JP2010134533A - Drowsiness detector - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a drowsiness detector capable of highly accurately detecting driver's slight drowsiness. <P>SOLUTION: The drowsiness detector 1 measures the heartbeat or the pulse of a driver with a physiological index meter 2; extracts a heartbeat feature amount, obtains a standard deviation value of the heartbeat feature amount; and corrects the standard deviation value of the heartbeat feature amount by the heartbeat feature amount. At this time, traveling environment information regarding own vehicle is acquired based on information from a navigation device 3; output the data from an ambient environment recognition sensor 4, and a passenger detection sensor 5; and correction of the standard deviation value of the heartbeat feature amount is not allowed to be performed, when own vehicle travels through either an urban area, a curve, a road or an intersection which is yet to be experienced, when there is a moving object around own vehicle, or when a passenger is on own vehicle and a driver is moving therein. Then, the standard deviation of the heartbeat feature amount corrected is used to determine whether the driver is drowsy. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a drowsiness detection device that detects drowsiness of a driver of a vehicle.

As a conventional drowsiness detection device, for example, one described in Patent Document 1 is known. The drowsiness detection device described in Patent Document 1 measures an index (such as a heartbeat) indicating a physical condition for determining the drowsiness of the driver, and extracts a feature amount that changes according to the drowsiness of the driver from the index, By comparing this feature amount with a threshold value, it is determined whether or not the driver is in a dozing state. When extracting features from the heartbeat, Fourier transform is performed on the time-series data of the heartbeat cycle to generate an amplitude power spectrum, and integration processing is applied to the amplitude power spectrum to obtain time-series data of heartbeat fluctuation. Is obtained by performing a differentiation process on the heart rate fluctuation time-series data, calculating a threshold value from the average value and standard deviation of the differential value of the heartbeat fluctuation, and the differential value of the heartbeat fluctuation exceeding the threshold value Extract as a quantity.
JP 2008-35964 A

  However, in the above-described prior art, when the driver is extremely drowsy while driving the vehicle, it may not be possible to accurately determine that the driver is drowsy.

  An object of the present invention is to provide a drowsiness detection device that can detect a driver's shallow drowsiness with high accuracy.

  The present invention is a drowsiness detection device for detecting drowsiness of a driver of a vehicle, which extracts a heart rate feature amount from a heart rate or a pulse measured from a driver and obtains a standard deviation of the heart rate feature amount, A sleepiness level determination unit that corrects the standard deviation of the heartbeat feature value with the heartbeat feature value according to the driving environment of the vehicle, and determines the driver's sleepiness level using the distribution of the standard deviation of the heartbeat feature value after correction; It is characterized by this.

  In order to detect shallow sleepiness, it is effective to focus on heart rate features such as heart rate and heart rate fluctuation that are affected by autonomic nervous activity related to the occurrence of sleepiness. In particular, the standard deviation of the heartbeat feature amount is considered to have a correlation with shallow sleepiness. Therefore, in the sleepiness detection device of the present invention, the heartbeat or pulse of the driver is measured, the heartbeat feature amount is extracted from the heartbeat or pulse, the standard deviation of the heartbeat feature amount is obtained, and the standard deviation distribution of the heartbeat feature amount is obtained. Is used to determine the driver's sleepiness.

  Here, since the heartbeat feature amount differs for each driver, the standard deviation of the heartbeat feature amount also differs for each driver. For this reason, the sleepiness detection result may differ depending on the driver. Therefore, by correcting the standard deviation of the heartbeat feature amount with the heartbeat feature amount, the sleepiness degree determination error due to the difference in the heartbeat feature amount for each driver is eliminated. At this time, the change of the heartbeat feature amount due to sleepiness occurs continuously for a relatively long time. Further, even if the running environment of the vehicle changes, the heart rate feature value changes, and the change in the heart rate feature value accompanying the change in the running environment occurs instantaneously. For this reason, the change in the heartbeat feature amount accompanying the change in the running environment can cause noise (false sleepiness detection) when correcting the standard deviation of the heartbeat feature amount.

