CN114209325A - Driver fatigue behavior monitoring method, device, equipment and storage medium - Google Patents
Driver fatigue behavior monitoring method, device, equipment and storage medium Download PDFInfo
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/18—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state for vehicle drivers or machine operators
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/168—Evaluating attention deficit, hyperactivity
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
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- G06F18/00—Pattern recognition
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- H04W—WIRELESS COMMUNICATION NETWORKS
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- A61B2503/00—Evaluating a particular growth phase or type of persons or animals
- A61B2503/20—Workers
- A61B2503/22—Motor vehicles operators, e.g. drivers, pilots, captains
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Abstract
The invention relates to the technical field of intelligent driving, and discloses a method, a device, equipment and a storage medium for monitoring fatigue behaviors of a driver. The method comprises the following steps: monitoring the interference of the current action of the driver on the wireless signal to obtain channel state information; carrying out feature extraction on the channel state information to obtain signal features; reducing the dimension of the signal characteristics to obtain target characteristics; inputting the target characteristics into a pre-trained target fatigue behavior recognition model to obtain a classification result output by the target fatigue behavior recognition model; and judging whether the current action of the driver is the fatigue driving action according to the classification result to obtain a monitoring result of the fatigue behavior of the driver. Through the mode, the action of the driver is monitored by combining wireless perception, and whether the action of the driver is the fatigue driving action is identified by using the trained fatigue behavior identification model, so that the fatigue driving is monitored, the occurrence of the fatigue driving is reduced, and the occurrence rate of road traffic safety accidents is reduced.
Description
Technical Field
The invention relates to the technical field of intelligent driving, in particular to a method, a device, equipment and a storage medium for monitoring fatigue behavior of a driver.
Background
At present, fatigue driving of a driver is one of the most major factors causing road traffic safety accidents. When a driver is in a fatigue state to drive, phenomena of gear shifting, untimely braking and the like can occur slightly, phenomena of slow action, forgetting operation and the like can occur seriously, even a transient sleep phenomenon can occur more seriously, the control capability of the vehicle is lost, and a series of safety accidents are caused. Therefore, the occurrence of fatigue driving can be reduced, and the incidence rate of road traffic safety accidents can be effectively reduced.
The method for monitoring the fatigue of the driver comprises two methods, wherein the first method is to monitor the fatigue of the driver by utilizing image processing analysis, and judge whether the driver is in a fatigue state or not by analyzing the actions of the driver during driving, specifically according to the blinking frequency, nodding frequency, yawning and other actions of the driver, the common fatigue driving occurs at night, and the recognition rate of the image-based fatigue driving monitoring method is greatly reduced due to insufficient illumination intensity during driving at night, so that the method has the characteristic of insufficient universality. The second way is by analyzing driver physiological signals such as: whether the driver is in a fatigue state is judged according to changes of signals such as blood pressure, heart rate and pulse, the driver is required to carry equipment comprising various sensors for acquiring physiological data of the driver in real time, and the driving feeling of the driver can be influenced or the driving operation of the driver is interfered in such a way, so that unnecessary accidents are caused.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a method, a device, equipment and a storage medium for monitoring fatigue behaviors of drivers, and aims to solve the technical problem of reducing the occurrence rate of road traffic safety accidents by reducing the occurrence rate of fatigue driving.
In order to achieve the above object, the present invention provides a method for monitoring fatigue behavior of a driver, comprising the steps of:
monitoring the interference of the current action of the driver on the wireless signal to obtain channel state information;
extracting the characteristics of the channel state information to obtain signal characteristics;
reducing the dimension of the signal characteristic to obtain a target characteristic;
inputting the target characteristics into a pre-trained target fatigue behavior recognition model to obtain a classification result output by the target fatigue behavior recognition model;
and judging whether the current action of the driver is a fatigue driving action according to the classification result to obtain a fatigue behavior monitoring result of the driver.
Optionally, the performing feature extraction on the channel state information to obtain a signal feature includes:
carrying out wavelet packet decomposition on the channel state information, and extracting wavelet packet energy characteristics and wavelet packet coefficient statistical characteristics;
and splicing the wavelet packet energy characteristics and the wavelet packet coefficient statistical characteristics to obtain signal characteristics.
Optionally, the performing wavelet packet decomposition on the channel state information, and extracting wavelet packet energy characteristics and wavelet packet coefficient statistical characteristics includes:
performing multi-layer wavelet packet decomposition on the channel state information to obtain a wavelet packet coefficient;
forming wavelet packet coefficient statistical characteristics according to the mean value, the standard deviation, the variance and the maximum value corresponding to the wavelet packet coefficients;
determining the signal energy ratio corresponding to each sub-frequency band;
determining the wavelet packet energy vector of each layer according to the signal energy ratio;
and forming wavelet packet energy characteristics according to the wavelet packet energy vectors of all layers.
