CN115402325B - Vehicle control methods, devices and vehicles - Google Patents

Vehicle control methods, devices and vehicles

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Publication number
CN115402325B
CN115402325B CN202210981846.2A CN202210981846A CN115402325B CN 115402325 B CN115402325 B CN 115402325B CN 202210981846 A CN202210981846 A CN 202210981846A CN 115402325 B CN115402325 B CN 115402325B
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China
Prior art keywords
data
driving
vehicle
control
identification information
Prior art date
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Active
Application number
CN202210981846.2A
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Chinese (zh)
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CN115402325A (en
Inventor
王洪峰
陈博
尚秉旭
张勇
张中举
陈志新
刘洋
何柳
许朝文
金百鑫
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FAW Group Corp
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FAW Group Corp
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Priority to CN202210981846.2A priority Critical patent/CN115402325B/en
Publication of CN115402325A publication Critical patent/CN115402325A/en
Application granted granted Critical
Publication of CN115402325B publication Critical patent/CN115402325B/en
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Classifications

    • 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
    • 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
    • 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
    • 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
    • B60W2040/0809—Driver authorisation; Driver identity check
    • 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
    • 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
    • B60W2540/30—Driving style

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

本发明公开了一种车辆控制的方法、装置和车辆。其中，该方法包括：获取车辆在行驶过程中的驾驶工况数据；基于车辆中操作对象的标识信息和驾驶工况数据，确定与操作对象对应的控制数据，其中，标识信息用于标识操作对象；基于控制数据，控制车辆行驶。本发明解决了车辆驾驶过程中控制效果差的技术问题。

This invention discloses a method, apparatus, and vehicle for vehicle control. The method includes: acquiring driving condition data of the vehicle during operation; determining control data corresponding to the operated object based on identification information of an object within the vehicle and the driving condition data, wherein the identification information is used to identify the operated object; and controlling the vehicle's movement based on the control data. This invention solves the technical problem of poor control performance during vehicle driving.

Description

Vehicle control method and device and vehicle
Technical Field
The invention relates to the field of vehicles, in particular to a vehicle control method and device and a vehicle.
Background
At present, with the development of intelligent driving technology of automobiles, more and more driving auxiliary technologies are produced on a passenger car in mass, the integration level of a driving auxiliary system is higher and higher, and the driving auxiliary technology is a safety technology for assisting a driver in driving, so that driving safety and comfortableness are improved. With the popularization of the driving assistance technology, the continuity of the driving assistance technology is continuously increasing.
In the related art, aiming at the problem of vehicle driving control, due to the difference of age, sex and driving style of drivers and the difference of driving road conditions, weather and the like, a common adaptive cruise control system is difficult to meet the personalized requirements of the drivers on driving comfort, so that the technical problem of poor control effect in the driving process of the vehicle exists.
Aiming at the technical problem of poor control effect in the driving process of the vehicle in the prior art, no effective solution is proposed at present.
Disclosure of Invention
The embodiment of the invention provides a vehicle control method and device and a vehicle, and aims to at least solve the technical problem of low vehicle running control performance.
According to one aspect of the embodiment of the invention, a vehicle control method is provided, and the vehicle control method comprises the steps of obtaining driving condition data of a vehicle in a driving process, determining control data corresponding to an operation object based on identification information of the operation object in the vehicle and the driving condition data, wherein the identification information is used for identifying the operation object, and controlling the vehicle to drive based on the control data.
Optionally, the control data corresponding to the operation object is determined based on the identification information of the operation object in the vehicle and the driving condition data, and the control data corresponding to the operation object is determined based on the matching result.
Optionally, determining the control data corresponding to the operation object based on the matching result comprises determining style data of the operation object in response to the matching result for representing that the identification information is successfully matched with the history identification information, wherein the style data is used for representing the driving style of the operation object, and determining the control data corresponding to the operation object based on the style data and the driving condition data.
Optionally, determining the control data corresponding to the operation object based on the style data and the driving condition data includes processing the style data and the driving condition data based on a target model, and generating the control data, wherein the target model is a neural network model.
Optionally, driving sample data and driving condition data corresponding to the driving sample data are selected from a database, wherein the driving sample data are used for representing driving state data of a vehicle in a driving process, the driving sample data are classified based on the driving condition data corresponding to the driving sample data to obtain a plurality of driving sample data groups, the number of the driving sample data groups is the same as the number of the types of the driving condition data, and the sub-models are trained based on the driving sample data groups to obtain a target model.
Optionally, style data of the operation object is determined based on historical driving data of the operation object.
Optionally, determining the control data corresponding to the operation object based on the matching result includes determining that the control data is preset control data in response to the matching result for characterizing the abnormal matching of the identification information and the historical identification information.
Optionally, based on the control data, generating an operation record of the operation object, and updating historical driving data of the operation object in response to a target number of operation records being present in the vehicle.
According to another aspect of the embodiment of the invention, there is also provided a vehicle control device, which includes an acquisition unit configured to acquire driving condition data of a vehicle during running, a determination unit configured to determine control data corresponding to an operation object based on identification information of the operation object in the vehicle and the driving condition data, wherein the identification information is configured to identify the operation object, and a control unit configured to control running of the vehicle based on the control data.
According to another aspect of the embodiment of the invention, a vehicle is also provided. The vehicle is used for executing the vehicle control method according to the embodiment of the invention.
