CN113907758A - Driver fatigue detection method, device, equipment and storage medium - Google Patents
Driver fatigue detection method, device, equipment and storage medium Download PDFInfo
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Abstract
The application discloses a driver fatigue detection method, a device, equipment and a storage medium, wherein the driver fatigue detection method comprises the steps of acquiring an electroencephalogram signal and a head acceleration signal of a driver in a preset time period; determining a first fatigue value of the driver according to the electroencephalogram signal of the driver; determining a second fatigue value of the driver according to the head acceleration signal of the driver; determining a fatigue level of the driver based on the first fatigue value and the second fatigue value. In this application, can acquire driver's brain electrical signal and head acceleration state through wearable equipment such as intelligence head ring, confirm driver's fatigue level, improve driver fatigue detection's accuracy and reliability and stablize, when the driver is in fatigue state, can remind it through wearable equipment to improve the security that the vehicle travel greatly, avoided the incidence of traffic accident.
Description
Technical Field
The present disclosure relates to the field of fatigue detection technologies, and in particular, to a method, an apparatus, a device, and a storage medium for detecting driver fatigue.
Background
With the rapid development of social economy, the number of automobiles is also rapidly increased, and meanwhile, traffic accidents are gradually increased, wherein the traffic accidents caused by fatigue driving account for about 40 percent. Fatigue driving refers to the phenomenon that after a driver drives for a long time continuously, the mental function and the physiological function are disordered, such as blurred vision, slow response, stiff action, soreness and pain in the waist and back, and reduction of driving ability occur, and the fatigue driving becomes an important factor of traffic accidents and seriously threatens the life and property safety of people. Therefore, the fatigue driving state can be quickly, timely and effectively detected, and the early warning signal is sent to the driver, so that the probability of traffic accidents can be effectively reduced.
Currently, in driver fatigue detection, detection methods based on facial image features of a driver, such as facial expressions, eye behavior changes, mouth state, and the like, are mainly used; the eye features are important features reflecting fatigue states, after a driver enters the fatigue states, the blinking frequency of the driver is reduced, the eye closing time is obviously increased compared with the normal state, the eye opening time is reduced along with the eye opening time, the eye opening degree is also reduced to a certain degree, if the driver enters the deep fatigue states, the serious condition that the eyes of the driver are in a closed state for a long time can occur, so that the facial image features, particularly the eye features, can well reflect the states of the driver, and then, the difference of the faces of the driver is increased, and the method is easily influenced by light, so that the detection result is inaccurate, and the conditions of erroneous judgment and missed judgment are easy to occur.
Therefore, the prior art still needs to be improved.
Disclosure of Invention
In view of the above deficiencies of the prior art, the present application aims to provide a method, an apparatus, a device and a storage medium for detecting driver fatigue, which aim to solve the technical problems of low detection accuracy and easy occurrence of erroneous judgment in driver fatigue detection in the prior art.
In a first aspect, the present application provides a driver fatigue detection method, including:
acquiring an electroencephalogram signal and a head acceleration signal of a driver within a preset time period;
determining a first fatigue value of the driver according to the electroencephalogram signal of the driver;
determining a second fatigue value of the driver according to the head acceleration signal of the driver;
determining a fatigue level of the driver based on the first fatigue value and the second fatigue value.
Optionally, the determining a first fatigue value of the driver according to the electroencephalogram signal of the driver includes:
dividing the electroencephalogram signal of the driver into a plurality of frequency bands according to the frequency range of the electroencephalogram signal of the driver;
extracting relative power of the plurality of frequency bands respectively;
determining the first fatigue value from the relative power.
Optionally, before dividing the electroencephalogram signal of the driver into a plurality of frequency bands according to the frequency range of the electroencephalogram signal of the driver, the method further includes:
removing signals with abnormal amplitude in the electroencephalogram signals of the driver to obtain residual electroencephalogram signals;
standardizing the residual electroencephalogram signals to obtain standardized electroencephalogram signals;
and filtering the standardized electroencephalogram signal, filtering a baseline drift signal in the standardized electroencephalogram signal, and taking the filtered standardized electroencephalogram signal as the electroencephalogram signal.