Optionally, the performing dimension reduction on the signal feature to obtain a target feature includes:
carrying out standardization processing on the signal characteristics to obtain a standardized sample matrix;
calculating a covariance matrix corresponding to the standardized sample matrix;
determining corresponding eigenvalue and eigenvector according to the covariance matrix;
sorting the eigenvectors according to the eigenvalues to obtain an eigenvector matrix;
and extracting a plurality of eigenvectors from the characteristic matrix according to a preset accumulated contribution rate to obtain target characteristics.
Optionally, before the interference of the current action of the driver on the wireless signal is monitored and the channel state information is obtained, the method further includes:
monitoring the interference of various fatigue driving actions of a driver on a wireless signal to obtain a plurality of sample channel state information;
respectively extracting the characteristics of each sample channel state information to obtain corresponding sample signal characteristics;
reducing the dimension of each sample signal characteristic to obtain a corresponding target sample characteristic;
constructing a training set and a verification set according to the characteristics of the target samples and the corresponding fatigue action labels;
training an initial fatigue behavior recognition model according to the training set to obtain a trained fatigue behavior recognition model;
verifying the trained fatigue behavior recognition model according to the verification set;
and when the verification is passed, obtaining a trained target fatigue behavior recognition model.
Optionally, the verifying the trained fatigue behavior recognition model according to the verification set includes:
identifying the characteristics of each target sample in the verification set according to the trained fatigue behavior identification model to obtain an identification result corresponding to each target sample characteristic in the verification set;
determining a corresponding recognition rate according to the recognition result corresponding to each target sample feature in the verification set;
and determining whether the trained fatigue behavior recognition model passes the verification according to the recognition rate, wherein when the recognition rate is greater than a preset threshold value, the trained fatigue behavior recognition model is judged to pass the verification.
Optionally, after determining whether the current action of the driver is a fatigue driving action according to the classification result and obtaining a monitoring result of the fatigue behavior of the driver, the method further includes:
when the current action of the driver is a fatigue driving action, determining the occurrence frequency of the fatigue driving action in a preset time period;
and when the occurrence frequency is greater than the preset frequency, reminding the driver.
In addition, in order to achieve the above object, the present invention also provides a driver fatigue behavior monitoring device, including:
the acquisition module is used for monitoring the interference of the current action of the driver on the wireless signal to obtain channel state information;
the characteristic extraction module is used for extracting the characteristics of the channel state information to obtain signal characteristics;
the dimension reduction module is used for reducing the dimension of the signal characteristics to obtain target characteristics;
the classification module is used for inputting the target characteristics into a pre-trained target fatigue behavior recognition model to obtain a classification result output by the target fatigue behavior recognition model;
and the judging module is used for judging whether the current action of the driver is the fatigue driving action according to the classification result to obtain the monitoring result of the fatigue behavior of the driver.
In addition, to achieve the above object, the present invention also provides a driver fatigue behavior monitoring apparatus, including: a memory, a processor and a driver fatigue behavior monitoring program stored on the memory and executable on the processor, the driver fatigue behavior monitoring program being configured to implement the driver fatigue behavior monitoring method as described above.
In addition, to achieve the above object, the present invention further provides a storage medium having a driver fatigue behavior monitoring program stored thereon, wherein the driver fatigue behavior monitoring program, when executed by a processor, implements the driver fatigue behavior monitoring method as described above.
The method comprises the steps of monitoring the interference of the current action of a driver on a wireless signal to obtain channel state information; carrying out feature extraction on the channel state information to obtain signal features; reducing the dimension of the signal characteristics to obtain target characteristics; inputting the target characteristics into a pre-trained target fatigue behavior recognition model to obtain a classification result output by the target fatigue behavior recognition model; and judging whether the current action of the driver is the fatigue driving action according to the classification result to obtain a monitoring result of the fatigue behavior of the driver. By the mode, the action of the driver is monitored by combining wireless perception, whether the action of the driver is the fatigue driving action is identified by utilizing the trained fatigue behavior identification model, the fatigue driving is monitored, the occurrence of the fatigue driving is reduced, the occurrence rate of road traffic safety accidents is reduced, and the fatigue driving is monitored relative to the acquired image; compared with a mode of analyzing the physiological signals of the driver, the method and the device do not need to be provided with signal acquisition equipment, and do not influence the driving experience and operation of the driver.
