CN111252077A - Vehicle control method and device - Google Patents
Vehicle control method and device Download PDFInfo
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- CN111252077A CN111252077A CN202010123769.8A CN202010123769A CN111252077A CN 111252077 A CN111252077 A CN 111252077A CN 202010123769 A CN202010123769 A CN 202010123769A CN 111252077 A CN111252077 A CN 111252077A
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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
- 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
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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
- 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
- B60W2050/0062—Adapting control system settings
- B60W2050/0075—Automatic parameter input, automatic initialising or calibrating means
- B60W2050/0083—Setting, resetting, calibration
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Abstract
The invention discloses a vehicle control method and device, and belongs to the technical field of vehicle control. The method comprises the following steps: matching auxiliary driving data corresponding to the acquired user information according to the acquired user information, and assisting a user to control a vehicle according to the auxiliary driving data, wherein the auxiliary driving data comprises: and operation inertial data acquired by using the machine learning model and operation function mode selection data acquired according to historical data selected by the user for the function mode. The invention increases the self-adaptive function of the ADAS system, carries out adaptive change according to the driving operation habit of the user, is more humanized and intelligent compared with the single factory default setting in the prior art, improves the driving experience of the user and meets the individual requirements of the user.
Description
Technical Field
The invention relates to the technical field of vehicle control, in particular to a vehicle control method and device.
Background
Advanced Driving Assistance system adas (advanced Driving Assistance system) is an intelligent system equipped on a vehicle to assist a driver in Driving the vehicle, which uses sensors installed at various positions on the vehicle to sense the surrounding environment of the vehicle during Driving, collect data, identify, detect and track static and dynamic objects, and combine with navigator map data to perform systematic calculation and analysis, thereby allowing the driver to detect possible dangers in advance and effectively increasing the comfort and safety of Driving the vehicle. Under the general condition, the ADAS function is default when leaving factory, the user cannot change the ADAS function by himself, the ADAS function cannot adapt to the driving habit of the user, specific function requirements cannot be formulated for different users, the automatic driving experience of the user is reduced, various sub-functions need to be manually and frequently turned on or turned off, and the complexity of user operation is increased.
Disclosure of Invention
In order to solve the problems in the prior art, embodiments of the present invention provide a vehicle control method and apparatus.
The technical scheme is as follows:
in a first aspect, a vehicle control method is provided, the method comprising:
matching auxiliary driving data corresponding to the acquired user information according to the acquired user information, and assisting a user to control a vehicle according to the auxiliary driving data, wherein the auxiliary driving data comprises: and operation inertial data acquired by using the machine learning model and operation function mode selection data acquired according to historical data selected by the user for the function mode.
Further, the operation function mode selection data includes: selecting data by a transverse auxiliary control function and/or selecting data by a longitudinal auxiliary control function;
further, assisting the user in controlling the vehicle according to the lateral assist control function selection data or the longitudinal assist control function selection data includes:
and sending a control signal to one or more of a lane deviation early warning system, an automatic braking auxiliary system, a doubling auxiliary or blind area monitoring system, an anti-lock system and an electronic stability program system according to the transverse auxiliary control function selection data or the longitudinal auxiliary control function selection data to control the on-off and parameter setting of the lane deviation early warning system, the automatic braking auxiliary system, the doubling auxiliary or blind area monitoring system, the anti-lock system and the electronic stability program system.
Further, the user information includes: one or more of facial recognition information, fingerprint information, voice print information.
Further, the operational inertial data includes: the optimal steering wheel steering torque; the obtaining of the optimal steering wheel steering torque includes:
collecting steering wheel steering torque, and a steering wheel corner, lane curvature and vehicle running speed corresponding to the steering wheel steering torque;
inputting the vehicle running speed, the steering wheel turning angle, the lane curvature, the steering wheel steering torque and the vehicle posture into a machine learning model of the steering wheel steering torque to obtain the optimal steering wheel steering torque;
associating and storing the optimal steering wheel steering torque with the user information.
