CN114228734A - Automatic reminding method and device based on deep learning - Google Patents

Automatic reminding method and device based on deep learning Download PDF

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Publication number
CN114228734A
CN114228734A CN202111633211.5A CN202111633211A CN114228734A CN 114228734 A CN114228734 A CN 114228734A CN 202111633211 A CN202111633211 A CN 202111633211A CN 114228734 A CN114228734 A CN 114228734A
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driving state
deep learning
vehicle
automatically
driver
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CN114228734B (en
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兰雨晴
乔孟阳
余丹
王丹星
邢智焕
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China Standard Intelligent Security Technology Co Ltd
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China Standard Intelligent Security Technology Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/08—Interaction between the driver and the control system
    • B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
    • B60H1/00—Heating, cooling or ventilating [HVAC] devices
    • B60H1/00642—Control systems or circuits; Control members or indication devices for heating, cooling or ventilating devices
    • B60H1/00735—Control systems or circuits characterised by their input, i.e. by the detection, measurement or calculation of particular conditions, e.g. signal treatment, dynamic models
    • B60H1/00742—Control systems or circuits characterised by their input, i.e. by the detection, measurement or calculation of particular conditions, e.g. signal treatment, dynamic models by detection of the vehicle occupants' presence; by detection of conditions relating to the body of occupants, e.g. using radiant heat detectors
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
    • B60H1/00—Heating, cooling or ventilating [HVAC] devices
    • B60H1/00642—Control systems or circuits; Control members or indication devices for heating, cooling or ventilating devices
    • B60H1/00814—Control systems or circuits characterised by their output, for controlling particular components of the heating, cooling or ventilating installation
    • B60H1/00878—Control systems or circuits characterised by their output, for controlling particular components of the heating, cooling or ventilating installation the components being temperature regulating devices
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
    • B60Q1/00—Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor
    • B60Q1/26—Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic
    • B60Q1/50—Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic for indicating other intentions or conditions, e.g. request for waiting or overtaking
    • B60Q1/52—Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic for indicating other intentions or conditions, e.g. request for waiting or overtaking for indicating emergencies
    • 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
    • B60W2040/0818—Inactivity or incapacity of driver
    • B60W2040/0827—Inactivity or incapacity of driver due to sleepiness
    • 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
    • B60W2040/0818—Inactivity or incapacity of driver
    • B60W2040/0836—Inactivity or incapacity of driver due to alcohol
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/08—Interaction between the driver and the control system
    • B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
    • B60W2050/143—Alarm means

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  • Mechanical Engineering (AREA)
  • Automation & Control Theory (AREA)
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  • Transportation (AREA)
  • Thermal Sciences (AREA)
  • Human Computer Interaction (AREA)
  • Mathematical Physics (AREA)
  • Traffic Control Systems (AREA)

Abstract

The application provides an automatic reminding method and device based on deep learning, and relates to the technical field of data processing. The automatic reminding method based on the deep learning comprises the steps of obtaining a driving image of a driver in the driving process; identifying the driving image through a deep learning algorithm to identify the driving state of a driver; and when the recognized driving state is the designated driving state, automatically making a sound for reminding. It can be seen that the abnormal driving state can be timely reminded, the safety of the vehicle in the driving process is improved, and traffic accidents are reduced.