Optionally, the determining a second fatigue value of the driver according to the head acceleration signal of the driver includes:
determining the head lowering times of the driver in a preset time period according to the head acceleration signal of the driver;
and determining a second fatigue value of the driver according to the head lowering times.
Optionally, before acquiring the electroencephalogram signal and the head acceleration signal of the driver within the predetermined time period, the method further includes:
acquiring environmental parameter information during vehicle running, wherein the environmental parameter information comprises road condition information and vehicle speed information of the vehicle running;
determining the time interval of the preset time period according to the environmental parameter information;
determining a first weight coefficient and a second weight coefficient according to the environment parameter information, wherein the first weight coefficient corresponds to the first fatigue value, and the second weight coefficient corresponds to the second fatigue value;
and determining the fatigue level of the driver according to the first weight coefficient, the first fatigue value, the second weight coefficient and the second fatigue value.
Optionally, the determining the fatigue level of the driver according to the first weight coefficient, the first fatigue value, the second weight coefficient and the second fatigue value includes:
calculating a target fatigue degree of the driver according to the first weight coefficient, the first fatigue value, the second weight coefficient and the second fatigue value;
determining the fatigue grade of the driver according to the target fatigue degree of the driver;
wherein the target fatigue of the driver satisfies the following formula:
p = λ 1 × U1+ λ 2 × U2, where P is the fatigue degree of the driver, λ 1 is a first weight coefficient, U1 is a first fatigue value, λ 2 is a second weight coefficient, and U2 is a second fatigue value.
Optionally, after determining the fatigue level of the driver according to at least the first fatigue value and the second fatigue value, the method further comprises:
and correspondingly reminding according to the fatigue grade of the driver, wherein the reminding mode comprises the following steps: one or more of voice prompt, vibration prompt and light prompt.
In a second aspect, an embodiment of the present application provides a driver fatigue detection apparatus, including:
the acquisition module is used for acquiring electroencephalogram signals and head acceleration signals of a driver within a preset time period;
the first fatigue value determining module is used for determining a first fatigue value of the driver according to the electroencephalogram signal of the driver;
the second fatigue value determining module is used for determining a second fatigue value of the driver according to the head acceleration signal of the driver;
and the fatigue grade determining module is used for determining the fatigue grade of the driver according to the first fatigue value and the second fatigue value.
In a third aspect, an embodiment of the present application provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method for detecting driver fatigue according to any one of the above-mentioned technical solutions is implemented.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for detecting fatigue of a driver according to any one of the above-mentioned technical solutions is implemented.
Has the advantages that: the application provides a driver fatigue detection method, a device, equipment and a storage medium, wherein the driver fatigue detection method comprises the steps of acquiring electroencephalogram signals and head acceleration signals of a driver in a preset time period; determining a first fatigue value of the driver according to the electroencephalogram signal of the driver; determining a second fatigue value of the driver according to the head acceleration signal of the driver; determining a fatigue level of the driver based on the first fatigue value and the second fatigue value. In this application, can acquire driver's brain electrical signal and head acceleration state through wearable equipment such as intelligence head ring, confirm driver's fatigue level, improve driver fatigue detection's accuracy and reliability and stablize, when the driver is in fatigue state, can remind it through wearable equipment to improve the security that the vehicle travel greatly, avoided the incidence of traffic accident.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments or the background art of the present application, the drawings required to be used in the embodiments or the background art of the present application will be described below.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic structural diagram of a method for detecting fatigue of a driver according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a driver fatigue detection apparatus according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, 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.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments. The embodiments of the present application will be described below with reference to the drawings.
In the prior art, in the detection of driver fatigue, the detection method is mainly based on facial image features of the driver, such as facial expressions, eye behavior changes, mouth state and the like; the eye features are important features reflecting fatigue states, after a driver enters the fatigue states, the blinking frequency of the driver is reduced, the eye closing time is obviously increased compared with the normal state, the eye opening time is reduced along with the eye opening time, the eye opening degree is also reduced to a certain degree, if the driver enters the deep fatigue states, the serious condition that the eyes of the driver are in a closed state for a long time can occur, so that the facial image features, particularly the eye features, can well reflect the states of the driver, and then, the difference of the faces of the driver is increased, and the method is easily influenced by light, so that the detection result is inaccurate, and the conditions of erroneous judgment and missed judgment are easy to occur.