Further, assisting the user in controlling the vehicle according to the optimal steering wheel steering torque includes: and controlling a steering power-assisted system according to the optimal steering wheel steering torque, and controlling a steering wheel of the vehicle through the steering power-assisted system.
Further, the operational inertial data includes: optimal pedal sensitivity; the obtaining of the optimal pedal sensitivity includes:
acquiring the force and depth of a pedal operated by a user, and vehicle acceleration and vehicle running speed corresponding to the force and depth of the pedal;
inputting the vehicle running speed, the vehicle acceleration, the pedal force and the depth into a machine learning model of pedal sensitivity to obtain the optimal pedal sensitivity;
associating and storing the optimal pedal sensitivity with the user information.
Further, assisting the user in controlling the vehicle according to the optimal pedal sensitivity includes: and controlling a pedal hydraulic power-assisted system according to the optimal pedal sensitivity, and controlling a pedal of the vehicle through the pedal hydraulic power-assisted system.
In a second aspect, there is provided a vehicle control apparatus, the apparatus comprising:
a storage module to store driving assistance data, the driving assistance data comprising: obtaining operation inertial data by using a machine learning model and operation function mode selection data according to historical data selected by a user for a function mode;
the matching module is used for matching the auxiliary driving data corresponding to the acquired user information according to the acquired user information;
and the control module is used for assisting a user in controlling the vehicle according to the driving assisting data.
Further, the operation function mode selection data includes: selecting data by a transverse auxiliary control function and/or selecting data by a longitudinal auxiliary control function; the control module is specifically used for sending control signals to one or more of a lane deviation early warning system, an automatic braking auxiliary system, a parallel auxiliary or blind area monitoring system, an anti-lock system and an electronic stability program system according to the transverse auxiliary control function selection data or the longitudinal auxiliary control function selection data, and controlling the on-off and parameter setting of the lane deviation early warning system, the automatic braking auxiliary system, the parallel auxiliary or blind area monitoring system and the electronic stability program system.
Further, the user information includes: one or more of facial recognition information, fingerprint information, voice print information.
Further, the apparatus further comprises: the auxiliary driving data acquisition module is used for acquiring auxiliary driving data; the operational inertial data includes: the optimal steering wheel steering torque; the driving assistance data acquisition module includes:
the optimal steering wheel steering torque acquisition module is used for acquiring steering wheel steering torque, and steering wheel turning angles, lane curvatures and vehicle running speeds corresponding to the steering wheel steering torque;
and inputting the vehicle running speed, the steering wheel angle, the lane curvature, the steering wheel steering torque and the vehicle posture into a machine learning model of the steering wheel steering torque to obtain the optimal steering wheel steering torque.
Further, the control module is specifically configured to control a steering assist system according to the optimal steering wheel steering torque, and control a steering wheel of the vehicle through the steering assist system.
Further, the operational inertial data includes: optimal pedal sensitivity; the driving assistance data module further includes:
the optimal pedal sensitivity acquisition module is used for acquiring the force and depth of a pedal operated by a user, and vehicle acceleration and vehicle running speed corresponding to the force and depth of the pedal; and inputting the vehicle running speed, the vehicle acceleration, the pedal force and the depth into a machine learning model of pedal sensitivity to obtain the optimal pedal sensitivity.
Further, the control module is specifically configured to assist a user in controlling the vehicle according to the optimal pedal sensitivity, and specifically includes: and controlling a pedal hydraulic power-assisted system according to the optimal pedal sensitivity, and controlling a pedal of the vehicle through the pedal hydraulic power-assisted system.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
the technical scheme disclosed by the invention increases the self-adaptive function of the ADAS system, changes the adaptability according to the driving operation habit of the user, is more humanized and intelligent compared with the single factory default setting in the prior art, improves the driving experience of the user and meets the individual requirements of the user.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an ADAS module in the prior art according to an embodiment of the present invention;
FIG. 2 is a flow chart of a vehicle control method provided by an embodiment of the present invention;
FIG. 3 is a process diagram of an operational inertial data acquisition method provided by an embodiment of the invention;
fig. 4 is a schematic structural diagram of a module of a vehicle control device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the ADAS system is an auxiliary system for driving a vehicle, and generally implements auxiliary control of vehicle driving by the following systems, which specifically include:
a control unit ECU: and the module is used for receiving the state information of each module and sending an execution module execution instruction. For example, receiving the distance, speed and direction information of the millimeter wave radar, and receiving the image information of the camera; and receiving EPS (steering power assist system) module steering angle information, and the like.