Drawings
FIG. 1 is a schematic structural diagram of a driver fatigue behavior monitoring device for a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a first exemplary embodiment of a method for monitoring fatigue behavior of a driver according to the present invention;
FIG. 3 is a flowchart illustrating a second embodiment of a method for monitoring fatigue behavior of a driver according to the present invention;
FIG. 4 is a flowchart illustrating a third exemplary embodiment of a method for monitoring fatigue behavior of a driver according to the present invention;
fig. 5 is a block diagram showing the structure of the first embodiment of the driver fatigue behavior monitoring apparatus according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a device for monitoring fatigue behavior of a driver in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the driver fatigue behavior monitoring apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the driver fatigue behavior monitoring device, and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a driver fatigue behavior monitoring program.
In the driver fatigue behavior monitoring apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the driver fatigue behavior monitoring device of the present invention may be disposed in the driver fatigue behavior monitoring device, and the driver fatigue behavior monitoring device calls the driver fatigue behavior monitoring program stored in the memory 1005 through the processor 1001 and executes the driver fatigue behavior monitoring method provided by the embodiment of the present invention.
An embodiment of the present invention provides a method for monitoring a fatigue behavior of a driver, and referring to fig. 2, fig. 2 is a schematic flow diagram of a first embodiment of the method for monitoring a fatigue behavior of a driver according to the present invention.
In this embodiment, the method for monitoring fatigue behavior of a driver includes the following steps:
step S10: and monitoring the interference of the current action of the driver on the wireless signal to obtain the channel state information.
It can be understood that the execution subject of the embodiment is a driver fatigue behavior monitoring device, the driver fatigue behavior monitoring device may be a vehicle machine, and may also be a controller disposed on a vehicle, and the embodiment takes the vehicle machine as an example for description.
It should be noted that, in this embodiment, a Channel State Information (CSI) data acquisition environment is arranged on a vehicle equipped with a Ubuntu system, and an external Intel5300 network card is used as a transceiver end of a wireless signal, where a transmitter end of the wireless signal employs 1 antenna, a receiver end of the wireless signal employs 3 antennas, and a working frequency band selects a 2.4GHz frequency band in which signals are less interfered. The method includes the steps that a Channel Frequency Response (CFR) is read as a CSI signal through a preset tool installed on a vehicle machine, and changes of signal amplitude and phase of subcarriers are mainly reflected, specifically, the preset tool may be a Linux 802.11n CSI tool, and the CFR is read as the CSI signal by using equipment conforming to Orthogonal Frequency Division Multiplexing (OFDM) in the preset tool. In a specific implementation, the channel model establishment is represented by equation (1):
wherein
Which represents the received vector, is,
which represents the transmission vector, is transmitted,
representing noise and H representing frequency response.
The channel frequency response can be specifically expressed by equation (2):
wherein N isTFor the number of transmitting antennas, NRFor the number of receive antennas, i ∈ [1, NT]，j∈[1，NR]，k∈[1，30]，hijkIndicating the CSI value of the kth subcarrier in the data stream between the ith transmit antenna to the jth receive antenna.
Wherein h isijkIn particular toIt can also be expressed by equation (3):
wherein, | hijkI represents the amplitude response and is hijkRepresenting the phase response, the amplitude response and the phase response of the signal can be resolved from the CSI information by equation (3).
It should be understood that when a wireless signal propagates in a channel when it is transmitted from a transmitting end, the channel is unstable, and changes such as diffraction, refraction, scattering and the like occur during transmission, and the changes are reflected in the fluctuation of a signal at a receiving end. Changes of the action of the driver to the environment are reflected in the waveforms, channel state information is monitored through a channel model represented by the formula (1), the channel state information is analyzed, whether the driver takes fatigue action or not is determined, and therefore whether the driver is in a fatigue driving state or not is determined.
Step S20: and extracting the characteristics of the channel state information to obtain the signal characteristics.
In the present embodiment, the feature information is extracted from the channel state information to generate the feature matrix. In the specific implementation, the wavelet packet decomposition is carried out on the acquired channel state information, the wavelet packet energy characteristic and the wavelet packet coefficient statistical characteristic are extracted, and the interference of noise on an original signal can be effectively avoided while the signal characteristic is extracted.
Step S30: and reducing the dimension of the signal characteristic to obtain a target characteristic.