The method comprises the steps of obtaining driving condition data of a vehicle in a running process, determining control data corresponding to an operation object based on identification information of the operation object in the vehicle and the driving condition data, wherein the identification information is used for identifying the operation object, and controlling the vehicle to run based on the control data. That is, the embodiment of the invention determines the control data corresponding to the operation object by the driving condition data of the vehicle in the driving process and the identification information of the operation object in the vehicle, and then controls the vehicle to drive according to the driving style of the operation object in the vehicle based on the control data, thereby solving the technical problem of poor control effect in the driving process of the vehicle and realizing the technical effect of improving the control effect in the driving process of the vehicle.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flow chart of a method of vehicle control according to an embodiment of the present invention;
FIG. 2 is a schematic illustration of a vehicle control system according to an embodiment of the present invention;
FIG. 3 is a flow chart of a learning module learning according to an embodiment of the present invention;
FIG. 4 is a flow chart of an adaptive cruise control according to an embodiment of the present invention;
fig. 5 is a schematic view of an apparatus for vehicle control according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
According to an embodiment of the present invention, there is provided an embodiment of a method of vehicle control, it being noted that the steps shown in the flowchart of the figures may be performed in a computer system such as a set of computer executable instructions, and, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in an order other than that shown or described herein.
Fig. 1 is a flowchart of a method of vehicle control according to an embodiment of the present invention, as shown in fig. 1, the method may include the steps of:
step S102, driving condition data of the vehicle in the driving process are obtained.
In the technical scheme provided in the step S102, driving condition data may be obtained during the running process of the vehicle, where the driving condition data may include road conditions, road surface conditions, and weather conditions during the running process of the vehicle, for example, the road conditions may be smooth, slow running, and congestion, the road surface conditions may be a slope road, a narrow bridge, an urban road, a rural road, and the like, and the weather conditions may be a sunny day, a heavy fog, a rainy day, a snowy day, and a haze, and the like. It should be noted that, this is merely an example, and the driving condition data is not particularly limited.
For example, an image acquisition device may acquire a picture of the vehicle during driving, and determine driving condition data of the vehicle during driving based on the acquired picture.
Step S104, determining control data corresponding to the operation object based on identification information of the operation object in the vehicle and driving condition data, wherein the identification information is used for identifying the operation object.
In the technical solution provided in the above step S104 of the present invention, control data corresponding to an operation object is determined based on identification information and driving condition data of the operation object in a vehicle, where the operation object may be a driver of a main driver seat, the identification information of the operation object may be identity information of the driver, and the control data may be data for characterizing and controlling running of the vehicle, for example, data such as a vehicle speed, an acceleration, a braking distance, a displacement of a brake lever, and a steering angle of a steering wheel of the vehicle.
For example, the identification information of the operation object may include a face image, fingerprint information, voiceprint information, etc. of the operation object, and the identification information of the operation object may be collected by installing a camera, a fingerprint recognition device, a voiceprint recognition device, etc. on the vehicle, which is to be noted, but the identification information and the device for collecting the identification information are not limited in particular.
Step S106, controlling the vehicle to travel based on the control data.
In the technical solution of the above step S106 of the present invention, the vehicle may be controlled to run based on the driving style obtained corresponding control data.
For example, the speed, acceleration, braking distance, displacement of the brake lever, turning amplitude, etc. of the vehicle may be controlled based on the control data.
The method comprises the steps of S102 to S106, the step of acquiring driving condition data of the vehicle in the running process, the step of determining control data corresponding to the operation object based on the identification information of the operation object in the vehicle and the driving condition data, wherein the identification information is used for identifying the operation object, and the step of controlling the vehicle to run based on the control data. That is, the embodiment of the application determines the control data corresponding to the operation object by the driving condition data of the vehicle in the driving process and the identification information of the operation object in the vehicle, and then controls the vehicle to drive according to the driving style of the operation object in the vehicle based on the control data, thereby solving the technical problem of poor control effect in the driving process of the vehicle and realizing the technical effect of improving the control effect in the driving process of the vehicle.
The above-described method of this embodiment is further described below.
As an optional embodiment, step S104, based on the identification information and driving condition data of the operation object in the vehicle, determines control data corresponding to the operation object, and comprises the steps of matching the identification information with the recorded historical identification information in the vehicle to obtain a matching result, and determining the control data corresponding to the operation object based on the matching result.
In this embodiment, the identification information of the operation object in the vehicle is matched with the history identification information that has been entered in the vehicle, so as to obtain a matching result, whether the identification information of the operation object has been entered in the system at this time may be determined based on the matching result, and the control data corresponding to the operation object may be determined based on the matching result, wherein the history identification information may be used to characterize the identity information of the driver that has been entered in the system.
As an optional embodiment, step S104, determining control data corresponding to the operation object based on the matching result includes determining style data of the operation object in response to the matching result for representing that the identification information is successfully matched with the history identification information, and determining operation data corresponding to the operation object based on the style data and the driving condition data.
In this embodiment, the identification information is matched with the history identification information recorded in the vehicle to obtain a matching result, when the matching result is used for representing that the matching is successful, it may be determined that the identity information of the operation object is recorded in the system, the style data of the operation object may be determined based on the identification information of the operation object, and the control data corresponding to the operation object may be determined based on the style data of the operation object and the driving condition data, where the style data is used for representing the driving style of the operation object.
For example, the driving style may include a conservative driving style, a normal driving style and an aggressive driving style, wherein the style data of the driver may be determined by the information such as the speed, acceleration, braking distance, displacement of the brake lever and steering wheel angle of the vehicle during driving of the driver, which is to be noted, but is merely illustrative and not limited to specific information for determining the style data.
For example, the driving style of the driver can be determined to be the normal driving style by using a vehicle speed in the driving process of the driver within a range of 10-15 m/s, an acceleration of 5-10 m/s 2, a braking distance of 10-15m, a braking lever displacement of 2 cm-5 cm, and the like.
Alternatively, the style data of the operation object may be determined by acquiring the driving state data of the vehicle during the driving process of the operation object in real time, where the driving state data may include information such as a following distance, an acceleration, a vehicle speed, an opening degree of a brake pedal, and an opening degree change rate of the vehicle, and it should be noted that the driving state data of the vehicle is only illustrated herein and is not specifically limited.