Millimeter wave radar: speed, azimuth, angle information between the vehicle and the target is obtained.
A camera: image information of the target is acquired.
ACC (adaptive cruise system): and acquiring information such as the distance between the vehicle and the front vehicle and the speed of the vehicle, and executing instructions such as acceleration and deceleration.
LDWS (lane departure warning system): and acquiring image information of a camera of the vehicle, and executing instructions such as alarming, steering wheel vibration and automatic steering change.
AEB (automatic brake assist system): and obtaining radar signal information of the vehicle, and executing an alarm and a brake light instruction.
BSM (blind zone monitoring system): and acquiring radar signal information of the vehicle, and executing instructions such as alarm and the like.
ABS (anti-lock braking system): and obtaining the wheel speed information of the four wheels of the vehicle, and executing the four-wheel braking force distribution instruction.
ESP (electronic stability program): obtaining the speed, acceleration, brake pedal and other instructions of the vehicle; and execute speed, acceleration instructions.
EPS (power steering system): obtaining information such as vehicle turning angle and rotating speed; and executes information of the rotation angle, the rotation speed and the like of the control unit.
EMS (engine management system): torque information is acquired and output.
TCU (transmission control unit): unit information is acquired and output.
A central control screen: and acquiring information of parameters set by a user for inputting a horizontal auxiliary class, a longitudinal auxiliary class and an awakening mode, and outputting the parameter setting condition.
The information acquisition module: the method comprises the steps of obtaining a user account, and collecting biological information such as a user face, a fingerprint, a voiceprint and the like.
However, in the ADAS system in the prior art, the setting of the system is factory default, the control unit ECU only controls other systems mechanically, and there is no function that is individually adapted to the driving operation habit of the user, and the function is single. The technical scheme of the invention provides a vehicle control method, a device and a system for increasing the self-adaptive function of an ADAS system, and the main principle is as follows: on one hand, for the steering wheel operation and pedal operation of a user, the collected related data is utilized to carry out learning training through a machine learning model, and corresponding operation inertia data is output; on the other hand, the modularized function selection of the vehicle can be turned on or off according to the historical option information of the user, and the user does not need to operate the modularized function selection. The specific scheme disclosed by the invention is as follows:
example 1
As shown in fig. 2, a vehicle control method includes the steps of:
and S1, acquiring user information, wherein the user information can be one or more of account information, facial recognition information, fingerprint information and voiceprint information of the user.
S2, matching and calling corresponding driving assistance data according to the user information, wherein the driving assistance data comprises: and operation inertial data acquired by using the machine learning model and operation function mode selection data acquired according to historical data selected by the user for the function mode.
And S3, assisting a user to control the vehicle according to the driving assisting data, specifically, controlling corresponding equipment on the vehicle according to the operation inertia data, and selecting data to control the switch and parameter setting of each function system on the vehicle according to the operation function mode.
In the above method, the account information of the user in step S1 may be obtained by user' S own input, the facial recognition information may be obtained by using a facial recognition technology through a camera device in the vehicle, the fingerprint information may be obtained by using a fingerprint recognition device installed on the vehicle, and the voiceprint information may be obtained by using a voice recognition device installed on the vehicle.
The operating the inertia data in step S2 includes: an optimal steering wheel steering torque, which is the torque experienced by the steering wheel when the user operates the steering wheel, and/or an optimal pedal sensitivity. The pedal sensitivity refers to the force and the descending depth of the pedal when a user steps on the pedal, and comprises the sensitivity of an accelerator pedal and the sensitivity of a brake pedal.
