CN116176600B - Control method of intelligent health cabin - Google Patents
Control method of intelligent health cabin Download PDFInfo
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- CN116176600B CN116176600B CN202310449574.6A CN202310449574A CN116176600B CN 116176600 B CN116176600 B CN 116176600B CN 202310449574 A CN202310449574 A CN 202310449574A CN 116176600 B CN116176600 B CN 116176600B
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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
- 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
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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
- 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
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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
- 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
- B60W2040/0872—Driver physiology
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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
- B60W2540/00—Input parameters relating to occupants
- B60W2540/221—Physiology, e.g. weight, heartbeat, health or special needs
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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
- B60W2540/00—Input parameters relating to occupants
- B60W2540/223—Posture, e.g. hand, foot, or seat position, turned or inclined
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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
- B60W2540/00—Input parameters relating to occupants
- B60W2540/30—Driving style
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The application discloses a control method of an intelligent health cabin, which comprises the following steps: constructing a state identification model of a driver in the cockpit, constructing a driving habit model of the driver in the cockpit, monitoring real-time sign data of the driver in the cockpit, and determining the real-time driving state of the driver in the cockpit based on the sign data of the driver in the cockpit by utilizing the state identification model; when the real-time driving state of the driver in the cabin is abnormal, monitoring real-time road condition data outside the cabin, and determining habitual control parameters based on the real-time road condition data outside the cabin by using a driving habit model. According to the method, the driving correction is carried out on the real-time control parameters of the driver based on habitual control parameters, the driving safety is guaranteed, meanwhile, the driving habit of the driver is restored to the greatest extent, correction optimization is carried out through a correction stability optimization function and a correction responsiveness optimization function in the correction process, and the stability and timeliness of the correction process are achieved.
Description
Technical Field
The application relates to the technical field of intelligent control, in particular to a control method of an intelligent health cabin.
Background
The working and residence time of developed cities are difficult to be relieved in a short period, the rate of self-driving long-distance travel rises year by year, and the like, so that the time and frequency of drivers on automobiles are increased, the method is a main cause for frequent social road traffic accidents at present, and the method is a great loss for common drivers and road network construction managers, and even seriously damages the life and property safety of citizens.
Through the development of more than one hundred years, the passenger car has gradually changed from original tool of riding instead of walk to mobile experience terminal, and intelligent network allies oneself with the development, has enriched the function and the experience of car, and the interactive behavior between people car is also more frequent, and the information of exchange is also more extensive in depth, can scientifically effectual improvement driver driving safety becomes the factor that excellent car product is indispensable.
The prior art provides technologies such as a driver state monitoring system (DMS), an active healthy seat of a driver and the like, although the occurrence of safety problems of the driver in the driving process can be reduced to a certain extent, the driver state monitoring system reminds the driving behavior of the driver by monitoring the driving state of the driver, and the driver can self-regulate the driving behavior to ensure safe driving; the active monitoring seat is used for monitoring the sitting posture of the driver to identify the driving behavior or the physical condition of the driver, so that the driver is reminded of external stimulus through the seat, and the driver can conduct self-regulation driving behavior to ensure safe driving.
However, these technologies are all on one side, and belong to the field of ensuring the safe driving of the driver, but are difficult to match the driving habit of the driver, so that the driving experience of the driver is affected, and the driving habit of the driver is unauthorized to be changed, so that the driving operation of the driver may be disturbed, and danger is generated in the road driving process.
Disclosure of Invention
The application aims to provide a control method of an intelligent health cabin, which aims to solve the technical problems that the prior art singly ensures the safe driving of a driver, but is difficult to match the driving habit of the driver, influences the driving experience of the driver, and the driving habit of the driver is unauthorized to change, so that the driving operation of the driver can be interfered, and danger is generated in the road driving process.