Optionally, the system may store the driving state data of the vehicle within a certain target time, so as to perform long-term analysis and judgment on the driving state data of the vehicle during the driving of the vehicle by a certain operation object, and prevent driving style analysis errors caused by excessively large difference between the driving state data of the vehicle and usual times of the operation object in a special situation.
For example, the system detects that the operation object is driving the vehicle, obtains the driving state data of the vehicle for a period of time, determines that the driving style of the current operation object is a normal driving style based on the driving state data, if the driving state of the vehicle is temporarily changed (such as the vehicle speed is faster and the acceleration is greater) during driving due to an emergency on a certain day of the operation object, if the system does not analyze the driving state of the long-term vehicle, the driving style of the current operation object may be erroneously determined to be a aggressive driving style.
Optionally, the system may determine driving condition data of the detected vehicle during the driving process, if it is determined that current driving condition data of the vehicle is first driving condition data (for example, road condition is smooth, road surface condition is urban road, and weather condition is sunny), further learn may be performed on operation data of the operation object under the first driving condition data, so as to determine style data of the operation object under the first driving condition data, and if it is determined that current driving condition data of the vehicle is second driving condition data (for example, road condition is congestion, road surface condition is narrow bridge or ramp, and weather condition is heavy rain or snow), then the second driving condition data may be classified, and operation data of the operation object under each specific driving condition data after classification may be further learned, so as to determine style data of the operation object under each specific driving condition data under the second driving condition data.
For example, when the vehicle is traveling on an urban road on a sunny day, the system may determine the detected driving condition data of the current vehicle, and if the driving condition data of the current vehicle is the first driving condition data, the driving style of the operation object may be further learned, so as to determine the style data of the operation object.
For another example, when the vehicle encounters a narrow bridge, heavy fog weather or heavy rain weather during running, the system may determine the detected driving condition data of the current vehicle, where the current driving condition data is the second driving condition data, the system may classify the driving condition data, may specifically divide the driving condition data into each special road condition or each special weather, and the like, and may learn only the driving style of the operation object corresponding to the specific driving condition data and store the driving style separately, so as to determine the driving style of the operation object under the fixed second driving condition data.
As an optional embodiment, step S104, determining control data corresponding to the operation object based on the style data and the driving condition data includes processing the style data and the driving condition data based on the target model to generate the control data.
In this embodiment, the style data and the driving condition data of the operation object may be processed based on a target model, which may be a neural network model, to generate the control data.
Alternatively, the embodiment may be trained to obtain the target model by a reinforcement learning method.
Optionally, the neural network model is built based on Actor-comment method (Actor-Critic method), the obtained driving state data of the vehicle and the data under the driving working condition data in the long-term driving process of the operation object are transmitted to the target model in the system for analysis and processing, the driving style of the operation object is obtained through summarization, and control data corresponding to the driving style can be generated, so that the driving style of the operation object is changed in a short time, the style data is eliminated, and the accuracy of the style data can be increased.
As an optional embodiment, step S104 is to select driving sample data and driving condition data corresponding to the driving sample data from the database, classify the driving sample data based on the driving condition data corresponding to the driving sample data to obtain a plurality of driving sample data sets, and train the sub-models based on the driving sample data sets to obtain the target model.
In this embodiment, driving sample data of an operation object stored in the system and driving condition data corresponding to the driving sample data may be collected, and the driving sample data may be classified based on the driving condition data to obtain a plurality of driving sample data sets, and a sub-model may be trained based on the driving sample data sets to obtain a target model, where the driving sample data is used to represent driving state data of the vehicle in the driving process, and the number of the driving sample data sets is the same as the number of types of the driving condition data.
Optionally, under certain driving condition data, driving condition data in the driving process of the vehicle at the moment can be acquired, the driving condition data and the driving condition data corresponding to the driving condition data can be classified, a plurality of data sets of the driving condition data of the vehicle under different driving condition data can be acquired, the classified data are subjected to data analysis by adopting a statistical method, training and learning are performed based on a sub-model corresponding to the data sets, a target model can be acquired, and further, control data corresponding to the target model can be acquired.
As an alternative embodiment, step S104 determines style data of the operation object based on the historical driving data of the operation object.
In this embodiment, when the vehicle is driven by the operation object, the adaptive cruise control system needs to be turned on, and the system may determine whether the system has been entered by identifying identification information of the operation object, if the system has been entered, the historical driving data of the operation object may be determined based on the identity information and the historical driving data of the operation object that have been entered into the system, and the style data of the operation object may be determined by the historical driving data of the operation object, and further, the control data corresponding to the style data may be determined, where the historical driving data may be driving data of the operation object in a past certain period of time.
Optionally, the historical driving data may include information such as a vehicle speed, an acceleration, a braking distance, a displacement of a brake lever, a steering wheel angle and the like of the operation object in a historical driving process, the recorded historical driving data of the operation object may be stored in the vehicle, and the historical driving data is analyzed to determine style data of the operation object, for example, when the vehicle has more than five times of driving, the vehicle speed is more than 15m/s, and the acceleration is in a range of 10-15 m/s 2, the driving style of the operation object may be determined to be a aggressive driving style.
Alternatively, style data corresponding to when the driving data satisfies what conditions may be preset, for example, driving requirements corresponding to different style data may be expressed in a form such as a table, so that it is possible to determine style data of the operation object based on historical driving data of the operation object.
For example, the historical driving data of a driver may include a vehicle speed of 12m/s, an acceleration of 7m/s 2, a braking distance of 13m, and a braking lever displacement of 3cm, and after analysis, it may be determined that the historical driving data of the driver is within a driving state data range corresponding to a normal driving style, and then it may be determined that the style data of the driver is the normal driving style.
It should be noted that, the above-mentioned correspondence relationship between the driver history driving data and the style data and the expression of the correspondence relationship are only examples, and are not particularly limited herein.
