CN112455452A - Method, device and equipment for detecting driving state - Google Patents
Method, device and equipment for detecting driving state Download PDFInfo
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- CN112455452A CN112455452A CN202011373819.4A CN202011373819A CN112455452A CN 112455452 A CN112455452 A CN 112455452A CN 202011373819 A CN202011373819 A CN 202011373819A CN 112455452 A CN112455452 A CN 112455452A
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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
- B60W2040/0818—Inactivity or incapacity of driver
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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
- B60W2040/0818—Inactivity or incapacity of driver
- B60W2040/0827—Inactivity or incapacity of driver due to sleepiness
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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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Abstract
The embodiment of the application discloses a method, a device and equipment for detecting a driving state. The method comprises the following steps: when the situation that a driver is in a vehicle driving state is monitored, acquiring driving state data of the driver; the driving state data comprises electroencephalogram data and/or human face image data; analyzing the driving state data to obtain an analysis result; determining whether the driver is in an abnormal driving state according to the analysis result; the abnormal driving state includes a distracted driving state and/or a fatigue driving state. According to the technical scheme, whether the driver is in the abnormal driving state or not is analyzed based on the brain wave data and/or the face image data, so that the judgment result of the driving state of the driver is more accurate, corresponding measures can be executed on the abnormal driving state of the driver accurately in follow-up, and the driving safety factor of the driver is improved.
Description
Technical Field
The invention relates to the technical field of vehicle safety, in particular to a method, a device and equipment for detecting a driving state.
Background
With the increasing importance of people on the driving safety problem, more and more people begin to pay attention to the fatigue driving problem. According to statistics of some industry survey data, the proportion of accidents caused by fatigue driving in the serious traffic accidents reaches more than 40 percent, and the method is one of important reasons for the occurrence of the serious traffic accidents. Particularly, drivers are easy to lose attention or doze due to fatigue in long-distance driving and the like, so that traffic accidents are caused. When a driver is in a fatigue state, the driver feels sleepy and sleepy, judgment force is reduced, and driving skill is objectively reduced, and traffic accidents easily occur if the driver still drives. Therefore, how to accurately detect the driving state of the driver becomes one of the problems which are urgently needed to be solved at present.
In the prior art, a vehicle-mounted fatigue driving detection System is generally implemented by a camera of a DMS (Dealer Management System), and first acquires driving behavior information of a driver, obtains offset index information according to the driving behavior information, determines whether the driver is in a fatigue driving state according to the offset index information, and sends out a warning sound if the driver is in the fatigue driving state. Therefore, in the prior art, whether the driver is in a fatigue driving state or not is judged according to the deviation index information obtained according to the driving behavior information of the driver, so that the judgment mode is single, the problems of large judgment error and low detection accuracy are caused, the fatigue driving relieving mode is simple, the intelligence degree is low, and the safety coefficient of a driving vehicle is reduced.
Disclosure of Invention
The embodiment of the application aims to provide a method, a device and equipment for detecting a driving state, which are used for solving the problem of low detection accuracy of the driving state of a driver in the prior art.
In order to solve the above technical problem, the embodiment of the present application is implemented as follows:
in one aspect, an embodiment of the present application provides a method for detecting a driving state, including:
when the situation that a driver is in a vehicle driving state is monitored, acquiring driving state data of the driver; the driving state data comprises electroencephalogram data and/or human face image data;
analyzing the driving state data to obtain an analysis result;
determining whether the driver is in an abnormal driving state according to the analysis result; the abnormal driving state includes a distracted driving state and/or a fatigue driving state.
On the other hand, an embodiment of the present application provides a driving state detection apparatus, including:
the acquisition module is used for acquiring driving state data of a driver when the driver is monitored to be in a vehicle driving state; the driving state data comprises electroencephalogram data and/or human face image data;
the analysis module is used for analyzing the driving state data to obtain an analysis result;
the first determination module is used for determining whether the driver is in an abnormal driving state or not according to the analysis result; the abnormal driving state includes a distracted driving state and/or a fatigue driving state.
In another aspect, an embodiment of the present application provides a driving state detection apparatus, including a processor and a memory electrically connected to the processor, where the memory stores a computer program, and the processor is configured to call and execute the computer program from the memory to implement:
when the situation that a driver is in a vehicle driving state is monitored, acquiring driving state data of the driver; the driving state data comprises electroencephalogram data and/or human face image data;
analyzing the driving state data to obtain an analysis result;
determining whether the driver is in an abnormal driving state according to the analysis result; the abnormal driving state includes a distracted driving state and/or a fatigue driving state.