Description

Automatic reminding method and device based on deep learning
Technical Field
The application relates to the technical field of data processing, in particular to an automatic reminding method and device based on deep learning.
Background
With the development of economy and the gradual improvement of living standard of people, automobiles become an indispensable part of people going out, and meanwhile, the safety problem of vehicle running is more and more concerned. At present, with the increase of the automobile holding capacity, the number of traffic accidents increases year by year, but some accidents are not intentionally caused by subjective control, but result of abnormal driving state of drivers, such as fatigue driving, drunk driving and the like, seriously damage lives and properties of people, and therefore the problem needs to be solved urgently.
Disclosure of Invention
In view of the above problems, the present application is provided to provide an automatic reminding method and apparatus based on deep learning, which overcomes or at least partially solves the above problems, and can remind the driver of abnormal driving state in time, improve the safety of the vehicle during driving, and reduce the occurrence of traffic accidents.
The technical scheme is as follows:
in a first aspect, an automatic reminding method based on deep learning is provided, which includes the following steps:
acquiring a driving image of a driver in a driving process;
identifying the driving image through a deep learning algorithm to identify the driving state of a driver;
and when the recognized driving state is the designated driving state, automatically making a sound for reminding.
In one possible implementation, after automatically sounding for the reminder, the method further includes:
and controlling the volume of the automatically emitted sound according to the duration of the identified driving state as the specified driving state.
In one possible implementation, the volume of the automatically emitted sound is controlled according to the duration of the identified driving state as the designated driving state using the following formula:
Figure BDA0003441657640000021
wherein e (t) represents a volume value of the automatically emitted sound at the current time; t represents the current time; emaxA maximum volume value representing the automatically emitted sound; (f) (t) represents a driving state output value of the driver recognized by the deep learning algorithm, and if the driving state at the present time is dozing, f (t) is 1, whereas if the driving state at the present time is not dozing, f (t) is 0; t is t0Indicating a time at which the driving state of the driver recognized for the first time is dozing; t represents a preset fatigue time.
In one possible implementation, after automatically sounding for the reminder, the method further includes:
if the recognized driving state is that the duration of the specified driving state exceeds a preset threshold value, the double flashing lamps of the vehicle are automatically controlled to further remind other vehicles in the coming and going directions, and then the cold air of the vehicle is controlled to be turned on.
In one possible implementation, the double flash and cool air enable of the vehicle is controlled according to the length of time that the identified driving state is the designated driving state using the following equations:
Figure BDA0003441657640000022
wherein D (t) represents a double flashing and cold air enable control value of the vehicle at the present time;
if D (t) is 0, the control of double flashing and cold air of the vehicle is not needed to be opened at the current moment;
if d (t) is 1, it indicates that the double flashing and the cooling of the vehicle need to be controlled to be turned on at the present time.
In one possible implementation, the vehicle cold air temperature is controlled according to the current time and the cold air enabled and recognized driver's driving state using the following formula:
Figure BDA0003441657640000023
wherein q (t) represents a cool air temperature control value of the vehicle at the present time; qmaxRepresents a maximum value of temperature within a cool air controllable range of the vehicle; qminIndicating a minimum value of the temperature within the cool air controllable range of the vehicle.
In a second aspect, an automatic reminding device based on deep learning is provided, which includes:
the acquisition module is used for acquiring a driving image of a driver in the driving process;
the recognition module is used for recognizing the driving image through a deep learning algorithm and recognizing the driving state of a driver;
and the reminding module is used for automatically making a sound for reminding when the identified driving state is the designated driving state.
In one possible implementation manner, the reminding module is further configured to:
and after the automatic sound is emitted for reminding, controlling the volume of the automatically emitted sound according to the time length of the identified driving state as the specified driving state.
In one possible implementation manner, the reminding module is further configured to:
controlling the volume of the automatically emitted sound according to the duration of the identified driving state as the designated driving state by using the following formula:
Figure BDA0003441657640000031
wherein e (t) represents a volume value of the automatically emitted sound at the current time; t represents the current time; emaxA maximum volume value representing the automatically emitted sound; (f) (t) represents a driving state output value of the driver recognized by the deep learning algorithm, and if the driving state at the present time is dozing, f (t) is 1, whereas if the driving state at the present time is not dozing, f (t) is 0; t is t0Indicating a time at which the driving state of the driver recognized for the first time is dozing; t representsThe fatigue time is preset.
In one possible implementation manner, the reminding module is further configured to:
after the vehicle is automatically sounded for reminding, if the duration of the identified driving state which is the designated driving state exceeds a preset threshold value, the double flashing lamps of the vehicle are automatically controlled to remind other vehicles coming and going, and then the cold air of the vehicle is controlled to be turned on.
By means of the technical scheme, the automatic reminding method and device based on deep learning provided by the embodiment of the application firstly obtain the driving image of a driver in the driving process; identifying the driving image through a deep learning algorithm to identify the driving state of a driver; and when the recognized driving state is the designated driving state, automatically making a sound for reminding. It can be seen that the abnormal driving state can be timely reminded, the safety of the vehicle in the driving process is improved, and traffic accidents are reduced.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below.