In order to solve the technical problems, the application specifically provides the following technical scheme:
the intelligent health cabin control method is characterized by comprising the following steps of:
constructing a state identification model of a driver in a cockpit control system to realize real-time detection of the driving state of the driver in the cockpit;
constructing a driving habit model of a driver in a cockpit control system to correlate and map control parameters representing driving habits of the driver in the cockpit with road condition data representing road conditions, wherein input items and output items of the driving habit model are the road condition data and the control parameters respectively, and the control parameters are systematic representation of the driving habits;
the cabin control system monitors real-time sign data of a driver in the cabin and determines a real-time driving state of the driver in the cabin based on the sign data of the driver in the cabin by using a state identification model;
when the real-time driving state of a driver in the cabin is an abnormal state, the cabin control system monitors real-time road condition data outside the cabin and determines habitual control parameters based on the input of the real-time road condition data outside the cabin by using a driving habit model, wherein the habitual control parameters are control parameters which are output by the driving habit model under the real-time road condition data and accord with the driving habit of the driver;
the real-time control parameters of the driver are corrected based on habitual control parameters, and the real-time control of the driving vehicle is interfered by the cabin control system so as to approach to the driving habit of the driver.
As a preferred embodiment of the present application, the constructing a state recognition model of a driver in a cabin includes:
taking the historical sign data of a driver in the cabin as an input item of a softmax model, and taking a driving state corresponding to the historical sign data as an output item of the softmax model;
model training is carried out by utilizing a softmax model based on an input item of the softmax model and an output item of the softmax model, and the state identification model is obtained;
model expression of the state recognition model:
Label=softmax(data)；
wherein Label is driving state, data is sign data, and softmax is softmax model.
As a preferred embodiment of the present application, the construction of the driving habit model of the driver in the cabin includes:
taking the historical road condition data as an input item of a CNN model, and taking a driver historical control parameter corresponding to the historical road condition data as an output item of the CNN model, wherein the historical control parameter is taken in a safe driving state;
performing model training by using a CNN model based on input items of the CNN model and output items of the CNN model to obtain the driving habit model;
the model expression of the driving habit model is as follows:
S=CNN(road_data)；
in the formula, S is a control parameter, road_data is road condition data, and CNN is a CNN model.
As a preferred embodiment of the present application, the sign data includes a driver posture image and driver physiological data.
As a preferred embodiment of the present application, the control parameters include steering angle, accelerator pedal depth, and brake pedal depth.
As a preferred aspect of the present application, the determining, using the state recognition model, the real-time driving state of the driver in the cabin based on the real-time sign data of the driver in the cabin includes:
and inputting the real-time physical sign data of the driver in the cabin into a state recognition model, and outputting the real-time driving state of the driver in the cabin by the state recognition model.
As a preferable mode of the present application, the road condition data includes road attribute data, road traffic vehicle data, and road pedestrian data.
As a preferable scheme of the application, the method for determining habitual control parameters based on real-time road condition data outside the cabin by using the driving habit model comprises the following steps:
and inputting the real-time road condition data into a driving habit model, and outputting habitual control parameters by the driving habit model.
As a preferable mode of the present application, the driving correction of the driver based on the habitual control parameter to the real-time control parameter of the driver includes:
setting correction time sequences and setting control parameters at each correction time sequence;
constructing a correction smoothness optimization function, wherein the function expression of the correction smoothness optimization function is as follows:
；
wherein Z1 is the correction smoothness, S i 、S i+1 Respectively the firstiFirst, secondiControl parameters at +1 correction timings, t i 、t i+1 Respectively the firstiFirst, secondi+1 correction timings, ||S i -S i+1 The I is S i And S is i+1 Is the minimize operator, min is the total number of corrected timings,is is a counting variable 1 For the driver to control parameters in real time S n Is a habitual control parameter;
constructing a correction responsivity optimization function, wherein the function expression of the correction responsivity optimization function is as follows:
；
wherein Z2 is correction responsivity, t i 、t i+1 Respectively the firstiFirst, secondi+1 correction timings, min is the minimize operator, n is the total number of correction timings,iis a count variable;
and solving a correction stability optimization function and a correction responsiveness optimization function to obtain the correction time sequence and the optimal value of the control parameter at each correction time sequence so as to realize the stability and timeliness of the correction process.
As a preferable mode of the present application, when the driving state of the driver in the cabin is a normal state, the driving control is performed based on the driver real-time control parameter.