In another aspect, an embodiment of the present application provides a storage medium for storing a computer program, where the computer program is executed by a processor to implement the following processes:
when the situation that a driver is in a vehicle driving state is monitored, acquiring driving state data of the driver; the driving state data comprises electroencephalogram data and/or human face image data;
analyzing the driving state data to obtain an analysis result;
determining whether the driver is in an abnormal driving state according to the analysis result; the abnormal driving state includes a distracted driving state and/or a fatigue driving state.
By adopting the technical scheme of the embodiment of the invention, when the driving state of the driver is monitored, the driving state data (including brain wave data and/or face image data) of the driver is acquired and analyzed, so that whether the driver is in an abnormal driving state or not is determined according to the analysis result. Because the brain wave data and/or the face image data can accurately represent the current driving state of the driver, the technical scheme analyzes whether the driver is in the abnormal driving state or not based on the brain wave data and/or the face image data, so that the judgment result of the driving state of the driver is more accurate, corresponding measures can be executed on the abnormal driving state of the driver accurately in the follow-up process, and the driving safety factor of the driver is improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart of a method of detecting a driving state according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a driving state detection method according to another embodiment of the present invention;
fig. 3 is a schematic block diagram of a driving state detection apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a driving state detection apparatus according to an embodiment of the present invention.
Detailed Description
The embodiment of the application aims to provide a method and a device for detecting a driving state, which are used for solving the problem of low detection accuracy of the driving state of a driver in the prior art.
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is a schematic flow chart of a driving state detection method according to an embodiment of the present invention, as shown in fig. 1, the method including:
s102, when the situation that the driver is in the vehicle driving state is monitored, the driving state data of the driver are obtained.
The driving state data comprises electroencephalogram data and/or human face image data.
In one embodiment, electroencephalogram data of a driver can be acquired by an electroencephalogram wearable device worn by the driver, wherein the electroencephalogram wearable device can be a portable device which can acquire electroencephalogram data in a plurality of wearing modes such as ear wearing type and frontal wearing type. The electroencephalogram data collected by the electroencephalogram wearable device can comprise electroencephalogram signals, facial muscle signals, eye muscle signals and the like.
The built-in camera device (such as a camera) of the vehicle can be used for collecting the face image data of the driver, and the current actions of closing eyes, yawning and turning the head of the driver can be identified through the face image data collected by the camera device.
And S104, analyzing the driving state data of the driver to obtain an analysis result.
If the driving state data of the driver is electroencephalogram data, first state information corresponding to the driver, which may include information such as concentration and fatigue, can be obtained by analyzing the electroencephalogram signal of the driver. By analyzing the facial muscle signals and/or eye muscle signals of the driver, whether the driver has facial fatigue behavior or not, the frequency and/or the frequency of the facial fatigue behavior, and the like can be determined. The facial fatigue behavior may be yawning, eye closing, blinking, etc.
If the driving state data of the driver is the face image data, whether the abnormal driving behavior occurs to the driver, the frequency and/or the frequency of the abnormal driving behavior and the like can be determined by analyzing the face image data of the driver. Abnormal driving behavior may include a deviation of line of sight from a normal driving direction, a call, yawning, eye closure, etc.
And S106, determining whether the driver is in an abnormal driving state or not according to the analysis result.
Wherein the abnormal driving state includes a distracted driving state and/or a fatigue driving state.
By adopting the technical scheme provided by the embodiment of the invention, when the driving state of the driver is monitored, the driving state data (including brain wave data and/or face image data) of the driver is acquired and analyzed, so that whether the driver is in an abnormal driving state or not is determined according to the analysis result. Because the brain wave data and/or the face image data can accurately represent the current driving state of the driver, the technical scheme analyzes whether the driver is in the abnormal driving state or not based on the brain wave data and/or the face image data, so that the judgment result of the driving state of the driver is more accurate, corresponding measures can be executed on the abnormal driving state of the driver accurately in the follow-up process, and the driving safety factor of the driver is improved.
How to perform the corresponding analysis on the driving state data of different categories to obtain the analysis result is described in detail below.
In one embodiment, the driving state data is brain wave data including at least one of brain wave signals, facial muscle signals, and eye muscle signals.
In this embodiment, the electroencephalogram data may be analyzed in any one or more of the following manners:
in the first aspect, a brain wave signal of a driver is analyzed to obtain first state information corresponding to the driver, where the first state information includes information such as concentration degree and fatigue degree. Concentration, fatigue, and other information are obtained by calculating a Power Spectral Density (PSD) of the brain wave signal. The power carried by a wave per unit frequency is obtained when the spectral density of the power of the wave is multiplied by a suitable coefficient, which is called the power spectral density of the signal. The higher the power spectral density, the higher the concentration and the lower the fatigue; conversely, the smaller the power spectral density, the lower the concentration and the higher the fatigue.