Alternatively, identification information of the operation object may be entered in the system, driving data of the operation object may be acquired, style data of the operation object may be determined based on the acquired driving data, and when the identification information of the operation object is acquired again, style data of the operation object may be determined based on historical driving data of the operation object.
As an optional embodiment, step S104, determining control data corresponding to the operation object based on the matching result includes determining that the control data is preset control data in response to the matching result for characterizing the abnormal matching of the identification information and the history identification information.
In this embodiment, the identification information is matched with the historical identification information recorded in the vehicle to obtain a matching result, when the matching result is used for representing a matching abnormality, it may be determined that the identification information of the operation object is not recorded in the system, at this time, the style data of the operation object may be determined as preset style data, and at this time, the control data may be determined as preset control data corresponding to the preset style data.
For example, when the operation object drives the vehicle, the system can identify the identification information of the operation object and match with the history identification information, if the matching result is used for representing that the matching is abnormal, the identification information of the operation object is judged to be not input into the system, at the moment, the system determines the driving style of the operation object as a normal driving style, can determine the control data at the moment as normal control parameters corresponding to the normal driving style, can further determine the information of the vehicle speed, acceleration and the like under the normal control data, and controls the vehicle to run based on the control data.
As an alternative embodiment, step S104 generates operation records of the operation object based on the control data, and updates the historical driving data of the operation object in response to the existence of the target number of operation records in the vehicle.
In this embodiment, control data of the operation object is acquired, the control data is stored, the stored control data may be used as one operation record of the operation object, and the operation record of the operation object may be marked, for example, the operation record may be added one by adding a number, each time the operation object acquires the control data once, until after a target number of operation records exist, the operation data from one to the target number is updated into the database, so as to implement updating of historical driving data of the operation object, where the target number may be a number set by the system or a number selected by the operation object according to self-requirements.
For example, when the operation object acquires a set of control data, the control data is stored to obtain a set of operation records, and when a target number of operation records exist, the stored control data may be used as historical driving data of the operation object, so as to complete the purpose of updating the historical driving data of the operation object, and when the operation records reach the target number, the number of operation records may be registered from the beginning.
Optionally, when new driving state data of the vehicle is obtained, driving condition data corresponding to the new driving state data can be determined, the driving state data and the driving condition data corresponding to the driving state data can be classified, data analysis is performed on the classified data by adopting a statistical method, data analysis results under different driving condition data can be obtained, if the total amount of the data analysis results reaches the target amount, new driving data can be learned through a neural network model, historical driving data of an operation object can be updated, if the total amount of the data analysis results does not reach the target amount, the new driving state data can be stored without learning, the new driving data can be acquired again next time, so that the driving style of the operation object can be dynamically learned by the system, and the driving state data updated by the system is ensured not to be influenced by abnormal operation of the operation object in a very short time.
For example, the driving state data and the driving condition data corresponding to the driving state data are classified, and data analysis is performed on the classified data by adopting a statistical method, wherein the statistical method can comprise data fitting, regression analysis and the like.
The method and the device for controlling the vehicle to run comprise the steps of obtaining driving condition data of the vehicle in the running process, determining control data corresponding to an operation object based on identification information of the operation object in the vehicle and the driving condition data, wherein the identification information is used for identifying the operation object, and controlling the vehicle to run based on the control data. That is, the embodiment of the invention determines the control data corresponding to the operation object by the driving condition data of the vehicle in the driving process and the identification information of the operation object in the vehicle, and then controls the vehicle to drive according to the driving style of the operation object in the vehicle based on the control data, thereby solving the technical problem of poor control effect in the driving process of the vehicle and realizing the technical effect of improving the control effect in the driving process of the vehicle.
Example 2
The technical solution of the embodiment of the present invention will be illustrated in the following with reference to a preferred embodiment.
In recent years, an adaptive cruise control system (Adaptive Cruise Control, abbreviated as ACC) has been widely used as an intelligent auxiliary driving technique, and thus, driving comfort of the adaptive cruise driving system is particularly important, so that it is of great significance to researchers for future driving vehicles and learning driving styles of drivers.
In a related technology, a vehicle control method and a system based on identity recognition are provided, the identity information is compared with preset approval information only according to the acquired identity information of a driver, a driving style matched with the identity information is acquired, an engine is automatically started, a style attribute table and a correction coefficient corresponding to the acquired driving style are inquired, the current accelerator pedal opening and engine rotating speed of the vehicle are acquired, the engine torque is acquired according to the pedal opening and the vehicle speed in the style attribute table, the required torque of the engine is calculated according to the engine torque and the correction coefficient, the torque output of the engine is controlled according to the required torque, and the driving condition data of the vehicle in the driving process is not acquired yet, so that the problem of poor driving comfort of a vehicle self-adaptive cruise system is caused.
In another related technology, a self-learning following system and method for learning driver behavior and surrounding environment are provided, which only collect road type information, distance and lane line information of surrounding target vehicles according to an external environment sensing module, and output the information into a domain control, when a driver runs in different road environments, the domain controller determines the road environment and traffic flow conditions corresponding to the driver according to the information collected by the external environment sensing module of the driver, then collects the time distance and distance between the driver and the target vehicles in real time, and determines the following distance and/or following distance of the driver,
Finally, the domain controller is used for matching the following time distance or the following distance of the driver according to the road working condition where the current vehicle is and the type of the target vehicle, and the problem that the driving comfort of the vehicle self-adaptive cruise system is poor due to the fact that the self-adaptive cruise control system with the driving style of the driver is not involved yet.
In order to solve the problems, the embodiment of the invention provides an adaptive cruise control system with learning capability, which can comprise an identification module, a learning module and an adaptive cruise control module. The identity recognition module has the functions of driver information input and identity recognition and can be used for setting learning objects and on-line switching control parameters; the learning module can acquire driving condition data of the vehicle in the driving process, the learning identity recognition module can input driving habits of a driver so as to change control data of the adaptive cruise control system, and the adaptive cruise control system module can learn system parameters according to the learning module to perform adaptive cruise control.