Based on this, the embodiment provides some technical schemes to improve the accuracy of driver fatigue detection, reduce the traffic accident that leads to because of fatigue driving. The embodiments of the present application will be described below with reference to the drawings.
The execution subject of the embodiment of the application is a driver fatigue detection device, wherein the driver fatigue detection device can be any electronic device capable of executing the technical scheme disclosed by the embodiment of the application method. Optionally, the driver fatigue detection device may be a wearable smart device such as a smart head ring or a smart band.
It should be understood that the method embodiments of the present application may also be implemented by means of a processor executing computer program code. The embodiments of the present application will be described below with reference to the drawings. Referring to fig. 1, fig. 1 is a schematic flowchart of a method for detecting fatigue of a driver according to an embodiment of the present application, including the following steps:
s100, acquiring electroencephalogram signals and head acceleration signals of a driver in a preset time period;
in one embodiment, the electroencephalogram signal and the head acceleration signal of the driver are acquired through an intelligent head ring, specifically, by arranging a plurality of electrodes and a plurality of acceleration sensors on the intelligent head ring, the electroencephalogram signal can be acquired by using three dry electrodes (including a test electrode, a reference electrode and a grounding electrode), wherein the test electrode acquires a single-channel electroencephalogram signal. Because only a single test electrode can be adopted to collect the original single-channel electroencephalogram signals, the number of the electrodes is greatly reduced, and the intelligent head ring is comfortable and convenient to wear. The head acceleration signal can be collected by a plurality of acceleration sensors, and the acceleration sensors can be arranged on the intelligent head ring and correspond to the forehead position of a driver and are used for collecting the triaxial acceleration data information of the head.
The smart headring may acquire the brain electrical signal and the head acceleration signal of the driver within a predetermined period of time, for example, in one embodiment, the brain electrical signal and the head acceleration signal of the driver may be acquired every 5 seconds. In various embodiments of the present disclosure, the predetermined period of time is not limited to 5 seconds, and may be other time lengths, for example, 10 seconds.
In a preferred embodiment, before step S100, the method further comprises the steps of:
s10, acquiring environment parameter information during vehicle driving, wherein the environment parameter information comprises road condition information and vehicle speed information during vehicle driving;
and S20, determining the time interval of the preset time period according to the environmental parameter information.
Specifically, the environmental parameter information may include, but is not limited to, road condition information and vehicle speed information during the driving process of the vehicle, and the road condition information may be the current light intensity of the vehicle, the road surface environment, the weather condition, and the like.
For example, when the vehicle is running in an accident-prone stage, rainy or snowy weather, dim light, fast speed and the like, a serious traffic accident is prone to be caused, and therefore, the time interval of the preset time period can be set to be short, so that the fatigue detection result of the driver can be obtained in time, and the traffic accident caused by untimely detection can be avoided. When the road condition is good and the vehicle speed is slow, the time interval of the preset time period can be properly increased so as to obtain a more accurate fatigue detection result. That is, the worse the traffic information is, the faster the vehicle speed is, the shorter the corresponding predetermined time period is.
S200, determining a first fatigue value of the driver according to the electroencephalogram signal of the driver;
in a preferred embodiment, before step S200, the method further comprises the steps of:
a1, removing signals with abnormal amplitude in the electroencephalogram signals of the driver to obtain residual electroencephalogram signals;
a2, standardizing the residual electroencephalogram signals to obtain standardized electroencephalogram signals;
and A3, filtering the standardized electroencephalogram signal, filtering a baseline drift signal in the standardized electroencephalogram signal, and taking the filtered standardized electroencephalogram signal as the electroencephalogram signal.
Specifically, for the electroencephalogram signal acquired in step S100, a signal with an amplitude abnormality exceeding 400 μ V in amplitude may be directly removed. The remaining signal may be normalized to produce a normalized signal, which includes removing the dc component and unitizing the standard deviation. The normalized signal may then be smoothly filtered to filter out baseline wander signals in the normalized signal having frequencies below 0.3 Hz.