And in the second mode, facial muscle signals of the driver are analyzed to obtain the frequency of facial fatigue behaviors of the driver within a first preset time length. For example, the facial fatigue behavior may be yawning behavior, and the frequency and/or the frequency of the yawning behavior of the driver within the first preset time period is obtained by analyzing the facial muscle signal of the driver. The first preset duration may be 20 seconds, 30 seconds, and the like, and the number of yawning may be 2 times, 3 times, and the like, and this embodiment does not limit the specific value of the set value.
And in the third mode, eye muscle signals of the driver are analyzed to obtain the frequency of the facial fatigue behaviors of the driver in the first preset time. For example, the facial fatigue behavior may be an eye closure behavior, and the frequency and/or the number of times of the eye closure behavior of the driver within the first preset time period is obtained by analyzing eye muscle signals of the driver. The first preset time period may be 2 seconds, 3 seconds, and the like, and the embodiment does not limit the specific value of the set value.
Of course, multiple of the above-described modes can be combined for analysis.
In this embodiment, through carrying out the analysis to brain wave data, can accurately analyze out the current state information of driver, if concentrate on degree, fatigue degree etc. because these state information can accurate representation driver's driving state, consequently be favorable to follow-up accurate analysis driver's driving state, provide accurate data basis for the analysis of driving state.
In one embodiment, the driving state data is facial image data. And analyzing the facial image data to obtain the occurrence frequency of the abnormal driving behaviors of the driver in a third preset time length. The abnormal driving behaviors can include behaviors of deviating the sight line from the normal driving direction, making a call, yawning, closing eyes and the like.
In this embodiment, the analysis of the face image data may obtain the following analysis results: whether the driver has the behavior that the sight line deviates from the normal driving direction within the third preset time length, whether the driver has the behavior of making a call within the third preset time length, the frequency and/or the frequency of yawning behavior of the driver within the third preset time length, the frequency and/or the frequency of eye closing behavior of the driver within the third preset time length and the like.
For example, for analysis of face image data, the analysis results may be: the sight line of the driver deviates from the normal driving direction for 3 seconds, the yawning times of the driver exceeds 2 times within 30 seconds, the eyes of the driver are closed for more than 3 seconds, and the like. It should be noted that, in this embodiment, a specific value of the third preset time period is not limited.
In the embodiment, the current state information of the driver, such as behaviors of deviating from a normal driving direction and making a call, can be accurately analyzed by analyzing the face image data, and the driving state of the driver can be accurately represented by the state information, so that the driving state of the driver can be accurately analyzed subsequently, and an accurate data basis is provided for analysis of the driving state.
How to determine whether the driver is in the abnormal driving state based on the analysis result is explained in detail below.
In one embodiment, the driving state data includes brain wave data including at least one of brain wave signals, facial muscle signals, and eye muscle signals, and face image data. When determining whether the driver is in an abnormal driving state according to the analysis result corresponding to the electroencephalogram data, determining according to the following mode:
if the concentration degree of the driver is continuously lower than a first preset threshold value within a second preset time period, determining that the driver is in a distracted driving state;
and if the fatigue degree of the driver is continuously higher than a second preset threshold value within a second preset time period, determining that the driver is in a fatigue driving state.
And if the frequency of occurrence of the facial fatigue behaviors of the driver in the first preset time period is higher than a third preset threshold value, determining that the driver is in a fatigue driving state. The facial fatigue behavior may be yawning, eye closing, blinking, etc.
For example, if the number of yawning by the driver is higher than 2 times within 30 seconds, it is determined that the driver is in a fatigue driving state; and if the driver has continuous eye closing behavior for more than 3 seconds, determining that the driver is in a fatigue driving state.
When determining whether the driver is in an abnormal driving state according to the analysis result corresponding to the face image data, the determination may be performed as follows:
and if the occurrence frequency of the abnormal driving behaviors of the driver in the third preset time period is higher than the fourth preset threshold value, determining that the driver is in a fatigue driving state. Abnormal driving behavior may include behavior that may include a line of sight deviation from a normal driving direction, a call, yawning, eye closure, and the like.
If the sight line of the driver deviates from the normal driving direction within the third preset time length, determining that the driver is in a distracted driving state; if the driver has a call-making behavior within the third preset time, determining that the driver is in a distracted driving state; if the frequency of yawning actions of the driver in the third preset time length is higher than a fourth preset threshold value, determining that the driver is in a fatigue driving state; and if the frequency of the eye closing behavior of the driver in the third preset time period is higher than a fourth preset threshold value, determining that the driver is in a fatigue driving state.
When one or more of the analysis results are obtained, it can be determined that the driver is in an abnormal driving state.
In the embodiment, the driving state of the driver represented based on the brain wave data and the face image data can be obtained by analyzing the brain wave data and the face image data, so that whether the driver is in the abnormal driving state or not is judged, the judgment result of the driving state of the driver is more accurate, corresponding measures can be accurately executed on the abnormal driving state of the driver subsequently, and the driving safety factor of the driver is improved.