The above-described methods of embodiments of the present invention are further described below.
Fig. 2 is a schematic diagram of a vehicle control system according to an embodiment of the invention, as shown in fig. 2, a vehicle control system 200 may include an identification module 201, a learning module 202, and an adaptive cruise control module 203.
In this embodiment, the identity module 201 may be used to enter information of the driver and identify the current driver according to the entered information of the driver.
Optionally, the identity recognition module 201 may include a recognition device, a display, a storage device, a transmission device, and the like, where the recognition device may be a camera and a fingerprint recognition device, and after the vehicle installs the vehicle control system, the driver needs to enter the identity for the first time, and can enter information such as a face and a fingerprint of the driver through the camera and the fingerprint recognition device respectively, and the identity information at this time is recorded to the storage device, so that the identity information of the driver can be conveniently recognized.
Optionally, the identity recognition module 201 may set an administrator user function, to prevent the driver information from being modified and entered at will, and when the administrator user is not set, the system may prompt "whether to use the driver device for the administrator" by sending a prompt message to the display each time after the driver identity information is entered, the driver may set by clicking the "yes" button on the display, the storage device may record the identity information of the administrator user, and once the administrator user is set, the prompt message will not be further presented.
Optionally, the administrator user can reset the administrator user, the method can comprise the steps that the administrator user can select a reset option on a display, after the identity recognition module obtains the instruction, whether the driver is the administrator user can be recognized through the camera and the fingerprint recognition device, if so, identity information of the current administrator user in the storage device can be emptied, and if not, the identity recognition module can forcedly finish the reset operation.
Optionally, after the administrator user completes the setting, the subsequent identity information of the driver may be set through the administrator user authorization, where the administrator user authorization may be face recognition or fingerprint recognition.
Optionally, the identity recognition module 201 can recognize the current driver according to the entered driver information, and the recognition process can include the steps that the identity recognition module collects the identity information of the driver of the current main driver position through the recognition device and can match with the historical identity information of the driver already entered into the storage device, if the matching result is used for representing that the matching is successful, the identity recognition of the driver can be completed, and if the matching result is used for representing that the matching is failed, the identity information of the driver can be indicated to be not entered by the storage device.
Alternatively, the identity recognition module 201 may transmit the obtained identity recognition result of the driver to the learning module 202 through a transmission device, where a signal of the transmission device for transmitting the identity recognition result may be a local area internet, a digital signal, an ethernet, etc., and the signal of the identity recognition result is only for illustration and not limited specifically.
In this embodiment, the learning module 202 may be configured to obtain driving condition data of the vehicle during the driving process, and determine control data corresponding to the current driver according to the identification result of the identity identification module 201 and the driving condition data.
Optionally, fig. 3 is a flowchart of learning by a learning module according to an embodiment of the present invention, and as shown in fig. 3, the learning module learning method may include the following steps:
In step S301, the identity recognition module recognizes the driver of the current main driver seat.
In the technical solution provided in step S301, the detection of the identity information of the driver in the current main driver seat by the identification device in the identity recognition module 201 may include obtaining the identity information such as the face and the fingerprint of the driver.
Step S302, whether the system is entered.
In the technical solution provided in the above step S302, the identity recognition module 201 determines whether the identity information of the driver is already recorded in the system by judging whether the identity information of the driver is identical to the information in the storage device, if yes, the step S303 is performed, and if not, the step S305 is performed.
In step S303, the learning module loads the control data corresponding to the current driver.
In the above step S303 of the present invention, if it is determined that the driver has entered the system, the learning device in the learning module 202 may load the control data and the learning record corresponding to the current driver.
Alternatively, the learning module 202 may include a detector, a learning device, a storage device, a receiving device, a transmitting device, and the like, where the detector may acquire driving state data of the vehicle in real time during driving, and transmit the driving state data to the storage device, for example, the detector may acquire information such as a following distance, an opening degree of an accelerator and a brake pedal, and an opening degree change rate of the driver during driving, and then transmit the driving state data of the vehicle to the storage module for recording.
Alternatively, the storage device may store the driving state data of the vehicle within a certain target time, and may prevent the storage amount of the data over time from being too large, where the target time may be a time selected by the driver on the display or a time set by the system by itself, which is only illustrative and not limiting.
For example, the storage device may store data only within a certain time range in a first-in first-out manner of the stack, for example, the system is set to a time of one month by itself based on the current time.
Optionally, the detector in the learning module 202 may also detect driving condition data of the vehicle during the driving process, for example, the road condition and the road surface condition during the driving process, and the detector may transmit the acquired data of the vehicle under the current driving condition data and the driving state data of the vehicle to the learning device.
Optionally, the learning device may classify the driving state data of the vehicle recorded by the storage device, perform data analysis on the classified driving state data, determine style data of the driver, and perform training learning by using the style data after data analysis as initial input data of the learning neural network model, so as to generate control data corresponding to different style data.
For example, the driving state data of the vehicle may be classified into three types, a conservative type, a normal type and an aggressive type, the driving state data of the vehicle recorded by the storage device may be classified by determining which of the three types corresponds to the driving state data of the vehicle, the driving state data of the classified vehicle may be subjected to data analysis by using statistical methods such as data fitting and regression analysis, the style data of the driver may be determined, the style data of the driver may be confirmed as optimal data, and the data may be used as initial input data of a reinforcement learning neural network model constructed by an Actor-comment method (Actor-Critic method), training learning may be performed, and control data corresponding to different style data may be generated.