As shown in fig. 3, the acquisition of the optimal steering wheel steering torque includes:
acquiring user information of a user, and acquiring steering wheel steering torque of the user during driving, and a steering wheel corner, lane curvature and vehicle running speed corresponding to the steering wheel steering torque;
and inputting the running speed of the vehicle, the steering wheel angle, the lane curvature, the steering moment of the steering wheel and the posture of the vehicle into a machine learning model of the steering moment of the steering wheel to obtain the optimal steering moment of the steering wheel.
In the method, the machine learning model of the steering wheel steering torque can be a neural network model, the neural network is a complex network system formed by widely connecting a large number of simple processing units (called neurons), and the method has large-scale parallel, distributed storage and processing, self-organization, self-adaptation and self-learning capabilities, and is particularly suitable for processing inaccurate and fuzzy information processing problems needing to consider a plurality of factors and conditions simultaneously. The embodiment preferably selects a convolutional neural network model, which is a feedforward neural network: the device comprises an input layer, a hidden layer and an output layer, and is trained and processed by sample training data of steering torque of a steering wheel in advance. The self-vehicle posture represents the relative offset turning angle of the vehicle body.
As shown in fig. 3, the acquisition of the optimal pedal sensitivity includes:
acquiring user information of a user, and acquiring the force and depth of the user operating a pedal of the user, and vehicle acceleration and vehicle running speed corresponding to the force and depth of the pedal;
and inputting the vehicle running speed, the vehicle acceleration, the pedal force and the depth into a machine learning model of the pedal sensitivity to obtain the optimal pedal sensitivity.
In the above method, the pedal may be an accelerator pedal or a brake pedal of the vehicle. The machine learning model of the pedal sensitivity can be a neural network model, the model comprises an input layer, a hidden layer and an output layer, and the model is trained by sample data of the pedal sensitivity in advance.
The operating function mode selection data in step S2 includes: the driving mode selection data, the transverse auxiliary control function selection data and the longitudinal auxiliary control function selection data. The driving mode selection data is the driving mode of the current driving obtained according to the selection condition data of various driving modes in the historical operation of the user. The driving modes may include: standard driving mode, economy driving mode, sport driving mode. The lateral assist control function and the longitudinal assist control function are assist controls for stability and safety in running of the vehicle, which are cooperatively controlled by a plurality of systems on the plurality of vehicles in combination. The transverse auxiliary control function selection data is selection data obtained according to one or more selection condition data of a lane deviation early warning system, an automatic braking auxiliary system, a doubling auxiliary or blind area monitoring system, an anti-lock system and an electronic stability program system in the previous driving of a user, and can be the latest selection data of the user or the most times selection data of the user. The longitudinal auxiliary control function selection data is selection data obtained according to one or more historical selection condition data of a lane deviation early warning system, an automatic braking auxiliary system, a doubling auxiliary or blind area monitoring system, an anti-lock system and an electronic stability program system, and can be the latest selection data of a user or the most times selection data of the user.
It should be noted that, in step S2, the operation inertia data and the operation function mode selection data are both obtained according to the operation and selection data of the user collected at the beginning of the user information establishment, and may also be obtained according to the operation inertia data and the operation function mode selection data transmitted by the user in another vehicle.
In step S3, the steering wheel steering torque is controlled and adjusted by the power steering system, and the assisting user in controlling the vehicle according to the optimal steering wheel steering torque includes: and controlling a steering power-assisted system according to the optimal steering torque of the steering wheel, and controlling the steering wheel of the vehicle through the steering power-assisted system. The pedal sensitivity is controlled and adjusted by a hydraulic power-assisted system of the pedal, and the vehicle is assisted to be controlled by a user according to the optimal pedal sensitivity, and the method comprises the following steps: and controlling a pedal hydraulic power-assisted system according to the optimal pedal sensitivity, and controlling the pedal of the vehicle through the pedal hydraulic power-assisted system.