In one embodiment, the step S200: according to the electroencephalogram signal of the driver, determining a first fatigue value of the driver comprises the following steps:
s210, dividing the electroencephalogram signal of the driver into a plurality of frequency bands according to the frequency range of the electroencephalogram signal of the driver;
in one embodiment, a frequency range of a mid-high frequency brain electrical signal in the brain electrical signal is divided into a plurality of frequency bands. The frequency range of the middle-high frequency electroencephalogram signals is 5-30 Hz, and therefore the electroencephalogram signals within the frequency range of 5-30 Hz can be used for detecting fatigue. The frequency bands of 5-30 Hz can be divided by a predetermined step (for example, an integer between 1-5). For example, when the predetermined step size is 1Hz, each frequency value corresponds to one frequency band, and there are 26 frequency bands, in the subsequent steps, the relative powers of these frequency bands (the ratio of the absolute power of each frequency band to the absolute power of the frequency band of 5-30 Hz) can be calculated by fast fourier transform, and then the first-order difference features of the respective relative powers can be calculated, so that a 52-dimensional feature vector can be obtained in total. When the preset step length is 5Hz, the frequency band of 5-30 Hz can be divided into six frequency bands: 5-9 Hz, 10-14 Hz, 15-19 Hz, 20-24 Hz and 25-30 Hz, in the subsequent steps, the relative power of the frequency bands (the ratio of the absolute power of each frequency band to the absolute power of the frequency band of 5-30 Hz) can be calculated through fast Fourier transform, then the first-order difference characteristic of each relative power can be calculated, and 10-dimensional characteristic vectors can be obtained in total. In addition, 5-30 Hz frequency bands can be divided according to common frequency bands, namely theta (5-8 Hz), alpha1 (9-10 Hz), alpha2 (11-13 Hz), beta1 (14-18 Hz), beta2 (19-24 Hz), and beta3 (25-30 Hz), in the subsequent steps, the relative power of the frequency bands (namely the ratio of the absolute power of the corresponding frequency band to the absolute power of the 5-30 Hz frequency band) can be calculated through fast Fourier transform, then the first-order difference characteristic of each relative power can be calculated, and the 12-dimensional characteristic vector can be obtained in total. Because each relative power is a numerical value between 0 and 1, the difference between different individuals can be avoided, and the accuracy of fatigue detection is greatly improved.
S220, extracting the relative power of the plurality of frequency bands respectively;
the relative power is a ratio of the absolute power of each frequency band divided in step S210 to the absolute power of the entire frequency band (e.g., 5 to 30Hz frequency band). For example, when the frequency band of 5 to 30Hz is divided into the following six frequency bands according to the common frequency band in step S210: theta (5-8 Hz), alpha1 (9-10 Hz), alpha2 (11-13 Hz), beta1 (14-18 Hz), beta2 (19-24 Hz), and beta3 (25-30 Hz), and the relative power of the frequency bands (namely the ratio of the absolute power of the corresponding frequency band to the absolute power of the frequency band of 5-30 Hz) is calculated through fast Fourier transform, so that six relative frequencies can be obtained and used as the static fatigue characteristic of the 10-second electroencephalogram signal. The relative power of the medium-high frequency electroencephalogram signals of different human bodies is calculated, and the fatigue detection is not directly carried out by using the absolute power of the medium-high frequency electroencephalogram signals, so that the difference among individuals can be avoided.
And S230, determining the first fatigue value according to the relative power.
And after the relative powers of a plurality of frequency bands are obtained, extracting a first-order difference dynamic characteristic for representing the variation trend of the relative powers. For example, a first-order difference dynamic feature for representing the variation trend of the static fatigue feature may be calculated, and then a principal component analysis method is used to perform dimension reduction on a feature vector formed by the relative power and the first-order difference dynamic feature, and feature selection is performed on the feature vector after feature dimension reduction. For example, feature selection may be performed on the feature vector by using a linear discriminant analysis method to select a feature component related to fatigue, and the feature component unrelated to fatigue is removed to finally obtain a two-dimensional feature vector, and the fatigue level may be determined according to the two-dimensional feature vector after fatigue feature selection. In one embodiment of the present disclosure, the first fatigue level is then determined by a statistical model.