Fig. 1 shows a flowchart of an automatic reminding method based on deep learning according to an embodiment of the present application;
fig. 2 shows a structure diagram of an automatic reminding device based on deep learning according to an embodiment of the application.
Detailed Description
Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that such uses are interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the term "include" and its variants are to be read as open-ended terms meaning "including, but not limited to".
The embodiment of the application provides an automatic reminding method based on deep learning, which can be applied to electronic equipment such as a mobile terminal, a personal computer, a tablet personal computer and an intelligent watch. As shown in fig. 1, the automatic reminding method based on deep learning may include the following steps S101 to S103:
step S101, acquiring a driving image of a driver in a driving process;
step S102, identifying the driving image through a deep learning algorithm, and identifying the driving state of a driver;
and step S103, when the recognized driving state is the designated driving state, automatically making a sound for reminding.
In the embodiment of the application, the driving image of the driver in the driving process can be acquired in real time through the camera in the driving cab, and the real time can be a specified time interval, such as 2 seconds. The deep learning algorithm may be an artificial intelligence algorithm for face recognition, which is not limited in the embodiment of the present application. In addition, the specified driving state mentioned in step S103 may be determined according to actual conditions, such as dozing fatigue driving, drunk driving, and the like, which is not limited in the embodiment of the present application.
The method comprises the steps of firstly, acquiring a driving image of a driver in the driving process; identifying the driving image through a deep learning algorithm to identify the driving state of a driver; and when the recognized driving state is the designated driving state, automatically making a sound for reminding. It can be seen that the abnormal driving state can be timely reminded, the safety of the vehicle in the driving process is improved, and traffic accidents are reduced.
In the embodiment of the present application, a possible implementation manner is provided, and after the step S103 automatically sounds for reminding, the volume of the automatically emitted sound may be controlled according to the duration of the identified driving state as the specified driving state. The sound which can be automatically emitted in the embodiment of the application is larger and larger along with the time until the driver is detected not to be dozing.
The embodiment of the present application provides a possible implementation manner, and the following formula can be used to control the volume of the automatically emitted sound according to the duration of the identified driving state as the specified driving state:
Figure BDA0003441657640000051
wherein e (t) represents the volume value of the sound automatically emitted at the present time; t represents the current time; emaxA maximum volume value representing an automatically emitted sound; (f) (t) represents a driving state output value of the driver recognized by the deep learning algorithm, and if the driving state at the present time is dozing, f (t) is 1, whereas if the driving state at the present time is not dozing, f (t) is 0; t is t0Indicating a time at which the driving state of the driver recognized for the first time is dozing; t represents a preset fatigue time.
The volume of the sound that sends automatically in this application embodiment can be along with fatigue state's time linear increase, and then along with driver fatigue state time is longer, and the volume of sound can be bigger and bigger, and then can reach the effect that intelligence was reminded according to the number of times that the driver was dozed off.
According to the embodiment of the application, the volume of the sound automatically sent out can be controlled according to the driving state of the driver detected by the camera, the driver is reminded through the sound, and the sound volume can be gradually increased along with the increase of the dozing times of the driver, so that the driver can keep awake to the maximum extent.
After the sound is automatically sent out to remind in the step S103, if the duration of the identified driving state being the specified driving state exceeds the preset threshold, the double flashing lights of the vehicle are automatically controlled to remind other vehicles coming and going, and then the cold air of the vehicle is controlled to be turned on. The preset threshold, such as 5 seconds, may be set according to actual requirements, and this is not limited in this embodiment of the application.
The embodiment of the application provides a possible implementation manner, and the following formula can be utilized to control the double flashing and the cold air enabling of the vehicle according to the time length of the identified driving state as the specified driving state:
Figure BDA0003441657640000061
wherein D (t) represents a double flashing and cold air enable control value of the vehicle at the present time;
if D (t) is 0, the control of double flashing and cold air of the vehicle is not needed to be opened at the current moment;
if d (t) is 1, it indicates that the double flashing and the cooling of the vehicle need to be controlled to be turned on at the present time.
In the embodiment of the application, the double flashes and the cold air are started only when the fatigue degree of the driver and the reminding of the maximum volume cannot enable the driver to be awake, so that the vehicle can keep a normal driving state when the driver dozes off occasionally, and the cold air intelligent control can save part of energy consumption.
In the embodiment of the application, can be according to the two flashing and the air conditioning of the driver's that camera history detected drive state control vehicle enable, and then when long-time dozing off appears, the system can open the two flashing and the air conditioning of vehicle, and then reminds other vehicles nearby to and remind the driver through the air conditioning secondary and need keep clear-headed, improve the security of vehicle driving in-process, reduce the emergence of traffic accident.
The embodiment of the application provides a possible implementation manner, and the vehicle cold air temperature can be controlled according to the current time and the cold air enabling and recognized driving state of the driver by using the following formula:
Figure BDA0003441657640000062
wherein q (t) represents a cool air temperature control value of the vehicle at the present time; qmaxRepresents a maximum value of temperature within a cool air controllable range of the vehicle; qminIndicating a minimum value of the temperature within the cool air controllable range of the vehicle.