  Therefore, in the present invention, the standard deviation of the heartbeat feature amount is corrected with the heartbeat feature amount in accordance with the traveling environment of the vehicle. Specifically, the heartbeat feature is changed only when the heartbeat feature changes due to changes in the driver's body condition, by removing the instantaneous heartbeat feature changes associated with changes in the driving environment of the vehicle as noise. The standard deviation of the quantity is corrected with the heartbeat feature quantity. Thereby, the driver's shallow sleepiness can be detected with high accuracy.

  Preferably, the drowsiness level determination means does not execute correction of the standard deviation of the heartbeat feature amount when it is detected that the vehicle travels in an urban area, a curved road, a road with no traveling experience, or an intersection. To do. When the vehicle travels in an urban area, a curved road, a road with no traveling experience, or an intersection, an instantaneous change in heart rate feature amount is likely to occur. Therefore, in such a case, the correction of the standard deviation of the heart rate feature amount is not executed, so that the driver's shallow drowsiness can be reliably detected with high accuracy.

  Further, the sleepiness level determination means may not execute correction of the standard deviation of the heartbeat feature amount when it is detected that a moving body is present around the vehicle. When a moving body such as another vehicle or a pedestrian exists around the vehicle, an instantaneous change in the heart rate feature amount is likely to occur. Therefore, in such a case, the correction of the standard deviation of the heart rate feature amount is not executed, so that the driver's shallow drowsiness can be reliably detected with high accuracy.

  Further, the sleepiness level determination means may not perform correction of the standard deviation of the heartbeat feature amount when it is detected that the passenger is on the vehicle and the driver is moving the body. When a passenger is on the vehicle and the driver moves his / her body, an instantaneous change in the heart rate feature amount is likely to occur. Therefore, in such a case, the correction of the standard deviation of the heart rate feature amount is not executed, so that the driver's shallow drowsiness can be reliably detected with high accuracy.

  The drowsiness detection device of the present invention is characterized in that the standard deviation of the heartbeat feature amount is corrected by the heartbeat feature amount obtained by excluding the heartbeat feature amount acquired in a driving environment in which attention consciousness is likely to be activated. Is.

  According to the present invention, it is possible to detect a driver's shallow sleepiness with high accuracy. Thereby, when the driver is drowsy while driving, for example, normal recovery of consciousness or rest can be promoted at that time.

  DESCRIPTION OF EMBODIMENTS Hereinafter, preferred embodiments of a drowsiness detection device according to the present invention will be described in detail with reference to the drawings.

  FIG. 1 is a block diagram showing a schematic configuration of an embodiment of a drowsiness detection apparatus according to the present invention. In the figure, a drowsiness detection device 1 of the present embodiment is a device that is mounted on a vehicle and detects drowsiness of a driver of the vehicle. The drowsiness detection device 1 includes a physiological index measuring instrument 2, a navigation 3, an ambient environment recognition sensor 4, an occupant detection sensor 5, a drowsiness detection ECU (Electronic Control Unit) 6, and an alarm device 7.

  The physiological index measuring device 2 is a device that measures a driver's physiological index. Specifically, examples of the physiological index measuring device 2 include an electrocardiograph that measures a heartbeat, a pulse wave meter that measures a pulse from a fingertip, a forearm, and the like.

  The navigation 3 is a device that detects the current position of the host vehicle using GPS (Global Positioning System), and acquires road information on which the host vehicle is traveling from the road map information stored in the built-in memory. is there.

  The surrounding environment recognition sensor 4 is a sensor for recognizing whether a moving body such as another vehicle, a pedestrian, or a bicycle exists around the host vehicle. As the surrounding environment recognition sensor 4, an image sensor such as a camera, a radar sensor, an ultrasonic sensor, an inter-vehicle communication device that performs wireless communication with another vehicle and receives other vehicle information is used.