S300, determining a second fatigue value of the driver according to the head acceleration signal of the driver;
in one embodiment, determining the second fatigue value of the driver based on the head acceleration signal of the driver comprises the steps of:
s310, determining the head lowering times of the driver in a preset time period according to the head acceleration signal of the driver;
since the head of the driver is prone to intermittent head lowering in the fatigue state, in the embodiment, the change condition of the head acceleration data is mainly obtained through the acceleration sensor, and the head lowering frequency of the driver in the preset time is determined.
And S320, determining a second fatigue value of the driver according to the head lowering times.
Specifically, the number of times of head lowering within the predetermined time may reflect the fatigue degree of the driver, the number of times of head lowering within the predetermined time of the driver corresponds to a different second fatigue value, and the more the number of times of head lowering within the predetermined time of the driver is, the larger the second fatigue value is, and the more the fatigue degree of the driver is reflected.
And S400, determining the fatigue grade of the driver according to the first fatigue value and the second fatigue value.
Specifically, the fatigue grades may be light fatigue, moderate fatigue and heavy fatigue, the light fatigue, the heavy fatigue and the heavy fatigue respectively correspond to different threshold ranges, after the first fatigue value and the second fatigue value are obtained, a target fatigue value may be obtained by combining the first fatigue value and the second fatigue value, and then the fatigue grade of the driver is determined according to the threshold range in which the target fatigue value is located.
In a preferred embodiment, said determining a fatigue level of said driver based on at least said first fatigue value and said second fatigue value comprises the steps of:
s410, determining a first weight coefficient and a second weight coefficient according to the environment parameter information, wherein the first weight coefficient corresponds to the first fatigue value, and the second weight coefficient corresponds to the second fatigue value;
the first weight coefficient and the second weight coefficient are closely related to the environmental parameter information, specifically, when the external driving environment is better and the vehicle speed is slower, the numerical values of the first weight coefficient and the second weight coefficient are smaller, namely the final obtained first fatigue value is smaller; when the external driving environment is worse and the vehicle speed is faster, the values of the first weight coefficient and the second weight coefficient are larger, that is, the value of the finally obtained second fatigue value is larger.
And S420, determining the fatigue level of the driver according to the first weight coefficient, the first fatigue value, the second weight coefficient and the second fatigue value.
The fatigue grade of the driver determined by the first weight coefficient, the first fatigue value, the second weight coefficient and the second fatigue value is combined with a brain signal and an acceleration signal of the driver to obtain an accurate result with high accuracy, and the time interval of a preset time period can be reduced under the relatively dangerous condition that the external driving environment is worse and the vehicle speed is faster according to different driving environments and driving vehicle speeds, so that the fatigue detection result of the driver can be obtained in time, and traffic accidents caused by untimely detection are avoided; the larger the values of the first weight coefficient and the second weight coefficient are increased, the higher the fatigue level is, so that the driver can pay more attention to the fatigue level, and the traffic accidents caused by fatigue driving can be further reduced. Under the relative safety conditions of good external driving environment, good vehicle speed and the like, the time interval of a preset time period can be increased, so that the fatigue detection result of the driver can be more accurately obtained, and the subsequent driver can more accurately adjust the fatigue according to the fatigue detection result; and the numerical values of the first weight coefficient and the second weight coefficient are reduced, so that the finally obtained fatigue grade is relatively low, the misjudgment caused by fatigue detection is avoided, and unnecessary panic is brought to the driver.
In a preferred embodiment, said determining the fatigue level of the driver based on the first weight coefficient, the first fatigue value, the second weight coefficient, the second fatigue value comprises the steps of:
b1, calculating a target fatigue degree of the driver according to the first weight coefficient, the first fatigue value, the second weight coefficient and the second fatigue value; wherein the target fatigue of the driver satisfies the following formula:
p = λ 1 × U1+ λ 2 × U2, where P is the fatigue level of the driver, λ 1 is a first weight coefficient, U1 is a first fatigue value, λ 2 is a second weight coefficient, and U2 is a second fatigue value;
specifically, the intelligent head ring can obtain the target fatigue degree according to a first weight coefficient, a first fatigue value, a second weight coefficient and a second fatigue value, and the first weight coefficient, the first fatigue value, the second weight coefficient and the second fatigue value are respectively substituted into a formula: p = λ 1 × U1+ λ 2 × U2, resulting in the target fatigue. It should be noted that λ 1 and λ 2 in the embodiment of the present application are related to environmental parameter information during vehicle driving, that is, in a case where a driving environment is relatively dangerous, λ 1 and λ 2 are relatively large, and in a case where the driving environment is relatively safe, λ 1 and λ 2 are relatively small.
