理设备中的一些或者全部部件的一些或者全部功能。本公开还可以实现为用于执行这里所描述的方法的一部分或者全部的设备或者装置程序(例如，计算机程序和计算机程序产品)。这样的实现本公开的程序可以存储在计算机可读介质上，或者可以具有一个或者多个信号的形式。这样的信号可以从因特网网站上下载得到，或者在载体信号上提供，或者以任何其他形式提供。
例如，图4示出了可以实现根据本公开的方法的计算处理设备。该计算处理设备传统上包括处理器1010和以存储器1020形式的计算机程序产品或者计算机可读介质。存储器1020可以是诸如闪存、EEPROM(电可擦除可编程只读存储器)、EPROM、硬盘或者ROM之类的电子存储器。存储器1020具有用于执行上述方法中的任何方法步骤的程序代码1031的存储空间1030。例如，用于程序代码的存储空间1030可以包括分别用于实现上面的方法中的各种步骤的各个程序代码1031。这些程序代码可以从一个或者多个计算机程序产品中读出或者写入到这一个或者多个计算机程序产品中。这些计算机程序产品包括诸如硬盘，紧致盘(CD)、存储卡或者软盘之类的程序代码载体。这样的计算机程序产品通常为如参考图5所述的便携式或者固定存储单元。该存储单元可以具有与图4的计算处理设备中的存储器1020类似布置的存储段、存储空间等。程序代码可以例如以适当形式进行压缩。通常，存储单元包括计算机可读代码1031’，即可以由例如诸如1010之类的处理器读取的代码，这些代码当由计算处理设备运行时，导致该计算处理设备执行上面所描述的方法中的各个步骤。
本说明书实施例的各种实施方式均采用递进的方式描述，每个实施方式重点说明的都是与其他实施方式的不同之处，各个实施方式之间相同相似的部分互相参见即可。
尽管已描述了本公开实施例的优选实施例，但本领域内的技术人员一旦 得知了基本创造性概念，则可对这些实施例做出另外的变更和修改。所以，所附权利要求意欲解释为包括优选实施例以及落入本公开实施例范围的所有变更和修改。
最后，还需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者电子设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者电子设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者电子设备中还存在另外的相同要素。
以上对本公开所提供的一种个性化驾驶模式设置方法、一种个性化驾驶模式设置系统、一种电子设备和一种计算机可读存储介质进行了详细介绍，本文中应用了具体个例对本公开的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本公开的方法及其核心思想；同时，对于本领域的一般技术人员，依据本公开的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本公开的限制。

Claims (14)

  1. 一种个性化驾驶模式设置方法，其特征在于，应用于车辆中设置的整机控制器，所述方法包括：
    在检测到车辆启动个性化驾驶模式的情况下，获取用户设置的个性化信息；
    根据所述个性化信息的信息类型以及预先配置的信息处理策略，对所述个性化信息进行处理，生成所述整机控制器的目标控制参数，所述目标控制参数为所述车辆在个性化驾驶模式下的控制参数。
  2. 根据权利要求1所述的方法，其特征在于，所述获取用户设置的个性化信息，包括以下任一者：
    在检测到用户触发对历史个性化信息的选择操作的情况下，获取自身预存的所述历史个性化信息；
    获取云端发送的新增个性化信息，所述新增个性化信息是用户在移动终端输入后，发送到所述云端保存的；
    识别个性化信息二维码，获取所述新增个性化信息，所述个性化信息二维码是根据用户在移动终端输入的个性化信息生成的；
    获取用户在所述整机控制器的人机交互界面输入的所述新增个性化信息。
  3. 根据权利要求2所述的方法，其特征在于，根据所述个性化信息的信息类型以及预先配置的信息处理策略，对所述个性化信息进行处理，生成所述整机控制器的目标控制参数，包括：
    在所述个性化信息为历史个性化信息的情况下，根据预先存储的历史个性化信息与控制参数的关联关系，获得与所述历史个性化信息对应的目标控制参数。