It should be understood that, in the embodiment, the dimension of the signal feature may be reduced in a linear dimension reduction manner, and the dimension of the signal feature may also be reduced in a nonlinear dimension reduction manner. By reducing the dimension of the signal characteristics, the complexity of the characteristics is reduced, noises mixed in the data are removed, and effective characteristic data are provided for model identification.
Specifically, the step S30 includes: carrying out standardization processing on the signal characteristics to obtain a standardized sample matrix; calculating a covariance matrix corresponding to the standardized sample matrix; determining corresponding eigenvalue and eigenvector according to the covariance matrix; sorting the eigenvectors according to the eigenvalues to obtain an eigenvector matrix; and extracting a plurality of eigenvectors from the characteristic matrix according to a preset accumulated contribution rate to obtain target characteristics.
It is to be noted that, assuming that the signal features are represented by a feature matrix P of i rows and j columns, the signal features P are normalized to obtain a normalized sample matrix R, and specifically, the normalization is performed by equation (4):
wherein, i is 1, 2.. times, m; j is 1, 2.
Calculating a covariance matrix corresponding to the normalized sample matrix by formula (5):
and determining an eigen equation corresponding to the covariance matrix, and determining m eigenvalues by the eigen equation. The characteristic equation is expressed by equation (6):
|C-λIm|＝0(6)
according to the calculated characteristic value lambdai(i ═ 1, 2.. times, m), a unit feature vector P can be obtained1，P2，...，Pk. And determining the dimensionality reduction degree by the principal component according to the accumulated contribution rate, and extracting a plurality of eigenvectors from the characteristic matrix to realize dimensionality reduction of the signal characteristics.
Step S40: and inputting the target characteristics into a pre-trained target fatigue behavior recognition model to obtain a classification result output by the target fatigue behavior recognition model.
It should be understood that, in the embodiment, a pre-trained target fatigue behavior recognition model is deployed on the vehicle-mounted device, and classification recognition is performed according to the target fatigue behavior recognition model based on a target feature corresponding to a current action to determine a classification result. Specifically, the target fatigue behavior recognition model is a model obtained by training according to a large number of fatigue behavior sample characteristics.
Step S50: and judging whether the current action of the driver is a fatigue driving action according to the classification result to obtain a fatigue behavior monitoring result of the driver.
It should be noted that the classification result is a tag corresponding to the fatigue driving action or a tag corresponding to another action, and when the classification result is a tag corresponding to the fatigue driving action, it is determined that the current action of the driver is the fatigue driving action.
Further, after the step S50, the method further includes: when the current action of the driver is a fatigue driving action, determining the occurrence frequency of the fatigue driving action in a preset time period; and when the occurrence frequency is greater than the preset frequency, reminding the driver.
It should be understood that, in this embodiment, a target fatigue behavior recognition model is trained in advance, the model is deployed on a vehicle, an environment for data acquisition is arranged on the vehicle, two Intel5300 network cards are assembled in front of a driver as a transceiving end during data acquisition, amplitude change of a CSI signal caused when the driver acts currently is monitored, CSI is subjected to feature extraction and dimension reduction, and is input into the target fatigue behavior recognition model for classification, and when it is determined that the driver continues to have fatigue-reflecting actions for a preset number of times or more in a preset time period, the control system prompts the driver to rest.
In the embodiment, the channel state information is obtained by monitoring the interference of the current action of the driver on the wireless signal; carrying out feature extraction on the channel state information to obtain signal features; reducing the dimension of the signal characteristics to obtain target characteristics; inputting the target characteristics into a pre-trained target fatigue behavior recognition model to obtain a classification result output by the target fatigue behavior recognition model; and judging whether the current action of the driver is the fatigue driving action according to the classification result to obtain a monitoring result of the fatigue behavior of the driver. By the mode, the action of the driver is monitored by combining wireless perception, whether the action of the driver is the fatigue driving action is identified by utilizing the trained fatigue behavior identification model, the fatigue driving is monitored, the occurrence of the fatigue driving is reduced, the occurrence rate of road traffic safety accidents is reduced, and the fatigue driving is monitored relative to the acquired image; compared with a mode of analyzing physiological signals of a driver, the method and the device do not need to be provided with signal acquisition equipment, and driving experience feeling and operation of the driver are not influenced.
Referring to fig. 3, fig. 3 is a flowchart illustrating a method for monitoring fatigue behavior of a driver according to a second embodiment of the present invention.
Based on the first embodiment, the step S20 of the method for monitoring fatigue behavior of driver in this embodiment includes:
step S201: and carrying out wavelet packet decomposition on the channel state information, and extracting wavelet packet energy characteristics and wavelet packet coefficient statistical characteristics.