And B2, determining the fatigue level of the driver according to the target fatigue of the driver.
The fatigue grades can be light fatigue, moderate fatigue and severe fatigue, the light fatigue, the severe fatigue and the severe fatigue respectively correspond to different threshold value ranges, and after the target fatigue value is obtained, the fatigue grade of the driver is determined according to the threshold value range where the target fatigue value is located.
After said determining the fatigue level of the driver from the first fatigue value and the second fatigue value, the method further comprises:
s500, carrying out corresponding reminding according to the fatigue grade of the driver, wherein the reminding mode comprises the following steps: one or more of voice prompt, vibration prompt and light prompt.
Specifically, after the fatigue level of the driver is determined, the driver can be reminded through but not limited to voice reminding, vibration reminding, light reminding and the like.
For example, in one embodiment, when the driver is in a mild fatigue state, the smart headring may alert the driver to adjust the mental state by voice; when the driver is in moderate fatigue state, the intelligence head ring can remind through the light of certain frequency or high-frequency warning sound, when the driver is in severe fatigue state, the intelligence head ring can remind through vibrations mode to remind the driver to stop the rest through voice mode, avoid appearing the traffic accident.
It will be understood by those skilled in the art that in the method of the present invention, the order of writing the steps does not imply a strict order of execution and any limitations on the implementation, and the specific order of execution of the steps should be determined by their function and possible inherent logic.
Based on the same inventive concept, an embodiment of the present application further provides a device for detecting fatigue of a driver, please refer to fig. 2, and fig. 2 is a schematic structural diagram of the device for detecting fatigue of a driver provided in the embodiment of the present application, and the device for detecting fatigue of a driver includes: the device comprises an acquisition module, a first fatigue value determination module, a second fatigue value determination module and a fatigue grade determination module, wherein:
the acquisition module is used for acquiring electroencephalogram signals and head acceleration signals of a driver within a preset time period;
the first fatigue value determining module is used for determining a first fatigue value of the driver according to the electroencephalogram signal of the driver;
the second fatigue value determining module is used for determining a second fatigue value of the driver according to the head acceleration signal of the driver;
and the fatigue grade determining module is used for determining the fatigue grade of the driver according to the first fatigue value and the second fatigue value.
In combination with any embodiment of the present application, the first fatigue value determining module is further configured to:
removing signals with abnormal amplitude in the electroencephalogram signals of the driver to obtain residual electroencephalogram signals;
standardizing the residual electroencephalogram signals to obtain standardized electroencephalogram signals;
filtering the standardized electroencephalogram signal, filtering a baseline drift signal in the standardized electroencephalogram signal, and taking the filtered standardized electroencephalogram signal as an electroencephalogram signal;
dividing the electroencephalogram signal of the driver into a plurality of frequency bands according to the frequency range of the electroencephalogram signal of the driver;
extracting relative power of the plurality of frequency bands respectively;
determining the first fatigue value from the relative power.
In combination with any embodiment of the present application, the second fatigue value determining module is further configured to:
determining the head lowering times of the driver in a preset time period according to the head acceleration signal of the driver;
and determining a second fatigue value of the driver according to the head lowering times.
In combination with any embodiment of the present application, the obtaining module is further configured to:
acquiring environmental parameter information during vehicle running, wherein the environmental parameter information comprises road condition information and vehicle speed information of the vehicle running; determining the time interval of the preset time period according to the environmental parameter information;
the fatigue level determination module is further configured to: determining a first weight coefficient and a second weight coefficient according to the environment parameter information, wherein the first weight coefficient corresponds to the first fatigue value, and the second weight coefficient corresponds to the second fatigue value;
and determining the fatigue level of the driver according to the first weight coefficient, the first fatigue value, the second weight coefficient and the second fatigue value.