  The occupant detection sensor 5 is a sensor that detects whether a passenger is on the vehicle and whether the driver is moving the body. As the occupant detection sensor 5, an image sensor such as a camera, a contact sensor that detects an operation state of the navigation 3, or the like is used.

  The drowsiness detection ECU 6 includes a CPU, a memory such as a ROM and a RAM, an input / output circuit, and the like. The drowsiness detection ECU 6 inputs the output data (measurement data) of the physiological index measuring instrument 2, the information of the navigation 3, the output data of the surrounding environment recognition sensor 4 and the occupant detection sensor 5, performs predetermined processing, and the driver is weak Determine if you are sleepy.

  The alarm device 7 is a device that gives an alarm by sound (buzzer sound), image (screen display), vibration (vibrator), etc., and informs the driver of sleepiness.

  FIG. 2 is a flowchart showing details of the drowsiness detection processing procedure executed by the drowsiness detection ECU 6. Here, the case where the driver's heart rate is measured by an electrocardiograph as the physiological index measuring device 2 will be described as an example.

  In the figure, first, measurement data (heartbeat data) of the physiological index measuring instrument 2 is acquired (procedure S11), and preprocessing of the measurement data is performed (procedure S12). Specifically, in order to remove noise from the heartbeat data, first, a bandpass filter (BPF) process is performed on the heartbeat data to extract a component of a predetermined pass band (for example, 0.1 Hz to 30 Hz).

  Subsequently, as shown in FIG. 3, the waveform of the heartbeat data subjected to the BPF process is binarized by comparing with a preset threshold value. At this time, binarization is performed so that each R wave portion of the waveform of the heartbeat data becomes “1” at the timing when it reaches the maximum value (see the enlarged view in FIG. 3).

  Subsequently, as shown in FIG. 4A, a section width (time interval) t at each timing at which the binary data becomes “1” is obtained, and a graph with each section width t as a vertical axis is generated. At this time, the section width t corresponds to the heartbeat cycle of the driver.

  Subsequently, as shown in FIG. 4B, the heartbeat cycle graph (see the broken line) is obtained by interpolating the graph of the heartbeat cycle to obtain time-series data of the heartbeat cycle. Then, as shown in FIG. 5, the vertical axis unit of the time-series data of the heartbeat cycle is converted into, for example, the heart rate per minute. As a result, the heart rate value of the driver is obtained as one of the heart rate feature values.

Next, heart rate fluctuation is extracted as another heartbeat feature amount of the driver (step S13). Specifically, with respect to time-series data of the cardiac cycle (see FIG. 5), as shown in FIG. 6, fast Fourier transform (FFT) is applied to the analysis unit interval width T _term before the reference time T (arbitrary time stamp). ) To obtain a power (amplitude) spectrum for the frequency component.

Subsequently, as shown in FIG. 7, two frequency band bands (low frequency component and high frequency component) are set for the power spectrum obtained for each analysis unit interval width T _term by the fast Fourier transform. These frequency band bands are bands in which heartbeat fluctuations (changes) are likely to appear. Then, the amplitude spectrum is integrated for each frequency band.

  By repeating the fast Fourier transform process, the frequency band setting process, and the integration process, time series data of the amplitude spectrum power for each frequency band is obtained as shown in FIG. The time series data of the amplitude spectrum power is the time series data of heartbeat fluctuation. Thereby, the heartbeat fluctuation low frequency component value representing the movement of the sympathetic nerve and the heartbeat fluctuation high frequency component value representing the movement of the parasympathetic nerve are obtained. Further, by dividing the heartbeat fluctuation low frequency component value by the heartbeat fluctuation high frequency component value, a ratio (heartbeat fluctuation ratio value) between the heartbeat fluctuation low frequency component value and the heartbeat fluctuation high frequency component value is obtained.

  Next, the reference interval width (reference time width) of the heartbeat feature value referred to obtain the standard deviation of the heartbeat feature value is set (step S14). The reference interval width is set for the heart rate value, the heart rate fluctuation low frequency component value, the heart rate fluctuation high frequency component value, and the heart rate fluctuation ratio value. A specific method for setting the reference interval width will be described below by taking as an example a case where the reference interval width is applied to a heart rate value.