In one embodiment, after determining that the driver is in the abnormal driving state according to the analysis result corresponding to the brain wave data and/or the face image data, the abnormal degree corresponding to the abnormal driving state of the driver can be determined, and then corresponding abnormal driving mitigation measures are executed for the abnormal degrees of different levels. The abnormal driving mitigation measures may include at least one of:
sending first prompt information for prompting that a driver is in an abnormal driving state at present; the prompting mode of the first prompting message can be voice prompting, text message prompting and the like.
Controlling the starting work of specified equipment in the vehicle; the designated equipment can comprise an on-vehicle air conditioner, an on-vehicle voice device, an on-vehicle fragrance device and the like.
And controlling the steering wheel to vibrate according to a preset frequency.
In this embodiment, the abnormal degree corresponding to the abnormal driving state may be determined according to the type of the abnormal driving behavior (such as eye closure, yawning, deviation of the line of sight from the normal driving direction, etc.) appearing by the driver, the frequency of the abnormal driving behavior, the concentration degree, the fatigue degree, and other factors. The degree of abnormality can be divided into three levels of low, medium and high. Alternatively, different abnormal degrees may be set in advance for different kinds of abnormal driving behaviors, and different abnormal degrees may be set for different values or ranges corresponding to the concentration degree and the fatigue degree.
For example, with a preset duration of 10s as a time threshold, if the number of yawning actions of the driver within 10s reaches 5 times or more than 5 times, determining that the degree of abnormality is high; if the number of times of yawning behaviors of the driver within 10s is between [ 1-3 ], determining that the abnormal degree is low and the like; and if the times of yawning behaviors of the driver within 10s are within [ 3-4 ], determining that the abnormal degree is medium.
For another example, still taking the preset duration 10s as a time threshold, if the concentration of the driver is lower than a preset threshold all the time within 10s, determining that the degree of abnormality is high; if the concentration degree of the driver is more than 5s and lower than the preset threshold value within 10s, determining that the abnormality degree is medium; and so on.
Further, the degree of abnormality may also be determined in combination with a plurality of factors in the kind of abnormal driving behavior, the frequency of the abnormal driving behavior, the concentration degree, and the fatigue degree. Optionally, a corresponding weight may be preset for each factor, and when determining the abnormal degree, the abnormal degree corresponding to the current abnormal driving state of the driver may be determined comprehensively according to the weight corresponding to each factor.
It should be noted that in other embodiment(s), the degree of abnormality may be characterized in other forms. For example, the abnormality degrees are classified into A, B, C, D, E kinds of degrees, and the abnormality degrees decrease sequentially from a to E, that is, the abnormality degree for a is the highest, and the abnormality degree for E is the lowest.
In the present embodiment, after determining the degree of abnormality of the abnormal driving state in which the driver is located, corresponding abnormal driving mitigation measures are executed for the degree of abnormality of different levels. The corresponding measures for alleviating abnormal driving include the following:
(1) when the driver is determined to be in the driving state with low abnormal degree, the vehicle-mounted voice device sends out voice prompt to remind the driver of being in the abnormal driving state at present, and the steering wheel is controlled to vibrate according to the preset frequency. For example, an in-vehicle voice device utters: "attention! You have distracted from driving, please pay attention to driving safety! "voice prompt.
(2) When the driver is determined to be in the abnormal driving state, the vehicle-mounted voice device sends out voice prompt to remind the driver of being in the abnormal driving state at present, the vehicle-mounted air conditioner is controlled to reduce the temperature and increase the air volume, and meanwhile, the vehicle-mounted fragrance device releases the refreshing fragrance to blow to the driver. For example, an in-vehicle voice device utters: "attention! You are tired of driving, please increase attention, pay attention to driving safety! "voice prompt.
(3) When the driver is determined to be in the driving state with high abnormal degree, the vehicle-mounted voice device sends out voice prompt to remind the driver of being in the abnormal driving state currently, the steering wheel is controlled to vibrate according to the preset frequency, and meanwhile, the specified devices in the vehicle are started together. For example, an in-vehicle voice device utters: "attention! You are tired of driving, please increase attention, pay attention to driving safety! The vehicle-mounted voice device automatically plays the refreshing music, the volume is properly increased, the steering wheel is controlled to continuously vibrate for 2 seconds every 2 seconds, the vehicle-mounted air conditioner is controlled to reduce the temperature and increase the air volume, and meanwhile, the vehicle-mounted fragrance device releases the refreshing fragrance to blow to a driver.