The step S3 of controlling the switches and parameter settings of the functional systems on the vehicle according to the operation function mode selection data includes: and sending a starting signal to a driving mode system according to the driving mode selection data to start a corresponding driving mode. And sending a control signal to one or more of a lane deviation early warning system, an automatic braking auxiliary system, a doubling auxiliary or blind area monitoring system, an anti-lock system and an electronic stability program system according to the transverse auxiliary control function selection data or the longitudinal auxiliary control function selection data to control the on-off and parameter setting of the lane deviation early warning system, the automatic braking auxiliary system, the doubling auxiliary or blind area monitoring system, the anti-lock system and the electronic stability program system.
The customized vehicle control method disclosed by the invention increases the ADAS system self-adaptability, realizes the personalized auxiliary control function of the vehicle, improves the driving experience of the user and meets the user requirements.
Example 2
As shown in fig. 4, in order to implement the method disclosed in embodiment 1, the present embodiment provides a vehicle control apparatus based on embodiment 1, including:
the user information acquisition module is used for acquiring user information, wherein the user information comprises: the account information of the user, the face identification information, the fingerprint information and the voiceprint information.
The auxiliary driving data acquisition module is used for acquiring auxiliary driving data, and the auxiliary driving data comprises: operating inertial data and operating functional mode selection data. The driving assistance data acquisition module therefore includes:
the operation inertia data acquisition module is used for acquiring operation inertia data by utilizing the machine learning model; the operational inertial data acquisition module includes:
the optimal steering wheel steering torque acquisition module is used for acquiring the optimal steering wheel steering torque;
and the optimal pedal sensitivity acquisition module is used for acquiring optimal pedal sensitivity.
An operation function mode selection data acquisition module, configured to acquire operation function mode selection data according to historical data selected by a user for a function mode, where the operation function mode selection data acquisition module includes:
the driving mode selection data acquisition module is used for acquiring driving mode selection data;
the system comprises a transverse auxiliary control function selection data acquisition module, a transverse auxiliary control function selection data acquisition module and a transverse auxiliary control function selection data acquisition module, wherein the transverse auxiliary control function selection data acquisition module is used for acquiring transverse auxiliary control function selection data, and specifically comprises switching information and operation parameters of a lane deviation early warning system, an automatic brake auxiliary system, a parallel line auxiliary or blind area monitoring system, an anti-lock system and an electronic stability program system which are related to a transverse auxiliary control function;
the longitudinal auxiliary control function selecting data acquiring module is used for acquiring longitudinal auxiliary control function selecting data, and specifically comprises switching information and operating parameters of a lane deviation early warning system, an automatic braking auxiliary system, a parallel line auxiliary or blind area monitoring system, an anti-lock system and an electronic stability program system which are related to the longitudinal auxiliary control function.
And the matching module is used for matching the auxiliary driving data corresponding to the acquired user information according to the acquired user information.
The storage module is used for storing the auxiliary driving data and specifically comprises: the inertial data storage module is operated, and the functional mode selection data storage module is operated.
The control module is used for assisting a user in controlling the vehicle according to the driving assisting data, and specifically comprises: .
It should be noted that the device disclosed in the present embodiment may be provided in the ECU of the ADAS system or replace the ECU of the existing ADAS system.
Wherein, user information acquisition module and information acquisition module communication connection can include: one or more of an account identification module, a face identification module, a fingerprint identification module and a voiceprint identification module.
The driving assistance data acquisition module can be in communication connection with other systems in the ADAS system.
The storage module is in communication connection with the auxiliary driving data acquisition module.
The matching module is in communication connection with the storage module and can inquire the auxiliary driving data corresponding to the user information. Specifically, the driving assistance data of one user corresponds to one number, the matching module stores the corresponding relation between the user information and the number, and the corresponding driving assistance data is inquired according to the corresponding relation.
The control module is in communication connection with other systems in the ADAS system and controls the operation of each module.