Specifically, the step S201 includes: performing multi-layer wavelet packet decomposition on the channel state information to obtain a wavelet packet coefficient; forming wavelet packet coefficient statistical characteristics according to the mean value, the standard deviation, the variance and the maximum value corresponding to the wavelet packet coefficients; determining the signal energy ratio corresponding to each sub-frequency band; determining the wavelet packet energy vector of each layer according to the signal energy ratio; and forming wavelet packet energy characteristics according to the wavelet packet energy vectors of all layers.
It should be understood that, preferably, the present embodiment performs three-layer wavelet packet decomposition on the channel state information, performs three-layer wavelet packet decomposition on the original channel state information by using wavelet basis functions, obtains eight wavelet packet coefficients of different action signals, and extracts a Mean value (Mean), a standard deviation (Std), a variance (Var) and a maximum value (Max) of the coefficients as statistical characteristics of the wavelet packet coefficients.
It should be noted that the signal x is assumedk，m(i) Correspond toIs N, the signal wavelet packet energy ratio is extracted as the wavelet packet energy characteristic by the formula (7):
where k is the number of decomposition layers and m is the signal subband.
Determining formula (8) according to the energy conservation principle:
and (3) determining the signal energy ratio of each sub-band by adopting normalization processing, and expressing as a formula (9):
selecting a relative wavelet packet energy vector of the nth layer, and expressing the relative wavelet packet energy vector as a formula (10):
Wn＝(En(0)，En(1)，...，En(2n-1)) (10)
and (3) determining three layers of wavelet packet energy vectors according to the formula (1) to form wavelet packet energy characteristics.
Step S202: and splicing the wavelet packet energy characteristics and the wavelet packet coefficient statistical characteristics to obtain signal characteristics.
It should be noted that, in this embodiment, the wavelet packet decomposition is used to perform feature extraction on the channel state information caused by the current action, the wavelet packet energy feature and the wavelet packet coefficient statistical feature are spliced to obtain the signal feature, and when performing feature extraction on the sample data, the same splicing method is used to splice, so as to effectively avoid the interference of noise on the original signal.
In the embodiment, the channel state information is obtained by monitoring the interference of the current action of the driver on the wireless signal; carrying out wavelet packet decomposition on the channel state information, and extracting wavelet packet energy characteristics and wavelet packet coefficient statistical characteristics; splicing the wavelet packet energy characteristics and the wavelet packet coefficient statistical characteristics to obtain signal characteristics; reducing the dimension of the signal characteristics to obtain target characteristics; inputting the target characteristics into a pre-trained target fatigue behavior recognition model to obtain a classification result output by the target fatigue behavior recognition model; and judging whether the current action of the driver is the fatigue driving action according to the classification result to obtain a monitoring result of the fatigue behavior of the driver. By the method, the collected channel state information is subjected to wavelet packet decomposition, the wavelet packet energy characteristics and the wavelet packet coefficient statistical characteristics are extracted and spliced to obtain the signal characteristics, the interference of noise on the original signal is effectively avoided while the signal characteristics are extracted, the accuracy of model classification is improved, the monitoring of fatigue driving is realized, the occurrence of fatigue driving is reduced, and the occurrence rate of road traffic safety accidents is reduced.
Referring to fig. 4, fig. 4 is a flowchart illustrating a method for monitoring fatigue behavior of a driver according to a third embodiment of the present invention.
Based on the first embodiment, before the step S10, the method for monitoring fatigue behavior of a driver in this embodiment further includes:
step S101: and monitoring the interference of various fatigue driving actions of a driver on the wireless signal to obtain a plurality of sample channel state information.
It can be understood that the car machine of this embodiment is provided with a sample data acquisition guidance process to guide the driver to make various different fatigue driving actions, specifically, actions such as yawning, frequent nodding, shaking, bending, and the like, and when the driver makes a fatigue driving action, the sample channel state information caused by the fatigue driving action is determined through the wireless signal transceiver terminal, and the sample channel state information is recorded.
Step S102: and respectively extracting the characteristics of the sample channel state information to obtain corresponding sample signal characteristics.
It should be noted that, wavelet packet decomposition is performed on each sample channel state information, corresponding wavelet packet energy characteristics and wavelet packet coefficient statistical characteristics are extracted, and the wavelet packet energy characteristics and the wavelet packet coefficient statistical characteristics corresponding to each sample channel state information are spliced to obtain sample signal characteristics.
Step S103: and reducing the dimension of each sample signal characteristic to obtain a corresponding target sample characteristic.