In combination with any one of the embodiments of the present application, the driver fatigue detection apparatus further includes:
the reminding module is used for carrying out corresponding reminding according to the fatigue grade of the driver, and the reminding mode comprises the following steps: one or more of voice prompt, vibration prompt and light prompt.
In some embodiments, the functions of the apparatus provided in the embodiments of the present application or the modules included in the apparatus may be used to execute the method described in the above method embodiments, and for specific implementation, reference may be made to the description of the above method embodiments, and for brevity, no further description is given here.
In the preferred embodiment of the present application, the driver fatigue detection device further comprises a processor, a memory, an input device, and an output device. The processor, the memory, the input device and the output device are coupled through a connector, which includes various interfaces, transmission lines or buses, etc., and the embodiment of the present application is not limited thereto. It should be appreciated that in various embodiments of the present application, coupled refers to being interconnected in a particular manner, including being directly connected or indirectly connected through other devices, such as through various interfaces, transmission lines, buses, and the like.
The processor may be one or more Graphics Processing Units (GPUs), and in the case of one GPU, the GPU may be a single-core GPU or a multi-core GPU. Alternatively, the processor may be a processor group consisting of a plurality of GPUs, and the plurality of processors are coupled to each other through one or more buses. Alternatively, the processor may be other types of processors, and the like, and the embodiments of the present application are not limited.
The memory can be used to store computer program instructions and various types of computer program code for executing the program code of aspects of the present application. Alternatively, the memory includes, but is not limited to, Random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM), which is used for associated instructions and data.
The input means are for inputting data and/or signals and the output means are for outputting data and/or signals. The input device and the output device may be separate devices or may be an integral device.
It is understood that, in the embodiment of the present application, the memory may be used to store not only the relevant instructions, but also relevant data, for example, the memory may be used to store data acquired through the input device, or the memory may be used to store comparison results obtained through the processor, and the like, and the embodiment of the present application is not limited to the data specifically stored in the memory.
It is understood that in practical applications, the driver fatigue detection device may also include other necessary elements, including but not limited to any number of input/output devices, processors, memories, etc., respectively, and all driver fatigue detection devices that may implement the embodiments of the present application are within the scope of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It is also clear to those skilled in the art that the descriptions of the various embodiments of the present application have different emphasis, and for convenience and brevity of description, the same or similar parts may not be repeated in different embodiments, so that the parts that are not described or not described in detail in a certain embodiment may refer to the descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in or transmitted over a computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)), or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., Digital Versatile Disk (DVD)), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
In summary, the present application provides a method, an apparatus, a device and a storage medium for detecting driver fatigue, wherein the method for detecting driver fatigue comprises obtaining an electroencephalogram signal and a head acceleration signal of a driver within a predetermined time period; determining a first fatigue value of the driver according to the electroencephalogram signal of the driver; determining a second fatigue value of the driver according to the head acceleration signal of the driver; determining a level of fatigue for the driver based at least on the first and second fatigue values. According to the method and the device, the electroencephalogram signal and the head acceleration state of the driver can be acquired through wearable equipment such as the intelligent head ring, so that the fatigue grade of the driver is determined, the accuracy and the stability and the reliability of fatigue detection of the driver are improved, and when the driver is in the fatigue state, the driver can be reminded through the wearable equipment, so that the driving safety of a vehicle is greatly improved, and the occurrence rate of traffic accidents is avoided; the time interval of a preset time period can be reduced under the relatively dangerous condition that the external driving environment is worse and the vehicle speed is faster according to different driving environments and driving vehicle speeds, so that the fatigue detection result of a driver can be obtained in time, and traffic accidents caused by untimely detection are avoided; the larger the values of the first weight coefficient and the second weight coefficient are increased, the higher the fatigue level is, so that the driver can pay more attention to the fatigue level, and the traffic accidents caused by fatigue driving can be further reduced. Under the relative safety conditions of good external driving environment, good vehicle speed and the like, the time interval of a preset time period can be increased, so that the fatigue detection result of the driver can be more accurately obtained, and the subsequent driver can more accurately adjust the fatigue according to the fatigue detection result; and the numerical values of the first weight coefficient and the second weight coefficient are reduced, so that the finally obtained fatigue grade is relatively low, the misjudgment caused by fatigue detection is avoided, and unnecessary panic is brought to the driver.