The device provided by the embodiment can provide personalized driving assistance service according to the driving operation habits of the user, and improves the driving experience of the user.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
the technical scheme disclosed by the invention increases the self-adaptive function of the ADAS system, changes the adaptability according to the driving operation habit of the user, is more humanized and intelligent compared with the single factory default setting in the prior art, improves the driving experience of the user and meets the individual requirements of the user.
All the above-mentioned optional technical solutions can be combined arbitrarily to form the optional embodiments of the present invention, and are not described herein again.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A vehicle control method characterized by comprising:
matching auxiliary driving data corresponding to the acquired user information according to the acquired user information, and assisting a user to control a vehicle according to the auxiliary driving data, wherein the auxiliary driving data comprises: and operation inertial data acquired by using the machine learning model and operation function mode selection data acquired according to historical data selected by the user for the function mode.
2. A vehicle control method as claimed in claim 1, wherein said operating function mode selection data comprises: transverse auxiliary control function selection data and/or longitudinal auxiliary control function selection data.
3. A vehicle control method as claimed in claim 2, wherein assisting the user in controlling the vehicle in accordance with the lateral assist control function selection data or the longitudinal assist control function selection data comprises:
and sending a control signal to one or more of a lane deviation early warning system, an automatic braking auxiliary system, a doubling auxiliary or blind area monitoring system, an anti-lock system and an electronic stability program system according to the transverse auxiliary control function selection data or the longitudinal auxiliary control function selection data to control the on-off and parameter setting of the lane deviation early warning system, the automatic braking auxiliary system, the doubling auxiliary or blind area monitoring system, the anti-lock system and the electronic stability program system.
4. A vehicle control method according to claim 1, wherein the user information includes: one or more of facial recognition information, fingerprint information, voice print information.
5. A vehicle control method as set forth in any one of claims 1-4, characterized in that the operation inertia data includes: the optimal steering wheel steering torque; the obtaining of the optimal steering wheel steering torque includes:
collecting steering wheel steering torque, and a steering wheel corner, lane curvature and vehicle running speed corresponding to the steering wheel steering torque;
inputting the vehicle running speed, the steering wheel turning angle, the lane curvature, the steering wheel steering torque and the vehicle posture into a machine learning model of the steering wheel steering torque to obtain the optimal steering wheel steering torque;
associating and storing the optimal steering wheel steering torque with the user information.
6. A vehicle control method as defined in claim 5, wherein assisting a user in controlling the vehicle in accordance with the optimal steering wheel steering torque comprises: and controlling a steering power-assisted system according to the optimal steering wheel steering torque, and controlling a steering wheel of the vehicle through the steering power-assisted system.
7. A vehicle control method as set forth in any one of claims 1-4, characterized in that the operation inertia data includes: optimal pedal sensitivity; the obtaining of the optimal pedal sensitivity includes:
acquiring the force and depth of a pedal operated by a user, and vehicle acceleration and vehicle running speed corresponding to the force and depth of the pedal;
inputting the vehicle running speed, the vehicle acceleration, the pedal force and the depth into a machine learning model of pedal sensitivity to obtain the optimal pedal sensitivity;
associating and storing the optimal pedal sensitivity with the user information.
8. A vehicle control method as claimed in claim 7, wherein assisting a user in controlling a vehicle in accordance with the optimal pedal sensitivity comprises: and controlling a pedal hydraulic power-assisted system according to the optimal pedal sensitivity, and controlling a pedal of the vehicle through the pedal hydraulic power-assisted system.
9. A vehicle control apparatus characterized by comprising:
a storage module to store driving assistance data, the driving assistance data comprising: obtaining operation inertial data by using a machine learning model and operation function mode selection data according to historical data selected by a user for a function mode;
the matching module is used for matching the auxiliary driving data corresponding to the acquired user information according to the acquired user information;
and the control module is used for assisting a user in controlling the vehicle according to the driving assisting data.
10. The vehicle control apparatus according to claim 9, wherein the user information includes: one or more of facial recognition information, fingerprint information, voice print information.
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