Optionally, when the learning device receives driving condition data of the vehicle in the driving process, the learning device can judge the detected driving condition data of the vehicle in the driving process, if the current driving condition data of the vehicle is judged to be the first driving condition data, the long-term driving style of the driver can be further learned by the learning device under the condition that the driving condition data is the first driving condition data so as to determine the style data of the driver, and if the current driving condition data of the vehicle is judged to be the second driving condition data, the abnormal condition is required to be classified, and the driving style of the driver under each classified abnormal driving condition data is learned for a long time.
For example, when the vehicle encounters heavy rain during driving on a slope or during driving, the detector may transmit the detected driving condition data information of the current vehicle to the learning device, the learning device may determine that the current driving condition data is the second driving condition data, the system may determine that the detected driving condition data of the current vehicle is the second driving condition data, the learning device may classify abnormal driving condition data, may be specifically classified into each special road condition or each special weather, and the like, and may learn only the driving style of the driver corresponding to the abnormal driving condition data after classification and store the driving style alone, thereby determining the driving style of the driver under the fixed abnormal condition.
Step S304, learning the driving habit of the current driver and storing new control data.
In the above step S304 of the present invention, learning the new control parameters may include the steps of:
Alternatively, each time the new driving state data of the vehicle is transmitted to the learning device, driving condition data corresponding to the new driving state data may be determined first, the driving state data and the driving condition data corresponding to the driving state data may be classified, data analysis may be performed on the classified data by using statistical methods such as data fitting and regression analysis, if the total amount of the data analysis results reaches the target amount, new driving data may be learned based on a neural network model constructed by the Actor-Critic method, further, new control data may be stored, and if the total amount of the data analysis results does not reach the target data, the driving state data at that time may not be learned, and the driving state data may be stored in the storage module.
Alternatively, the learning device may wait to acquire the running state data of the new vehicle next time, and after the data analysis result is accumulated to reach the target number, the new learning process may be restarted, so that it may be ensured that the driving data learned by the system may not be changed due to abnormal data in a certain extremely short time, and the driving style of the driver may be dynamically learned.
Step S305, the learning is exited.
In the above step S305 of the present invention, if the identity recognition module 201 determines that the identity information of the driver in the current main driving position is not input into the system, the information may be transmitted to the learning module 202 through the transmission device, and the learning module may not learn the driving style of the driver after obtaining the information.
Step S306, preset control data are loaded by selecting.
In the above step S306 of the present invention, the identity information of the driver is not entered, and the learning module 202 may set the driving style of the vehicle at this time to the normal driving style, and may determine that the control data at this time is the normal control parameter.
Alternatively, the learning module 202 may transmit the determined control data information to the adaptive cruise control module 203 through a transmission device, where the signal for transmitting the control data information may be a local area internet, a digital signal, an ethernet, etc., which is only illustrated herein without specific limitation.
In this embodiment, the adaptive cruise control module 203 may be used to control vehicle travel based on control data from the learning module 202.
Alternatively, FIG. 4 is a flow chart of an adaptive cruise control according to an embodiment of the present invention, as shown in FIG. 4, which may include the steps of:
In step S401, the identity recognition module recognizes the driver of the current main driver seat.
In the technical solution provided in step S401 of the present invention, the detection of the identity information of the driver in the current main driver seat by the identification device in the identity recognition module 201 may include obtaining the identity information such as the face and the fingerprint of the driver.
Step S402, whether the driver has entered the system.
In the technical solution provided in the above step S402 of the present invention, the identity recognition module 201 may determine whether the identity information of the driver is already recorded in the system by determining whether the identity information of the driver matches with the information in the storage device, if yes, then step S403 is performed, otherwise, step S404 is performed.
In step S403, the adaptive cruise control module acquires control data loaded by the learning module.
In the solution provided in the above step S403 of the present invention, the adaptive cruise control module 203 may include a receiving device for receiving the control data from the learning module, a control device for controlling the vehicle to travel based on the acquired control data, and the like.
Alternatively, the receiving means in the adaptive cruise control module 203 may receive control data from the learning module 202 having the driver driving style.
For example, the identity recognition module recognizes whether identity information of a driver in a current main driving position is input into the system, if the identity information is input, the detector of the learning module acquires driving condition data in the current driving process of the vehicle, if the driving condition data is judged to be the first driving condition data, the storage device searches for style data corresponding to the identity information as an aggressive driving style, further determines that control data corresponding to the driver is an aggressive control parameter, and the transmission device of the learning module transmits the aggressive control parameter to the receiving device of the adaptive cruise control module.
In step S404, the adaptive cruise control system module reads control data preset in the learning module.
In the technical solution provided in the above step S404 of the present invention, the identity recognition module 201 recognizes whether the identity information of the driver of the current main driver is input into the system, and if the identity information is not input into the system, the transmission device of the learning module 202 transmits the preset normal control parameters to the receiving device in the adaptive cruise control module 203.
Optionally, when the detector in the learning module 201 detects driving condition data of the vehicle during running, if it is determined that the current driving condition data is the second type of driving condition data, historical driving data of the storage module when the driver encounters the current driving condition data may be obtained, and style data of the driver at the moment may be determined, and control data information corresponding to the historical driving data may be transmitted to the adaptive cruise control system 203.
For example, when the vehicle encounters a heavy rain during driving on a slope or during driving, the detector of the learning module may transmit the detected driving condition data information of the current vehicle to the learning device, the learning device determines that the current driving condition data is the second driving condition data, and obtains the control data corresponding to the conservative driving style when the driver encounters the current driving condition data in the storage module, where the control data is the conservative driving data, and the control data is transmitted to the receiving device in the adaptive cruise control module.
Step S405, adaptive cruise control.
In the solution provided in the above step S405 of the present invention, the control system in the adaptive cruise control module 203 may control the vehicle to run based on the obtained control data having the driving style of the driver.