In the embodiment, under the condition that the driver is determined to be in the abnormal driving state, the abnormal degree corresponding to the abnormal driving state of the driver can be determined, and corresponding abnormal driving relieving measures are executed on the abnormal driving states with different degrees, so that the relieving measures executed on the driver are more targeted, and the driving safety coefficient of the driver is improved.
In one embodiment, when the driver is monitored to be in the driving state, brain wave data and/or face image data of the driver are acquired, the driver is determined to be in an abnormal driving state according to the brain wave data and/or the face image data of the driver, the abnormal degree corresponding to the abnormal driving state is determined, after the abnormal driving relieving measure is executed on the driver, the driving state of the driver is continuously monitored, when the driver is monitored to eliminate the abnormal driving state within a fourth preset time period, the abnormal driving relieving measure is stopped being executed, and the normal driving state is recovered. When it is monitored that the abnormal driving state of the driver is not eliminated within the fourth preset time, namely when it is monitored that the driver is still in the abnormal driving state within the fourth preset time, a rest area within the preset range is located, and second prompt information for prompting the driver to go to the rest area is sent out.
Wherein the rest area can be a rest service station, a clock room, a hotel and the like.
After the second prompt message is sent out, when the determination operation of the driver for the target rest area is received, determining the geographical position information of the target rest area; and further starting a specified navigation application program, and navigating based on the geographical position information of the target rest area through the navigation application program.
For example, the fourth preset time period is 3 minutes. Within 3 minutes of executing the abnormal driving relieving measure on the driver, when the driver is monitored to be still in the abnormal driving state, namely the abnormal driving state cannot be relieved, a rest area which is 5 kilometers away from the current geographic position of the vehicle is positioned through the navigation application program, and the vehicle-mounted voice device sends out: "attention! You have been driving severely tired, please go to a rest place for rest! The consent request replies with 'yes'. And the voice prompt of the step (2) controls the display screen of the center console in the car to display the options of yes and no (namely the second prompt message).
After receiving the second prompt message, the driver can reply with the voice "yes" or click a "yes" option on a display screen of a center console in the vehicle to complete the determination operation of the target rest area. After the determination operation is received, the navigation application program navigates the target rest area and can broadcast the navigation route so that the driver can drive to the target rest area for rest according to the navigation route, thereby ensuring that the driver does not fatigue or distract from driving and ensuring the safety of the driver.
Fig. 2 is a schematic flow chart of a driving state detection method according to another embodiment of the present invention, which is applied to a processor inside a vehicle, a network connection between the processor and a cloud platform located on a network side, as shown in fig. 2, and includes the following steps:
s201, when it is monitored that the driver is in a driving state, brain wave data of the driver is collected, and face image data of the driver is collected.
In the step, electroencephalogram wearable equipment can be used for collecting electroencephalogram data of a driver, and a camera in a vehicle is used for collecting face image data of the driver. The brain wave data may include: brain wave signals, facial muscle signals, eye muscle signals.
It should be noted that, in this embodiment, the acquisition order of the electroencephalogram data and the human face image data is not limited. In an actual scene, the brain wave data and the face image data can be acquired simultaneously.
S202, analyzing the acquired electroencephalogram data and the acquired human face image data to obtain an analysis result.
In the step, after the acquired electroencephalogram data and the acquired human face image data are processed and analyzed by a processor in the vehicle, the obtained analysis result can be one or more of the following analysis results:
the brain wave signals of the driver are analyzed, and information such as concentration degree and fatigue degree of the driver can be obtained; analyzing the facial muscle signals and the eye muscle signals of the driver to obtain the occurrence frequency of facial fatigue behaviors (such as yawning, eye closing, blinking and the like) of the driver within a first preset time length; the facial image data of the driver are analyzed, and the occurrence frequency of abnormal driving behaviors (such as line of sight deviation from a normal driving direction, calling, yawning, eye closing and the like) of the driver in a third preset time length can be obtained.
And S203, judging whether the driver is in an abnormal driving state or not according to the analysis result. If not, returning to S201; if yes, go to step S204.
In the present embodiment, the abnormal driving state includes a distracted driving state and/or a fatigue driving state.
If the concentration degree of the driver is continuously lower than a first preset threshold value within a second preset time period, determining that the driver is in a distracted driving state; if the fatigue degree of the driver is continuously higher than a second preset threshold value within a second preset time period, determining that the driver is in a fatigue driving state; and if the occurrence frequency of the facial fatigue behaviors of the driver in the first preset time period is higher than a third preset threshold value, determining that the driver is in the fatigue driving state. And if the occurrence frequency of the abnormal driving behaviors of the driver in the third preset time period is higher than the fourth preset threshold value, determining that the driver is in a fatigue driving state.
And S204, determining the abnormal degree corresponding to the abnormal driving state.