It can be understood that feature dimensionality reduction processing is performed on each sample signal feature by using Principal Component Analysis (PCA), and a plurality of target sample features are obtained.
Step S104: and constructing a training set and a verification set according to a plurality of target sample characteristics and corresponding fatigue action labels.
It should be noted that, in this embodiment, a large amount of acquired sample data is segmented according to a preset ratio, a training set and a verification set are constructed, and each sample data includes a target sample feature and a corresponding fatigue action tag. The preset ratio may be 7: 3.
Step S105: and training the initial fatigue behavior recognition model according to the training set to obtain the trained fatigue behavior recognition model.
It can be understood that the initial fatigue behavior recognition model may be a neural network model, preferably, the initial fatigue behavior recognition model of this embodiment is a Support Vector Machine (SVM), the Support Vector Machine (SVM) is adopted to perform classification processing on the target sample features of the training set, and model parameters are adjusted to perform training and learning, so as to obtain a trained fatigue behavior recognition model.
Step S106: and verifying the trained fatigue behavior recognition model according to the verification set.
Specifically, the step S106 includes: identifying the characteristics of each target sample in the verification set according to the trained fatigue behavior identification model to obtain an identification result corresponding to each target sample characteristic in the verification set; determining a corresponding recognition rate according to the recognition result corresponding to each target sample feature in the verification set; and determining whether the trained fatigue behavior recognition model passes the verification according to the recognition rate, wherein when the recognition rate is greater than a preset threshold value, the trained fatigue behavior recognition model is judged to pass the verification.
It should be noted that, each target sample feature in the verification set is identified through the trained fatigue behavior identification model, the identification result is compared with the fatigue action tag corresponding to each target sample feature, when the identification result is matched with the fatigue action tag, the identification result is recorded as identification success, when the identification result is not matched with the fatigue action tag, the identification result is recorded as identification failure, the corresponding identification rate is determined according to the number of the identification success and the number of the identification failure, the preset threshold value can be a fixed value set in advance, in the specific implementation, the preset threshold value is set to be 90%, and when the identification rate of the support vector machine is more than 90%, the trained target fatigue behavior identification model is obtained.
Step S107: and when the verification is passed, obtaining a trained target fatigue behavior recognition model.
In the embodiment, the interference of various fatigue driving actions of a driver on a wireless signal is monitored to obtain the state information of a plurality of sample channels; respectively extracting the characteristics of each sample channel state information to obtain corresponding sample signal characteristics; reducing the dimension of each sample signal characteristic to obtain a corresponding target sample characteristic; constructing a training set and a verification set according to the characteristics of the target samples and the corresponding fatigue action labels; training the initial fatigue behavior recognition model according to a training set to obtain a trained fatigue behavior recognition model; verifying the trained fatigue behavior recognition model according to a verification set; and when the verification is passed, obtaining a trained target fatigue behavior recognition model. By the method, the sample channel state information caused by various fatigue driving actions of the driver is monitored, the sample channel state information is subjected to feature extraction and dimension reduction to obtain target sample features, a training set and a verification set are constructed, the initial fatigue behavior recognition model is trained, and the trained target fatigue behavior recognition model is used for recognizing the features corresponding to the current actions of the driver, so that whether the actions of the driver are the fatigue driving actions is determined, the monitoring of the fatigue driving is realized, the occurrence of the fatigue driving is reduced, and the occurrence rate of road traffic safety accidents is reduced.
In addition, an embodiment of the present invention further provides a storage medium, where a driver fatigue behavior monitoring program is stored on the storage medium, and when being executed by a processor, the driver fatigue behavior monitoring program implements the driver fatigue behavior monitoring method described above.
Since the storage medium adopts all technical solutions of all the embodiments, at least all the beneficial effects brought by the technical solutions of the embodiments are achieved, and no further description is given here.
Referring to fig. 5, fig. 5 is a block diagram illustrating the structure of the first embodiment of the driver fatigue behavior monitoring apparatus according to the present invention.
As shown in fig. 5, a driver fatigue behavior monitoring apparatus according to an embodiment of the present invention includes:
the acquisition module 10 is configured to monitor interference of a current action of a driver on a wireless signal, and obtain channel state information.
And a feature extraction module 20, configured to perform feature extraction on the channel state information to obtain a signal feature.
And the dimension reduction module 30 is configured to perform dimension reduction on the signal features to obtain target features.
And the classification module 40 is configured to input the target features into a pre-trained target fatigue behavior recognition model to obtain a classification result output by the target fatigue behavior recognition model.