Compared with the prior art, the application has the following beneficial effects:
the method comprises the steps of constructing a state identification model of a driver in a cockpit, constructing a driving habit model of the driver in the cockpit, monitoring real-time sign data of the driver in the cockpit, and determining the real-time driving state of the driver in the cockpit based on the sign data of the driver in the cockpit by utilizing the state identification model; when the real-time driving state of the driver in the cabin is an abnormal state, monitoring real-time road condition data outside the cabin, and determining habitual control parameters based on the real-time road condition data outside the cabin by using a driving habit model; and carrying out driving correction on the real-time control parameters of the driver based on habitual control parameters, so as to realize that the driving safety is ensured and the driving habit of the driver is restored to the greatest extent, and carrying out correction optimization by using a correction stability optimization function and a correction responsiveness optimization function in the correction process, thereby realizing the stability and timeliness of the correction process.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those of ordinary skill in the art that the drawings in the following description are exemplary only and that other implementations can be obtained from the extensions of the drawings provided without inventive effort.
Fig. 1 is a flowchart of a control method of an intelligent health cabin according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
As shown in fig. 1, the application provides a control method of an intelligent health cabin, comprising the following steps:
constructing a state identification model of a driver in a cockpit control system to realize real-time detection of the driving state of the driver in the cockpit;
constructing a state recognition model of a driver in a cockpit, comprising:
taking the historical sign data of a driver in the cabin as an input item of a softmax model, and taking a driving state corresponding to the historical sign data as an output item of the softmax model;
model training is carried out by utilizing the softmax model based on the input item of the softmax model and the output item of the softmax model, so as to obtain a state identification model;
model expression of the state recognition model:
Label=softmax(data)；
wherein Label is driving state, data is sign data, and softmax is softmax model.
The state identification model is built, so that the modeling identification of the state of the driver can be realized, the degree of automation of the identification is improved, and the accuracy of the state identification is improved.
Constructing a driving habit model of a driver in the cockpit to learn the driving habits of the driver in the cockpit, which are related to road conditions;
constructing a driving habit model of a driver in a cockpit, comprising:
taking the historical road condition data as an input item of a CNN model, taking a driver historical control parameter corresponding to the historical road condition data as an output item of the CNN model, and taking the historical control parameter under a safe driving state;
performing model training by utilizing the CNN model based on the input item of the CNN model and the output item of the CNN model to obtain a driving habit model;
the model expression of the driving habit model is:
S=CNN(road_data)；
in the formula, S is a control parameter, road_data is road condition data, and CNN is a CNN model.
The method comprises the steps of constructing a driving habit model of a driver in a cockpit control system to perform association mapping on control parameters representing driving habits of the driver in the cockpit and road condition data representing road conditions, wherein input items and output items of the driving habit model are the road condition data and the control parameters respectively, the control parameters are systematic representation of the driving habits, the driving habits of the driver in the cockpit are learned, so that the habits of the driver about steering angles, accelerator pedal depth, brake pedal depth and the like of the steering wheel are obtained according to different road conditions, and driving correction can be matched to the habits of the driver when the driving correction is performed subsequently.
The driving habit model is built, the modeling calculation of the habitual control parameters of the driver can be realized, the calculation automation degree is improved, and the accuracy of the control parameter calculation is improved.
The cabin control system monitors real-time sign data of a driver in the cabin and determines a real-time driving state of the driver in the cabin based on the sign data of the driver in the cabin by using a state identification model;
when the real-time driving state of the driver in the cabin is abnormal, the cabin control system monitors real-time road condition data outside the cabin and determines habitual control parameters based on the input of the real-time road condition data outside the cabin by utilizing the driving habit model, wherein the habitual control parameters are control parameters which are output by the driving habit model under the real-time road condition data and accord with the driving habits of the driver, the driving habits of the driver are actually habitually set according to the road conditions, for example, the driving habits of the driver under the road condition A are set as B, at the moment, the real-time control parameters of the driver are C, and the real-time control parameters are C, so that the real-time driving state of the driver in the cabin is adjusted from the abnormal state to the normal state and accord with the driving habits of the driver, and the real-time control parameters are required to be corrected according to the driving habits of the driver.
And inputting the real-time physical sign data of the driver in the cabin into a state recognition model, and outputting the real-time driving state of the driver in the cabin by the state recognition model.