In this step, the abnormal degree corresponding to the abnormal driving state may be determined according to the type of the abnormal driving behavior (such as eye closure, yawning, deviation of the line of sight from the normal driving direction, etc.) appearing by the driver, the frequency of the abnormal driving behavior, the concentration degree, the fatigue degree, and other factors. The degree of abnormality can be divided into three levels of low, medium and high. Alternatively, different abnormal degrees may be set in advance for different kinds of abnormal driving behaviors, and different abnormal degrees may be set for different values or ranges corresponding to the concentration degree and the fatigue degree.
And S205, executing corresponding abnormal driving relieving measures for the driver according to the abnormal degree corresponding to the abnormal driving state.
In the present embodiment, the abnormal driving alleviation measure may include at least one of: sending first prompt information for prompting a driver to be in an abnormal driving state at present, controlling specified equipment in the vehicle to start, controlling a steering wheel to vibrate according to a preset frequency and the like, wherein the specified equipment can comprise a vehicle-mounted air conditioner, a vehicle-mounted voice device, a vehicle-mounted fragrance device and the like.
S206, monitoring whether the abnormal driving state of the driver is eliminated within a fourth preset time period; if yes, go to step S207, otherwise go to step S208.
In the step, corresponding abnormal driving mitigation measures are executed according to the abnormal driving state of the driver, the driving state data of the driver are continuously acquired within the fourth preset duration of the execution of the abnormal driving mitigation measures, and whether the driver is still in the abnormal driving state is determined according to the analysis result.
And S207, stopping executing the abnormal driving relieving measure.
In this step, if the driver eliminates the abnormal driving state within the fourth preset time period, that is, after the driver is determined to be in the normal driving state, the vehicle-mounted air conditioner, the vehicle-mounted voice device, the vehicle-mounted fragrance device and the steering wheel controlled by the processor in the vehicle are all restored to be normal.
S208, positioning a rest area within the preset range, and sending out second prompt information for prompting the driver to go to the rest area.
In the step, if the abnormal driving state of the driver is not eliminated within the fourth preset time, namely after the driver is still in the abnormal driving state within the fourth preset time, the driver is automatically positioned to a rest area within the preset range through the navigation application program, and the vehicle-mounted voice device is controlled to send out second prompt information to remind the driver to go to the rest area to have a rest in time.
S209, when the determination operation of the driver for the target rest area is received, starting the specified navigation application program, and navigating based on the target rest area.
In the step, after the determination operation of the driver on the second prompt information is received, the navigation application program is automatically started to navigate the target rest area, and the navigation route can be broadcasted, so that the driver drives the target rest area to rest according to the navigation route, fatigue driving or distraction driving of the driver is avoided, and safety of the driver is guaranteed.
By adopting the technical scheme provided by the embodiment of the invention, when the driving state of the driver is monitored, the driving state data (including electroencephalogram data and facial image data) of the driver is obtained and analyzed, and then whether the driver is in an abnormal driving state or not is determined according to the analysis result. Because the brain wave data and the face image data can accurately represent the current driving state of the driver, the technical scheme analyzes whether the driver is in an abnormal driving state or not based on the brain wave data and the face image data, so that the judgment result of the driving state of the driver is more accurate. Corresponding relieving measures are executed on abnormal driving states of the driver in different degrees, and an intelligent recommendation system of ecological services is combined, so that the measures for relieving the abnormal driving states are diversified, the driver can obtain more effective relieving measures, and the driving safety factor of the driver is improved.
In summary, particular embodiments of the present subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may be advantageous.
Based on the same idea, the driving state detection method provided in the embodiment of the present application further provides a driving state detection device.
Fig. 3 is a schematic block diagram of a driving state detection apparatus according to an embodiment of the present invention, as shown in fig. 3, the apparatus including:
the acquiring module 310 is configured to acquire driving state data of a driver when it is monitored that the driver is in a vehicle driving state; the driving state data comprises electroencephalogram data and/or human face image data;
the analysis module 320 is configured to analyze the driving state data to obtain an analysis result;
a first determining module 330, configured to determine whether the driver is in an abnormal driving state according to the analysis result; the abnormal driving state includes a distracted driving state and/or a fatigue driving state.
In one embodiment, the obtaining module 310 includes:
the first acquisition unit is used for acquiring the electroencephalogram data of the driver by utilizing electroencephalogram wearable equipment worn by the driver; and/or the presence of a gas in the gas,
and the second acquisition unit is used for acquiring the facial image data of the driver by utilizing a camera in the vehicle.
In one embodiment, the driving state data includes the brain wave data; the brain wave data includes at least one of brain wave signals, facial muscle signals, and eye muscle signals;
the analysis module 320 includes:
the first analysis unit is used for analyzing the brain wave signals to obtain first state information corresponding to the driver; the first state information comprises at least one of concentration, fatigue; and/or the presence of a gas in the gas,
and the second analysis unit is used for analyzing the facial muscle signals and/or the eye muscle signals to obtain the frequency of the facial fatigue behaviors of the driver in a first preset time period.