And the judging module 50 is configured to judge whether the current action of the driver is a fatigue driving action according to the classification result, so as to obtain a monitoring result of the fatigue behavior of the driver.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.
In the embodiment, the channel state information is obtained by monitoring the interference of the current action of the driver on the wireless signal; carrying out feature extraction on the channel state information to obtain signal features; reducing the dimension of the signal characteristics to obtain target characteristics; inputting the target characteristics into a pre-trained target fatigue behavior recognition model to obtain a classification result output by the target fatigue behavior recognition model; and judging whether the current action of the driver is the fatigue driving action according to the classification result to obtain a monitoring result of the fatigue behavior of the driver. By the mode, the action of the driver is monitored by combining wireless perception, whether the action of the driver is the fatigue driving action is identified by utilizing the trained fatigue behavior identification model, the fatigue driving is monitored, the occurrence of the fatigue driving is reduced, the occurrence rate of road traffic safety accidents is reduced, and the fatigue driving is monitored relative to the acquired image; compared with a mode of analyzing physiological signals of a driver, the method and the device do not need to be provided with signal acquisition equipment, and driving experience feeling and operation of the driver are not influenced.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment may be referred to a method for monitoring fatigue behavior of a driver provided in any embodiment of the present invention, and are not described herein again.
In an embodiment, the feature extraction module 20 is further configured to perform wavelet packet decomposition on the channel state information, extract a wavelet packet energy feature and a wavelet packet coefficient statistical feature, and splice the wavelet packet energy feature and the wavelet packet coefficient statistical feature to obtain a signal feature.
In an embodiment, the feature extraction module 20 is further configured to perform multi-layer wavelet packet decomposition on the channel state information to obtain a wavelet packet coefficient, form a wavelet packet coefficient statistical feature according to a mean value, a standard deviation, a variance, and a maximum value corresponding to the wavelet packet coefficient, determine a signal energy ratio corresponding to each sub-band, determine each layer of wavelet packet energy vector according to the signal energy ratio, and form a wavelet packet energy feature according to each layer of wavelet packet energy vector.
In an embodiment, the dimension reduction module 30 is further configured to perform normalization processing on the signal features to obtain a normalized sample matrix, calculate a covariance matrix corresponding to the normalized sample matrix, determine corresponding eigenvalues and eigenvectors according to the covariance matrix, sort the eigenvectors according to the eigenvalues to obtain an eigenvector matrix, and extract a plurality of eigenvectors from the eigenvector matrix according to a preset cumulative contribution rate to obtain the target feature.
In one embodiment, the driver fatigue behavior monitoring device further comprises a training module;
the training module is used for monitoring the interference of various fatigue driving actions of a driver on a wireless signal to obtain a plurality of sample channel state information, respectively extracting the characteristics of each sample channel state information to obtain corresponding sample signal characteristics, reducing the dimensions of each sample signal characteristic to obtain corresponding target sample characteristics, constructing a training set and a verification set according to a plurality of target sample characteristics and corresponding fatigue action labels, training an initial fatigue behavior recognition model according to the training set to obtain a trained fatigue behavior recognition model, verifying the trained fatigue behavior recognition model according to the verification set, and obtaining the trained target fatigue behavior recognition model when the verification is passed.
In an embodiment, the training module is further configured to identify, according to the trained fatigue behavior identification model, each target sample feature in the verification set to obtain an identification result corresponding to each target sample feature in the verification set, determine a corresponding identification rate according to the identification result corresponding to each target sample feature in the verification set, and determine whether the trained fatigue behavior identification model passes verification according to the identification rate, where when the identification rate is greater than a preset threshold, it is determined that the trained fatigue behavior identification model passes verification.
In one embodiment, the driver fatigue behavior monitoring device further comprises a reminding module;
the reminding module is used for determining the occurrence frequency of the fatigue driving action in a preset time period when the current action of the driver is the fatigue driving action, and reminding the driver when the occurrence frequency is greater than the preset frequency.
Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system 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 system. 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 system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g. a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A driver fatigue behavior monitoring method, characterized by comprising:
monitoring the interference of the current action of the driver on the wireless signal to obtain channel state information;
extracting the characteristics of the channel state information to obtain signal characteristics;
reducing the dimension of the signal characteristic to obtain a target characteristic;
inputting the target characteristics into a pre-trained target fatigue behavior recognition model to obtain a classification result output by the target fatigue behavior recognition model;
and judging whether the current action of the driver is a fatigue driving action according to the classification result to obtain a fatigue behavior monitoring result of the driver.