For example, when the identity recognition module recognizes whether the identity information of the driver in the current main driving position is input into the system, if the identity information is input, the detector of the learning module acquires driving condition data in the current driving process of the vehicle, if the driving condition data is first-class driving condition data, the storage device searches that style data corresponding to the identity information is conservative, further determines that control data corresponding to the style data is conservative control parameters, the transmission device of the learning module transmits the control data with the aggressive driving style to the receiving device of the adaptive cruise control module, and the control device in the adaptive cruise control system controls the vehicle to drive according to the conservative control data.
The method and the device for controlling the vehicle to run comprise the steps of obtaining driving condition data of the vehicle in the running process, determining control data corresponding to an operation object based on identification information of the operation object in the vehicle and the driving condition data, wherein the identification information is used for identifying the operation object, and controlling the vehicle to run based on the control data. That is, the embodiment of the invention determines the control data corresponding to the operation object by the driving condition data of the vehicle in the driving process and the identification information of the operation object in the vehicle, and then controls the vehicle to drive according to the driving style of the operation object in the vehicle based on the control data, thereby solving the technical problem of poor control effect in the driving process of the vehicle and realizing the technical effect of improving the control effect in the driving process of the vehicle.
Example 3
According to the embodiment of the invention, a vehicle control device is also provided. The apparatus for vehicle control may be used to execute the method for vehicle control in embodiment 1.
Fig. 5 is a schematic diagram of an apparatus for vehicle control according to an embodiment of the present invention, and as shown in fig. 5, the apparatus 500 for vehicle control may include an acquisition unit 502, a determination unit 504, and a control unit 506.
And the acquiring unit 502 is used for acquiring driving condition data of the vehicle in the driving process.
A determining unit 504 for determining control data corresponding to the operation object based on identification information of the operation object of the vehicle and driving condition data, wherein the identification information is used for identifying the operation object.
And a control unit 506 for controlling the vehicle to travel based on the control data.
Optionally, the determining unit 504 includes a matching module, configured to match the identification information with the historical identification information recorded in the vehicle at the best, so as to obtain a matching result.
Alternatively, the determining unit 504 includes a first determining module for determining control data corresponding to the operation object.
Optionally, the determining unit 504 comprises a second determining module for determining style data of the operation object in response to the matching result for characterizing that the matching of the identification information with the history identification information is successful,
Optionally, the determining unit 504 includes a third determining module configured to determine control data corresponding to the operation object based on the style data and the driving condition data.
Optionally, the third determining module comprises a generating sub-module for processing the style data and the driving condition data based on a target model to generate control data, wherein the target model is a neural network model.
Optionally, the third determining module further comprises a selecting sub-module, which is used for selecting driving sample data from the database and driving condition data corresponding to the driving sample data, wherein the driving sample data is used for representing the driving state of the vehicle in the driving process.
Optionally, the third determining module further comprises a classifying sub-module, configured to classify the driving sample data based on driving condition data corresponding to the driving sample data, to obtain a plurality of driving sample data sets, where the number of the driving sample data sets is the same as the number of the types of the driving condition data.
Optionally, the third determining module further comprises a training sub-module for training the sub-model based on the driving sample data set to obtain the target model.
Optionally, the third determining module further includes a determining sub-module for determining style data of the operation object based on historical driving data of the operation object.
Optionally, the determining unit 504 further includes a fourth determining module, configured to determine, in response to the matching result, that the identification information is abnormal with the historical identification information, that the control data is preset control data.
Optionally, the device further comprises a generating unit for generating an operation record of the operation object based on the control data.
Optionally, the apparatus further comprises an updating unit for updating the historical driving data of the operation object in response to the presence of the target number of operation records in the vehicle.
In the embodiment of the invention, the driving condition data of the vehicle in the driving process is acquired by the acquisition unit, the determination unit determines the control data corresponding to the operation object based on the identification information and the driving condition of the operation object of the vehicle, wherein the identification information is used for identifying the operation object, and the control unit controls the vehicle to run based on the control data, so that the technical problem of poor control effect in the driving process of the vehicle is solved, and the technical effect of improving the control effect in the driving process of the vehicle is realized.
Example 4
According to an embodiment of the present invention, there is also provided a vehicle for executing the method of vehicle control of the embodiment of the present invention.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of units may be a logic function division, and there may be another division manner in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. The storage medium includes various media capable of storing program codes, such as a usb disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory RAM), a removable hard disk, a magnetic disk, or an optical disk.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims (7)