Determining a real-time driving state of the driver in the cabin based on the real-time sign data of the driver in the cabin using the state identification model, comprising:
determining habitual control parameters based on real-time road condition data outside the cabin by using a driving habit model comprises:
and inputting the real-time road condition data into a driving habit model, and outputting habitual control parameters by the driving habit model.
The real-time control parameters of the driver are corrected based on habitual control parameters, and the real-time control of the driving vehicle is interfered by the cabin control system so as to approach to the driving habit of the driver.
The sign data includes driver pose images and driver physiological data.
The control parameters include steering angle, accelerator pedal depth, and brake pedal depth.
The road condition data includes road attribute data, road traffic vehicle data, and road pedestrian data.
Performing driver driving correction on the driver real-time control parameter based on the habitual control parameter, including:
setting correction time sequences and setting control parameters at each correction time sequence;
constructing a correction smoothness optimization function, wherein the function expression of the correction smoothness optimization function is as follows:
；
wherein Z1 is the correction smoothness, S i 、S i+1 Respectively the firstiFirst, secondiControl parameters at +1 correction timings, t i 、t i+1 Respectively the firstiFirst, secondi+1 correction timings, ||S i -S i+1 The I is S i And S is i+1 Is the minimize operator, min is the total number of corrected timings,is is a counting variable 1 For the driver to control parameters in real time S n Is a habitual control parameter;
constructing a correction responsivity optimization function, wherein the function expression of the correction responsivity optimization function is as follows:
；
wherein Z2 is correction responsivity, t i 、t i+1 Respectively the firstiFirst, secondi+1 correction timings, min is the minimize operator, n is the total number of correction timings,iis a count variable;
and solving a correction stability optimization function and a correction responsiveness optimization function to obtain the correction time sequence and the optimal value of the control parameter at each correction time sequence so as to realize the stability and timeliness of the correction process.
The higher the correction stability is, the longer the required correction time length is, and then the correction response is influenced, so that the lower the correction response is, otherwise, the higher the correction response is, the shorter the correction time length is, and then the correction stability is influenced, so that the lower the correction stability is, therefore, the correction stability and the correction response are two indexes which are reversely and mutually influenced, the optimization of the two indexes is performed by utilizing multi-objective optimization, the correction process with both the correction stability and the correction response is obtained, the correction process is stable, the phenomenon of a wrong feeling in the vehicle driving process can be avoided, the danger in the correction process can be avoided due to the overlong vehicle correction time in the correction process, and the combination of the stability and the timeliness is realized.
When the driving state of the driver in the cabin is a normal state, driving control is performed based on the driver real-time control parameter.
The method comprises the steps of constructing a state identification model of a driver in a cockpit, constructing a driving habit model of the driver in the cockpit, monitoring real-time sign data of the driver in the cockpit, and determining the real-time driving state of the driver in the cockpit based on the sign data of the driver in the cockpit by utilizing the state identification model; when the real-time driving state of the driver in the cabin is an abnormal state, monitoring real-time road condition data outside the cabin, and determining habitual control parameters based on the real-time road condition data outside the cabin by using a driving habit model; and carrying out driving correction on the real-time control parameters of the driver based on habitual control parameters, so as to realize that the driving safety is ensured and the driving habit of the driver is restored to the greatest extent, and carrying out correction optimization by using a correction stability optimization function and a correction responsiveness optimization function in the correction process, thereby realizing the stability and timeliness of the correction process.
The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application, the scope of which is defined by the claims. Various modifications and equivalent arrangements of this application will occur to those skilled in the art, and are intended to be within the spirit and scope of the application.