According to the embodiment of the application, the cold air temperature of the vehicle can be controlled according to the current time and the cold air enable and the state of the driver detected by the camera, and then the cold air temperature is lower along with the longer time that the driver dozes off, so that the driver can keep awake physiologically.
It should be noted that, in practical applications, all the possible embodiments described above may be combined in a combined manner at will to form possible embodiments of the present application, and details are not described here again.
Based on the automatic reminding method based on deep learning provided by each embodiment, the embodiment of the application also provides an automatic reminding device based on deep learning based on the same inventive concept.
Fig. 2 shows a structure diagram of an automatic reminding device based on deep learning according to an embodiment of the application. As shown in fig. 2, the automatic reminding apparatus based on deep learning may include an obtaining module 210, an identifying module 220 and a reminding module 230.
The acquiring module 210 is used for acquiring a driving image of a driver in a driving process;
the recognition module 220 is configured to recognize the driving image through a deep learning algorithm, and recognize a driving state of a driver;
and the reminding module 230 is configured to automatically make a sound for reminding when the identified driving state is the designated driving state.
In the embodiment of the present application, a possible implementation manner is provided, and the reminding module 230 shown in fig. 2 is further configured to:
and after the automatic sound is emitted for reminding, controlling the volume of the automatically emitted sound according to the time length of the identified driving state as the specified driving state.
In the embodiment of the present application, a possible implementation manner is provided, and the reminding module 230 shown in fig. 2 is further configured to:
controlling the volume of the automatically emitted sound according to the duration of the identified driving state as the designated driving state by using the following formula:
Figure BDA0003441657640000071
wherein e (t) represents a volume value of the automatically emitted sound at the current time; t represents the current time; emaxA maximum volume value representing the automatically emitted sound; (f) (t) represents a driving state output value of the driver recognized by the deep learning algorithm, and if the driving state at the present time is dozing, f (t) is 1, whereas if the driving state at the present time is not dozing, f (t) is 0; t is t0Indicating a time at which the driving state of the driver recognized for the first time is dozing; t represents a preset fatigue time.
In the embodiment of the present application, a possible implementation manner is provided, and the reminding module 230 shown in fig. 2 is further configured to:
after the vehicle is automatically sounded for reminding, if the duration of the identified driving state which is the designated driving state exceeds a preset threshold value, the double flashing lamps of the vehicle are automatically controlled to remind other vehicles coming and going, and then the cold air of the vehicle is controlled to be turned on.
In the embodiment of the present application, a possible implementation manner is provided, and the reminding module 230 shown in fig. 2 is further configured to:
controlling the double-flashing and cold air enabling of the vehicle according to the duration of the identified driving state as the designated driving state by using the following formula:
Figure BDA0003441657640000081
wherein D (t) represents a double flashing and cold air enable control value of the vehicle at the present time;
if D (t) is 0, the control of double flashing and cold air of the vehicle is not needed to be opened at the current moment;
if d (t) is 1, it indicates that the double flashing and the cooling of the vehicle need to be controlled to be turned on at the present time.
In the embodiment of the present application, a possible implementation manner is provided, and the reminding module 230 shown in fig. 2 is further configured to:
controlling the vehicle cool air temperature according to the current time and the cool air enable and recognized driving state of the driver using the following formula:
Figure BDA0003441657640000082
wherein q (t) represents a cool air temperature control value of the vehicle at the present time; qmaxRepresents a maximum value of temperature within a cool air controllable range of the vehicle; qminIndicating a minimum value of the temperature within the cool air controllable range of the vehicle.
The automatic reminding device based on deep learning provided by the embodiment of the application firstly obtains a driving image of a driver in a driving process; identifying the driving image through a deep learning algorithm to identify the driving state of a driver; and when the recognized driving state is the designated driving state, automatically making a sound for reminding. It can be seen that the abnormal driving state can be timely reminded, the safety of the vehicle in the driving process is improved, and traffic accidents are reduced.
It can be clearly understood by those skilled in the art that the specific working processes of the system, the apparatus, and the module described above may refer to the corresponding processes in the foregoing method embodiments, and for the sake of brevity, the detailed description is omitted here.
Those of ordinary skill in the art will understand that: the technical solution of the present application may be essentially or wholly or partially embodied in the form of a software product, where the computer software product is stored in a storage medium and includes program instructions for enabling an electronic device (e.g., a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application when the program instructions are executed. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Alternatively, all or part of the steps of implementing the foregoing method embodiments may be implemented by hardware (an electronic device such as a personal computer, a server, or a network device) associated with program instructions, which may be stored in a computer-readable storage medium, and when the program instructions are executed by a processor of the electronic device, the electronic device executes all or part of the steps of the method described in the embodiments of the present application.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments can be modified or some or all of the technical features can be equivalently replaced within the spirit and principle of the present application; such modifications or substitutions do not depart from the scope of the present application.