  4. 根据权利要求2所述的方法，其特征在于，根据所述个性化信息的信息类型以及预先配置的信息处理策略，对所述个性化信息进行处理，生成所述整机控制器的目标控制参数，包括：
    在所述个性化信息为新增个性化信息的情况下，对所述新增个性化信息进行特征提取，获得第一特征数据；
    根据预设转换规则，对所述第一特征数据进行转换，获得第一个性化因子；
    根据所述第一个性化因子，生成所述整机控制器的第一目标控制参数。
  5. 根据权利要求4所述的方法，其特征在于，所述根据所述第一个性化 因子，生成所述整机控制器的第一目标控制参数，包括：
    根据所述第一个性化因子，生成初始物理特性参数；
    对所述初始物理特性参数进行回归处理，得到标准物理特性参数；
    根据所述车辆的物理参数范围，对所述标准物理特性参数进行调整，生成所述第一目标控制参数。
  6. 根据权利要求4或5所述的方法，其特征在于，在生成所述整机控制器的第一目标控制参数之后，所述方法还包括：
    建立所述新增个性化信息和所述第一目标控制参数之间的关联关系；
    将所述新增个性化信息和所述第一目标控制参数之间的关联关系作为一条历史个性化信息进行保存。
  7. 根据权利要求4所述的方法，其特征在于，在生成所述整机控制器的第一目标控制参数之后，所述方法还包括：
    获取用户的个性化驾驶行为数据，所述个性化驾驶行为数据为车辆在所述个性化驾驶模式下用户产生的驾驶行为数据；
    对所述个性化驾驶行为数据进行特征提取，获得第二特征数据；
    根据所述预设转换规则，对所述第二特征数据进行转换，获得第二个性化因子；
    根据所述第一个性化因子、所述第二个性化因子，以及所述第一个性化因子和所述第二个性化因子两者之间的预设比例关系，生成所述整机控制器的第二目标控制参数。
  8. 根据权利要求4所述的方法，其特征在于，在生成所述整机控制器的第一目标控制参数之后，所述方法还包括：
    获取用户所在区域的驾驶行为统计数据；
    对所述驾驶行为统计数据进行特征提取，获得第三特征数据；
    根据所述预设转换规则，对所述第三特征数据进行转换，获得第三个性化因子；
    根据所述第一个性化因子、所述第三个性化因子，以及所述第一个性化因子和所述第三个性化因子两者之间的预设比例关系，生成所述整机控制器的第三目标控制参数。
  9. 根据权利要求4所述的方法，其特征在于，在生成所述整机控制器的第一目标控制参数之后，所述方法还包括：
    获取用户的个性化驾驶行为数据，以及用户所在区域的驾驶行为统计数 据，所述个性化驾驶行为数据为车辆在所述个性化驾驶模式下用户产生的驾驶行为数据；
    对所述个性化驾驶行为数据进行特征提取，获得第二特征数据，以及，对所述驾驶行为统计数据进行特征提取，获得第三特征数据；
    根据所述预设转换规则，对所述第二特征数据进行转换，获得第二个性化因子，以及对所述第三特征数据进行转换，获得第三个性化因子；
    根据所述第一个性化因子、所述第二个性化因子、所述第三个性化因子以及所述第一个性化因子、所述第二个性化因子和所述第三个性化因子三者之间的预设比例关系，生成所述整机控制器的第四目标控制参数。
  10. 一种个性化驾驶模式设置系统，其特征在于，应用于车辆中设置的整机控制器，所述系统包括：
    第一获取模块，用于在检测到车辆启动个性化驾驶模式的情况下，获取用户设置的个性化信息；
    第一生成模块，用于根据所述个性化信息的信息类型以及预先配置的信息处理策略，对所述个性化信息进行处理，生成所述整机控制器的目标控制参数，所述目标控制参数为所述车辆在个性化驾驶模式下的控制参数。
  11. 一种车辆，其特征在于，包括如权利要求10所述的个性化驾驶模式设置系统。
  12. 一种计算处理设备，其特征在于，包括：
    存储器，其中存储有计算机可读代码；以及
    一个或多个处理器，当所述计算机可读代码被所述一个或多个处理器执行时，所述计算处理设备执行如权利要求1-9中任一项所述的个性化驾驶模式设置方法。
  13. 一种计算机程序，包括计算机可读代码，当所述计算机可读代码在计算处理设备上运行时，导致所述计算处理设备执行根据权利要求1-9中任一项所述的个性化驾驶模式设置方法。
  14. 一种计算机可读介质，其中存储了如权利要求13所述的计算机程序。
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