Claims (9)
1. The intelligent health cabin control method is characterized by comprising the following steps of:
constructing a state identification model of a driver in a cockpit control system to realize real-time detection of the driving state of the driver in the cockpit;
constructing a driving habit model of a driver in a cockpit control system to correlate and map control parameters representing driving habits of the driver in the cockpit with road condition data representing road conditions, wherein input items and output items of the driving habit model are the road condition data and the control parameters respectively, and the control parameters are systematic representation of the driving habits;
the cabin control system monitors real-time sign data of a driver in the cabin and determines a real-time driving state of the driver in the cabin based on the sign data of the driver in the cabin by using a state identification model;
when the real-time driving state of a driver in the cabin is an abnormal state, the cabin control system monitors real-time road condition data outside the cabin and determines habitual control parameters based on the input of the real-time road condition data outside the cabin by using a driving habit model, wherein the habitual control parameters are control parameters which are output by the driving habit model under the real-time road condition data and accord with the driving habit of the driver; correcting the real-time control parameters of the driver based on habitual control parameters, and interfering with the real-time control of the driving vehicle through a cabin control system so as to approach to the driving habit of the driver;
performing driver driving correction on the driver real-time control parameter based on the habitual control parameter, including:
setting correction time sequences and setting control parameters at each correction time sequence;
constructing a correction smoothness optimization function, wherein the function expression of the correction smoothness optimization function is as follows:
wherein Z1 is the correction smoothness, S i 、S i+1 Respectively the firstiFirst, secondiControl parameters at +1 correction timings, t i 、t i+1 Respectively the firstiFirst, secondi+1 correction timings, ||S i -S i+1 The I is S i And S is i+1 Is the minimize operator, min is the total number of corrected timings,is is a counting variable 1 For the driver to control parameters in real time S n Is a habitual control parameter;
constructing a correction responsivity optimization function, wherein the function expression of the correction responsivity optimization function is as follows:
wherein Z2 is correction responsivity, t i 、t i+1 Respectively the firstiFirst, secondi+1 correction timings, min is the minimize operator, n is the total number of correction timings,iis a count variable;
and solving a correction stability optimization function and a correction responsiveness optimization function to obtain the correction time sequence and the optimal value of the control parameter at each correction time sequence so as to realize the stability and timeliness of the correction process.
2. The method for controlling an intelligent health cabin according to claim 1, wherein:
the construction of the state recognition model of the driver in the cabin comprises the following steps:
taking the historical sign data of a driver in the cabin as an input item of a softmax model, and taking a driving state corresponding to the historical sign data as an output item of the softmax model;
model training is carried out by utilizing a softmax model based on an input item of the softmax model and an output item of the softmax model, and the state identification model is obtained;
model expression of the state recognition model:
Label=softmax(data)；
wherein Label is driving state, data is sign data, and softmax is softmax model.
3. The method for controlling an intelligent health cabin according to claim 2, wherein: the construction of the driving habit model of the driver in the cockpit comprises the following steps:
taking the historical road condition data as an input item of a CNN model, and taking a driver historical control parameter corresponding to the historical road condition data as an output item of the CNN model, wherein the historical control parameter is taken in a safe driving state;
performing model training by using a CNN model based on input items of the CNN model and output items of the CNN model to obtain the driving habit model;
the model expression of the driving habit model is as follows:
S=CNN(road_data)；
in the formula, S is a control parameter, road_data is road condition data, and CNN is a CNN model.
4. A control method of an intelligent health cabin according to claim 3, characterized in that: the sign data includes driver pose images and driver physiological data.
5. The method for controlling an intelligent health cabin according to claim 4, wherein: the control parameters comprise steering angle of the steering wheel, accelerator pedal depth and brake pedal depth.
6. The method for controlling an intelligent health cabin according to claim 5, wherein: the real-time driving state of the driver in the cabin is determined based on the real-time sign data of the driver in the cabin by using the state recognition model, and the method comprises the following steps:
and inputting the real-time physical sign data of the driver in the cabin into a state recognition model, and outputting the real-time driving state of the driver in the cabin by the state recognition model.
7. The method for controlling an intelligent health cabin according to claim 6, wherein: the road condition data comprises road attribute data, road traffic vehicle data and road pedestrian data.
8. The method for controlling an intelligent and healthy cabin according to claim 7, wherein the determining habitual control parameters based on real-time road condition data outside the cabin using the driving habit model comprises:
and inputting the real-time road condition data into a driving habit model, and outputting habitual control parameters by the driving habit model.
9. The control method of an intelligent health cabin according to claim 8, wherein the driving control is performed based on the driver real-time control parameter when the driving state of the driver in the cabin is a normal state.
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