Claims (10)

1. An automatic reminding method based on deep learning is characterized by comprising the following steps:
acquiring a driving image of a driver in a driving process;
identifying the driving image through a deep learning algorithm to identify the driving state of a driver;
and when the recognized driving state is the designated driving state, automatically making a sound for reminding.
2. The automatic reminding method based on deep learning of claim 1, wherein after automatically sounding for reminding, the method further comprises:
and controlling the volume of the automatically emitted sound according to the duration of the identified driving state as the specified driving state.
3. The automatic reminding method based on deep learning according to claim 2, characterized in that the volume of the automatically emitted sound is controlled according to the duration of the identified driving state as the specified driving state by using the following formula:
Figure FDA0003441657630000011
wherein e (t) represents a volume value of the automatically emitted sound at the current time; t represents the current time; emaxA maximum volume value representing the automatically emitted sound; (f) (t) represents a driving state output value of the driver recognized by the deep learning algorithm, and if the driving state at the present time is dozing, f (t) is 1, whereas if the driving state at the present time is not dozing, f (t) is 0; t is t0Indicating a time at which the driving state of the driver recognized for the first time is dozing; t represents a preset fatigue time.
4. The automatic reminding method based on deep learning of claim 3, wherein after automatically sounding for reminding, the method further comprises:
if the recognized driving state is that the duration of the specified driving state exceeds a preset threshold value, the double flashing lamps of the vehicle are automatically controlled to further remind other vehicles in the coming and going directions, and then the cold air of the vehicle is controlled to be turned on.
5. The automatic reminding method based on deep learning of claim 4, characterized in that the double flashing and cold air enabling of the vehicle are controlled according to the time length of the identified driving state as the specified driving state by using the following formula:
Figure FDA0003441657630000021
wherein D (t) represents a double flashing and cold air enable control value of the vehicle at the present time;
if D (t) is 0, the control of double flashing and cold air of the vehicle is not needed to be opened at the current moment;
if d (t) is 1, it indicates that the double flashing and the cooling of the vehicle need to be controlled to be turned on at the present time.
6. The deep learning-based automatic reminding method according to claim 5, wherein the vehicle cool air temperature is controlled according to the current time and the cool air enable and recognized driving state of the driver by using the following formula:
Figure FDA0003441657630000022
wherein q (t) represents a cool air temperature control value of the vehicle at the present time; qmaxRepresents a maximum value of temperature within a cool air controllable range of the vehicle; qminIndicating a minimum value of the temperature within the cool air controllable range of the vehicle.
7. An automatic reminding device based on deep learning, comprising:
the acquisition module is used for acquiring a driving image of a driver in the driving process;
the recognition module is used for recognizing the driving image through a deep learning algorithm and recognizing the driving state of a driver;
and the reminding module is used for automatically making a sound for reminding when the identified driving state is the designated driving state.
8. The automatic deep learning based reminder device of claim 7, wherein the reminder module is further configured to:
and after the automatic sound is emitted for reminding, controlling the volume of the automatically emitted sound according to the time length of the identified driving state as the specified driving state.
9. The automatic deep learning based reminder device of claim 8, wherein the reminder module is further configured to:
controlling the volume of the automatically emitted sound according to the duration of the identified driving state as the designated driving state by using the following formula:
Figure FDA0003441657630000031
wherein e (t) represents a volume value of the automatically emitted sound at the current time; t represents the current time; emaxA maximum volume value representing the automatically emitted sound; (f) (t) represents a driving state output value of the driver recognized by the deep learning algorithm, and if the driving state at the present time is dozing, f (t) is 1, whereas if the driving state at the present time is not dozing, f (t) is 0; t is t0Indicating a time at which the driving state of the driver recognized for the first time is dozing; t represents a preset fatigue time.
10. The automatic deep learning based reminder device of claim 9, wherein the reminder module is further configured to:
after the vehicle is automatically sounded for reminding, if the duration of the identified driving state which is the designated driving state exceeds a preset threshold value, the double flashing lamps of the vehicle are automatically controlled to remind other vehicles coming and going, and then the cold air of the vehicle is controlled to be turned on.
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