2. The method for monitoring fatigue behavior of driver as claimed in claim 1, wherein said performing feature extraction on said channel state information to obtain signal features comprises:
carrying out wavelet packet decomposition on the channel state information, and extracting wavelet packet energy characteristics and wavelet packet coefficient statistical characteristics;
and splicing the wavelet packet energy characteristics and the wavelet packet coefficient statistical characteristics to obtain signal characteristics.
3. The method for monitoring fatigue behavior of driver as claimed in claim 2, wherein said performing wavelet packet decomposition on said channel state information and extracting wavelet packet energy characteristics and wavelet packet coefficient statistical characteristics comprises:
performing multi-layer wavelet packet decomposition on the channel state information to obtain a wavelet packet coefficient;
forming wavelet packet coefficient statistical characteristics according to the mean value, the standard deviation, the variance and the maximum value corresponding to the wavelet packet coefficients;
determining the signal energy ratio corresponding to each sub-frequency band;
determining the wavelet packet energy vector of each layer according to the signal energy ratio;
and forming wavelet packet energy characteristics according to the wavelet packet energy vectors of all layers.
4. The method for monitoring the fatigue behavior of the driver as claimed in claim 1, wherein said reducing the dimensions of the signal features to obtain the target features comprises:
carrying out standardization processing on the signal characteristics to obtain a standardized sample matrix;
calculating a covariance matrix corresponding to the standardized sample matrix;
determining corresponding eigenvalue and eigenvector according to the covariance matrix;
sorting the eigenvectors according to the eigenvalues to obtain an eigenvector matrix;
and extracting a plurality of eigenvectors from the characteristic matrix according to a preset accumulated contribution rate to obtain target characteristics.
5. The method for monitoring fatigue behavior of a driver as claimed in claim 1, wherein before the interference of the current action of the driver on the wireless signal is monitored and the channel state information is obtained, the method further comprises:
monitoring the interference of various fatigue driving actions of a driver on a wireless signal to obtain a plurality of sample channel state information;
respectively extracting the characteristics of each sample channel state information to obtain corresponding sample signal characteristics;
reducing the dimension of each sample signal characteristic to obtain a corresponding target sample characteristic;
constructing a training set and a verification set according to the characteristics of the target samples and the corresponding fatigue action labels;
training an initial fatigue behavior recognition model according to the training set to obtain a trained fatigue behavior recognition model;
verifying the trained fatigue behavior recognition model according to the verification set;
and when the verification is passed, obtaining a trained target fatigue behavior recognition model.
6. The method for monitoring fatigue behavior of a driver as claimed in claim 5, wherein said verifying said trained fatigue behavior recognition model according to said verification set comprises:
identifying the characteristics of each target sample in the verification set according to the trained fatigue behavior identification model to obtain an identification result corresponding to each target sample characteristic in the verification set;
determining a corresponding recognition rate according to the recognition result corresponding to each target sample feature in the verification set;
and determining whether the trained fatigue behavior recognition model passes the verification according to the recognition rate, wherein when the recognition rate is greater than a preset threshold value, the trained fatigue behavior recognition model is judged to pass the verification.
7. The method for monitoring the fatigue behavior of the driver as claimed in any one of claims 1 to 6, wherein the determining whether the current behavior of the driver is the fatigue driving behavior according to the classification result further comprises:
when the current action of the driver is a fatigue driving action, determining the occurrence frequency of the fatigue driving action in a preset time period;
and when the occurrence frequency is greater than the preset frequency, reminding the driver.
8. A driver fatigue behavior monitoring device, characterized by comprising:
the acquisition module is used for monitoring the interference of the current action of the driver on the wireless signal to obtain channel state information;
the characteristic extraction module is used for extracting the characteristics of the channel state information to obtain signal characteristics;
the dimension reduction module is used for reducing the dimension of the signal characteristics to obtain target characteristics;
the classification module is used for inputting the target characteristics into a pre-trained target fatigue behavior recognition model to obtain a classification result output by the target fatigue behavior recognition model;
and the judging module is used for judging whether the current action of the driver is the fatigue driving action according to the classification result to obtain the monitoring result of the fatigue behavior of the driver.
9. A driver fatigue behavior monitoring device, characterized in that the device comprises: a memory, a processor and a driver fatigue behavior monitoring program stored on the memory and executable on the processor, the driver fatigue behavior monitoring program being configured to implement the driver fatigue behavior monitoring method according to any one of claims 1 to 7.
10. A storage medium, characterized in that the storage medium has stored thereon a driver fatigue behavior monitoring program, which when executed by a processor implements the driver fatigue behavior monitoring method according to any one of claims 1 to 7.
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