The technical principle of the present invention is described above in connection with specific embodiments. The description is made for the purpose of illustrating the principles of the invention and should not be construed in any way as limiting the scope of the invention. Based on the explanations herein, those skilled in the art will be able to conceive of other embodiments of the present invention without inventive effort, which would fall within the scope of the present invention.
Claims (10)
1. A driver fatigue detection method, characterized by comprising:
acquiring an electroencephalogram signal and a head acceleration signal of a driver within a preset time period;
determining a first fatigue value of the driver according to the electroencephalogram signal of the driver;
determining a second fatigue value of the driver according to the head acceleration signal of the driver;
determining a fatigue level of the driver based on the first fatigue value and the second fatigue value.
2. The detection method according to claim 1, wherein said determining a first fatigue value of said driver from said electroencephalogram signal of said driver comprises the steps of:
dividing the electroencephalogram signal of the driver into a plurality of frequency bands according to the frequency range of the electroencephalogram signal of the driver;
extracting relative power of the plurality of frequency bands respectively;
determining the first fatigue value from the relative power.
3. The detection method according to claim 2, wherein before dividing the driver's electroencephalogram signal into a plurality of frequency bands according to the frequency range thereof, the determining the first fatigue value of the driver according to the driver's electroencephalogram signal further comprises:
removing signals with abnormal amplitude in the electroencephalogram signals of the driver to obtain residual electroencephalogram signals;
standardizing the residual electroencephalogram signals to obtain standardized electroencephalogram signals;
and filtering the standardized electroencephalogram signal, filtering a baseline drift signal in the standardized electroencephalogram signal, and taking the filtered standardized electroencephalogram signal as the electroencephalogram signal.
4. Detection method according to claim 1, wherein said determining a second fatigue value of said driver from said head acceleration signal of said driver comprises the steps of:
determining the head lowering times of the driver in a preset time period according to the head acceleration signal of the driver;
and determining a second fatigue value of the driver according to the head lowering times.
5. The detection method according to claim 1, further comprising:
acquiring environmental parameter information during vehicle running, wherein the environmental parameter information comprises road condition information and vehicle speed information of the vehicle running;
determining the time interval of the preset time period according to the environmental parameter information;
determining a first weight coefficient and a second weight coefficient according to the environment parameter information, wherein the first weight coefficient corresponds to the first fatigue value, and the second weight coefficient corresponds to the second fatigue value;
and determining the fatigue level of the driver according to the first weight coefficient, the first fatigue value, the second weight coefficient and the second fatigue value.
6. The detection method according to claim 5, wherein said determining the fatigue level of the driver based on the first weight coefficient, the first fatigue value, the second weight coefficient, the second fatigue value comprises the steps of:
calculating a target fatigue degree of the driver according to the first weight coefficient, the first fatigue value, the second weight coefficient and the second fatigue value;
determining the fatigue grade of the driver according to the target fatigue degree of the driver;
wherein the target fatigue of the driver satisfies the following formula:
p = λ 1 × U1+ λ 2 × U2, where P is the fatigue degree of the driver, λ 1 is a first weight coefficient, U1 is a first fatigue value, λ 2 is a second weight coefficient, and U2 is a second fatigue value.
7. The detection method according to any one of claims 1 to 6, characterized in that the method further comprises:
and correspondingly reminding according to the fatigue grade of the driver, wherein the reminding mode comprises the following steps: one or more of voice prompt, vibration prompt and light prompt.
8. A driver fatigue detecting device, characterized by comprising:
the acquisition module is used for acquiring electroencephalogram signals and head acceleration signals of a driver within a preset time period;
the first fatigue value determining module is used for determining a first fatigue value of the driver according to the electroencephalogram signal of the driver;
the second fatigue value determining module is used for determining a second fatigue value of the driver according to the head acceleration signal of the driver;
and the fatigue grade determining module is used for determining the fatigue grade of the driver at least according to the first fatigue value and the second fatigue value.
9. A computer arrangement comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the driver fatigue detection method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out a driver fatigue detection method according to any one of claims 1 to 7.
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