In one embodiment, the first determining module 330 includes:
the first determination unit is used for determining that the driver is in the distracted driving state if the concentration degree is continuously lower than a first preset threshold value within a second preset duration; and/or the presence of a gas in the gas,
the second determining unit is used for determining that the driver is in the fatigue driving state if the fatigue degree is continuously higher than a second preset threshold value within a second preset time period; and/or the presence of a gas in the gas,
a third determining unit, configured to determine that the driver is in the fatigue driving state if the frequency of occurrence of the facial fatigue behavior within the first preset time period is higher than a third preset threshold.
In one embodiment, the driving state data includes the facial image data;
the analysis module 320 includes:
the third analysis unit is used for analyzing the facial image data to obtain the occurrence frequency of abnormal driving behaviors of the driver within a third preset time length;
the first determining module 330 includes:
and the fourth determining unit is used for determining that the driver is in the fatigue driving state if the occurrence frequency of the abnormal driving behaviors in the third preset time period is higher than a fourth preset threshold value.
In one embodiment, the apparatus further comprises:
the second determining module is used for determining the abnormal degree corresponding to the abnormal driving state if the driver is determined to be in the abnormal driving state;
the first execution module is used for executing corresponding abnormal driving relieving measures for the driver according to the abnormal degree;
wherein the abnormal driving mitigation measures include at least one of:
sending first prompt information for prompting the current abnormal driving state;
controlling appointed equipment in the vehicle to start, wherein the appointed equipment comprises at least one of a vehicle-mounted air conditioner, a vehicle-mounted voice device and a vehicle-mounted fragrance device;
and controlling the steering wheel to vibrate according to a preset frequency.
In one embodiment, the apparatus further comprises:
the monitoring module is used for monitoring whether the abnormal driving state of the driver is eliminated within a fourth preset time length after the corresponding abnormal driving relieving measure is executed on the driver according to the abnormal degree;
the execution stopping module is used for stopping executing the abnormal driving relieving measures if the abnormal driving relieving measures are met;
and the prompting module is used for positioning a rest area in a preset range and sending second prompting information for prompting the driver to go to the rest area if the driver does not go to the rest area.
In one embodiment, the apparatus further comprises:
a third determining module, configured to determine, after the sending of the second prompt information for prompting the driver to go to the rest area, geographic location information of a target rest area when a determination operation of the driver for the target rest area is received;
and the second execution module is used for starting a specified navigation application program and navigating based on the geographic position information.
It should be understood by those skilled in the art that the driving state detecting device of fig. 3 can be used to implement the driving state detecting method described above, and the detailed description thereof should be similar to that of the method described above, and is not repeated herein to avoid complexity.
By adopting the device provided by the embodiment of the invention, when the driving state of the driver is monitored, the driving state data (including brain wave data and/or face image data) of the driver is obtained and analyzed, so that whether the driver is in an abnormal driving state or not is determined according to the analysis result. Because the brain wave data and/or the face image data can accurately represent the current driving state of the driver, the device analyzes whether the driver is in an abnormal driving state or not based on the brain wave data and/or the face image data, so that the judgment result of the driving state of the driver is more accurate, corresponding measures can be executed on the abnormal driving state of the driver accurately in the follow-up process, and the driving safety factor of the driver is improved.
Based on the same idea, the embodiment of the present application further provides a device for detecting a driving state, as shown in fig. 4. The driving state detection device may have a relatively large difference due to different configurations or performances, and may include one or more processors 401 and a memory 402, where the memory 402 may store one or more stored applications or data. Wherein memory 402 may be transient or persistent. The application program stored in memory 402 may include one or more modules (not shown), each of which may include a series of computer-executable instructions in a device for detecting driving conditions. Still further, the processor 401 may be configured to communicate with the memory 402 to execute a series of computer-executable instructions in the memory 402 on a detection device of a driving condition. The detection apparatus of the driving state may also include one or more power sources 403, one or more wired or wireless network interfaces 404, one or more input-output interfaces 405, one or more keyboards 406.
In particular, in this embodiment, the driving state detection device includes a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions in the driving state detection device, and the one or more programs configured to be executed by the one or more processors include computer-executable instructions for:
when the situation that a driver is in a vehicle driving state is monitored, acquiring driving state data of the driver; the driving state data comprises electroencephalogram data and/or human face image data;
analyzing the driving state data to obtain an analysis result;
determining whether the driver is in an abnormal driving state according to the analysis result; the abnormal driving state includes a distracted driving state and/or a fatigue driving state.