1.一种车辆控制方法，其特征在于，包括：1. A vehicle control method, characterized in that it comprises: 获取车辆在行驶过程中的驾驶工况数据；Acquire driving condition data of the vehicle during operation; 基于所述车辆中操作对象的标识信息和所述驾驶工况数据，确定与所述操作对象对应的控制数据，其中，所述标识信息用于标识所述操作对象；Based on the identification information of the object being operated in the vehicle and the driving condition data, control data corresponding to the object being operated is determined, wherein the identification information is used to identify the object being operated; 基于所述控制数据，控制所述车辆行驶；Based on the control data, the vehicle is controlled to move. 其中，基于所述车辆中操作对象的标识信息和所述驾驶工况数据，确定与所述操作对象对应的控制数据，包括：将所述标识信息与所述车辆中已录入的历史标识信息进行匹配，得到匹配结果；响应于所述匹配结果用于表征所述标识信息与所述历史标识信息匹配成功，获取所述操作对象的驾驶数据；基于所述驾驶数据，确定所述操作对象的风格数据，其中，所述风格数据用于表征所述操作对象的驾驶风格；响应于再次获取到所述操作对象的标识信息，基于所述操作对象的历史驾驶数据，确定所述风格数据，其中，所述历史驾驶数据为所述操作对象在当前时刻的过去时间段的驾驶数据；基于所述风格数据和所述驾驶工况数据，确定与所述操作对象对应的控制数据；The method for determining control data corresponding to the operating object based on the identification information of the operating object in the vehicle and the driving condition data includes: matching the identification information with historical identification information already recorded in the vehicle to obtain a matching result; in response to the matching result indicating that the identification information and the historical identification information are successfully matched, acquiring the driving data of the operating object; determining the style data of the operating object based on the driving data, wherein the style data is used to characterize the driving style of the operating object; in response to acquiring the identification information of the operating object again, determining the style data based on the historical driving data of the operating object, wherein the historical driving data is the driving data of the operating object in the past time period at the current moment; and determining control data corresponding to the operating object based on the style data and the driving condition data. 基于所述控制数据，控制所述车辆行驶，包括：基于所述控制数据，控制所述车辆按照所述操作对象的所述驾驶风格行驶。Controlling the vehicle's movement based on the control data includes: controlling the vehicle to drive according to the driving style of the user based on the control data. 2.根据权利要求1所述的方法，其特征在于，基于所述风格数据和所述驾驶工况数据，确定与所述操作对象对应的控制数据，包括：2. The method according to claim 1, characterized in that, determining the control data corresponding to the operation object based on the style data and the driving condition data includes: 基于目标模型对所述风格数据和所述驾驶工况数据进行处理，生成所述控制数据，其中，所述目标模型为神经网络模型。The style data and driving condition data are processed based on the target model to generate the control data, wherein the target model is a neural network model. 3.根据权利要求2所述的方法，其特征在于，所述方法还包括：3. The method according to claim 2, characterized in that the method further comprises: 从数据库中选取驾驶样本数据和所述驾驶样本数据对应的驾驶工况数据，其中，所述驾驶样本数据用于表征车辆在行驶过程中的行驶状态数据；Select driving sample data and corresponding driving condition data from the database, wherein the driving sample data is used to characterize the driving status data of the vehicle during driving. 基于所述驾驶样本数据对应的驾驶工况数据，对所述驾驶样本数据进行分类，得到多个驾驶样本数据组，其中，所述驾驶样本数据组的数量与所述驾驶工况数据的种类个数相同；Based on the driving condition data corresponding to the driving sample data, the driving sample data is classified to obtain multiple driving sample data groups, wherein the number of driving sample data groups is the same as the number of types of driving condition data. 基于所述驾驶样本数据组对子模型进行训练，得到所述目标模型。The target model is obtained by training the sub-model based on the driving sample data set. 4.根据权利要求1所述的方法，其特征在于，所述方法还包括：4. The method according to claim 1, characterized in that the method further comprises: 响应于所述匹配结果用于表征所述标识信息与所述历史标识信息匹配异常，确定所述控制数据为预设控制数据。In response to the matching result indicating an abnormal match between the identification information and the historical identification information, the control data is determined to be preset control data. 5.根据权利要求1所述的方法，其特征在于，所述方法还包括：5. The method according to claim 1, characterized in that the method further comprises: 基于所述控制数据，生成所述操作对象的操作记录；Based on the control data, an operation record for the operation object is generated; 响应于所述车辆中存在目标数量的所述操作记录，更新所述操作对象的历史驾驶数据。In response to the presence of a target number of operation records in the vehicle, the historical driving data of the operation object is updated. 6.一种车辆控制装置，其特征在于，包括：6. A vehicle control device, characterized in that it comprises: 获取单元，用于获取车辆在行驶过程中的驾驶工况数据；The acquisition unit is used to acquire driving condition data of the vehicle during driving. 确定单元，用于基于所述车辆中操作对象的标识信息和所述驾驶工况数据，确定与所述操作对象对应的控制数据，其中，所述标识信息用于标识所述操作对象；The determining unit is configured to determine control data corresponding to the operating object based on the identification information of the operating object in the vehicle and the driving condition data, wherein the identification information is used to identify the operating object; 控制单元，用于基于所述控制数据，控制所述车辆行驶；A control unit, used to control the vehicle's movement based on the control data; 其中，确定单元还用于通过以下步骤来基于所述车辆中操作对象的标识信息和所述驾驶工况数据，确定与所述操作对象对应的控制数据：将所述标识信息与所述车辆中已录入的历史标识信息进行匹配，得到匹配结果；响应于所述匹配结果用于表征所述标识信息与所述历史标识信息匹配成功，获取所述操作对象的驾驶数据；基于所述驾驶数据，确定所述操作对象的风格数据，其中，所述风格数据用于表征所述操作对象的驾驶风格；响应于再次获取到所述操作对象的标识信息，基于所述操作对象的历史驾驶数据，确定所述风格数据，其中，所述历史驾驶数据为所述操作对象在当前时刻的过去时间段的驾驶数据；基于所述风格数据和所述驾驶工况数据，确定与所述操作对象对应的控制数据；The determining unit is further configured to determine control data corresponding to the operating object based on the identification information of the operating object in the vehicle and the driving condition data through the following steps: matching the identification information with historical identification information already recorded in the vehicle to obtain a matching result; in response to the matching result indicating that the identification information and the historical identification information are successfully matched, acquiring the driving data of the operating object; determining the style data of the operating object based on the driving data, wherein the style data is used to characterize the driving style of the operating object; in response to acquiring the identification information of the operating object again, determining the style data based on the historical driving data of the operating object, wherein the historical driving data is the driving data of the operating object in the past time period at the current moment; and determining the control data corresponding to the operating object based on the style data and the driving condition data. 所述控制单元还用于通过以下步骤来基于所述控制数据，控制所述车辆行驶：基于所述控制数据，控制所述车辆按照所述操作对象的所述驾驶风格行驶。The control unit is also configured to control the vehicle to drive based on the control data by means of the following steps: controlling the vehicle to drive according to the driving style of the operator based on the control data. 7.一种车辆，其特征在于，用于执行权利要求1至5中任意一项所述的方法。7. A vehicle, characterized in that it is used to perform the method according to any one of claims 1 to 5.
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