The embodiment of the present application further provides a storage medium, where the storage medium stores one or more computer programs, where the one or more computer programs include instructions, and when the instructions are executed by an electronic device including multiple application programs, the electronic device can execute the processes of the above-mentioned method for detecting a driving state, and can achieve the same technical effects, and in order to avoid repetition, details are not described here again.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method of detecting a driving state, comprising:
when the situation that a driver is in a vehicle driving state is monitored, acquiring driving state data of the driver; the driving state data comprises electroencephalogram data and/or human face image data;
analyzing the driving state data to obtain an analysis result;
determining whether the driver is in an abnormal driving state according to the analysis result; the abnormal driving state includes a distracted driving state and/or a fatigue driving state.
2. The method of claim 1, wherein the obtaining driving state data of the driver comprises:
acquiring the electroencephalogram data of the driver by utilizing electroencephalogram wearable equipment worn by the driver; and/or the presence of a gas in the gas,
and acquiring the facial image data of the driver by utilizing a camera in the vehicle.
3. The method of claim 1, wherein the driving state data comprises the brain wave data; the brain wave data includes at least one of brain wave signals, facial muscle signals, and eye muscle signals;
the analyzing the driving state data to obtain an analysis result comprises the following steps:
analyzing the brain wave signal to obtain first state information corresponding to the driver; the first state information comprises at least one of concentration, fatigue; and/or the presence of a gas in the gas,
analyzing the facial muscle signals and/or the eye muscle signals to obtain the frequency of facial fatigue behaviors of the driver in a first preset time period.
4. The method of claim 3, wherein said determining whether the driver is in an abnormal driving state based on the analysis result comprises:
if the concentration degree is continuously lower than a first preset threshold value within a second preset time period, determining that the driver is in the distracted driving state; and/or the presence of a gas in the gas,
if the fatigue degree is continuously higher than a second preset threshold value within the second preset time period, determining that the driver is in the fatigue driving state; and/or the presence of a gas in the gas,
and if the frequency of the facial fatigue behaviors in the first preset time period is higher than a third preset threshold, determining that the driver is in the fatigue driving state.
5. The method according to claim 1, wherein the driving state data includes the face image data;
the analyzing the driving state data to obtain an analysis result comprises the following steps:
analyzing the facial image data to obtain the occurrence frequency of abnormal driving behaviors of the driver within a third preset time length;
the determining whether the driver is in an abnormal driving state according to the analysis result includes:
and if the occurrence frequency of the abnormal driving behaviors in the third preset time period is higher than a fourth preset threshold value, determining that the driver is in the fatigue driving state.
6. The method according to claim 1, wherein after determining whether the driver is in an abnormal driving state according to the analysis result, the method further comprises:
if the driver is determined to be in the abnormal driving state, determining the abnormal degree corresponding to the abnormal driving state;
executing corresponding abnormal driving relieving measures for the driver according to the abnormal degree;
wherein the abnormal driving mitigation measures include at least one of:
sending first prompt information for prompting the current abnormal driving state;
controlling appointed equipment in the vehicle to start, wherein the appointed equipment comprises at least one of a vehicle-mounted air conditioner, a vehicle-mounted voice device and a vehicle-mounted fragrance device;
and controlling the steering wheel to vibrate according to a preset frequency.
7. The method according to claim 6, wherein after performing a corresponding abnormal driving mitigation measure for the driver according to the degree of abnormality, the method further comprises:
monitoring whether the abnormal driving state of the driver is eliminated within a fourth preset time period;
if so, stopping executing the abnormal driving relieving measure;
if not, positioning a rest area within a preset range, and sending second prompt information for prompting the driver to go to the rest area.
8. The method of claim 6, wherein after issuing the second prompting message for prompting the driver to travel to the rest area, the method further comprises:
when determining operation of the driver for a target rest area is received, determining geographic position information of the target rest area;
and starting a specified navigation application program, and navigating based on the geographical position information.
9. A driving state detection device, characterized by comprising:
the acquisition module is used for acquiring driving state data of a driver when the driver is monitored to be in a vehicle driving state; the driving state data comprises electroencephalogram data and/or human face image data;
the analysis module is used for analyzing the driving state data to obtain an analysis result;
the first determination module is used for determining whether the driver is in an abnormal driving state or not according to the analysis result; the abnormal driving state includes a distracted driving state and/or a fatigue driving state.
10. A driving state detection apparatus comprising a processor and a memory electrically connected to the processor, the memory storing a computer program, the processor being configured to invoke and execute the computer program from the memory to implement:
when the situation that a driver is in a vehicle driving state is monitored, acquiring driving state data of the driver; the driving state data comprises electroencephalogram data and/or human face image data;
analyzing the driving state data to obtain an analysis result;
determining whether the driver is in an abnormal driving state according to the analysis result; the abnormal driving state includes a distracted driving state and/or a fatigue driving state.
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