  That is, as shown in FIG. 9A, first, time-series data of heart rate values (see FIG. 5) are divided into arbitrary lengths (about several minutes) m and stored in a reference time width determining data storage buffer. To do.

Then, by performing a fast Fourier transform (FFT) operation on the heart rate value stored in the data storage buffer, a frequency analysis result as shown in FIG. 9B is obtained. Here, F is the frequency range, f _max is the maximum value of the frequency range F, f _min is the minimum value of the frequency range F, and A is the maximum value of the amplitude spectrum power of the heart rate value in the frequency range F. F _peak is a frequency at which the maximum value A of the amplitude spectrum power is obtained. The frequency range F is a range obtained by statistical analysis as corresponding to sleepiness for each individual, and the frequency f _peak is a frequency at which changes in sleepiness are particularly likely to occur in the heart rate value.

Subsequently, the reference interval width of the heart rate value is obtained from the following calculation formula using such a frequency f _peak .
Reference interval width of heart rate value = 1 / f _peak

Thus, by extracting the peak value frequency f _peak in the frequency range F as a place where sleepiness appears prominently, it becomes possible to remove the influence of data noise and determine the sleepiness state (described later).

Next, the heart rate value, the heart rate fluctuation low frequency component value, the heart rate fluctuation high frequency component value, and the heart rate fluctuation ratio value are each cut out with the reference interval width (total number of data: N), and the average value in this interval is calculated (step S15). .
Cut out heart rate value = {X ₁ , X ₂ , X ₃ ,... X _N }
Cut out the heartbeat fluctuation low frequency component value _{= {Y 1, Y 2,} Y 3, ... Y N}
The extracted heartbeat fluctuation high-frequency component value = {Z ₁ , Z ₂ , Z ₃ ,... Z _N }
The extracted heartbeat fluctuation ratio value = {W ₁ , W ₂ , W ₃ ,... W _N }

  Next, in the same manner as described above, the heart rate value, the heart rate fluctuation low frequency component value, the heart rate fluctuation high frequency component value, and the heart rate fluctuation ratio value are each cut out with the reference interval width, and the standard deviation value in this interval is calculated (step S16).

Ｎ：切り出された心拍数値データの総数
ｉ：心拍数値の番号
Ｘｉ：ｉ番目の心拍数値
Ｘ_ave：心拍数値Ｎ個の平均値 The calculation formula for the standard deviation of the heart rate value is as follows.

N: Total number of cut out heart rate data
i: Number of heart rate value
Xi: i-th heart rate value
X _ave : Average value of N heart rate values

Ｎ：切り出された心拍ゆらぎ低周波成分値データの総数
ｉ：心拍ゆらぎ低周波成分値の番号
Ｙｉ：ｉ番目の心拍ゆらぎ低周波成分値
Ｙ_ave：心拍ゆらぎ低周波成分値Ｎ個の平均値 The calculation formula of the standard deviation of the heartbeat fluctuation low frequency component value is as follows.

N: Total number of extracted heartbeat fluctuation low frequency component value data
i: Number of heartbeat fluctuation low frequency component value
Yi: i-th heartbeat fluctuation low frequency component value
Y _ave : Average value of N heartbeat fluctuation low frequency component values

Ｎ：切り出された心拍ゆらぎ高周波成分値データの総数
ｉ：心拍ゆらぎ高周波成分値の番号
Ｚｉ：ｉ番目の心拍ゆらぎ高周波成分値
Ｚ_ave：心拍ゆらぎ高周波成分値Ｎ個の平均値 The calculation formula of the standard deviation of the heartbeat fluctuation high-frequency component value is as follows.

N: Total number of extracted heartbeat fluctuation high-frequency component value data
i: Number of heartbeat fluctuation high frequency component value
Zi: i-th heartbeat fluctuation high-frequency component value
_Zave : Average value of N heartbeat fluctuation high frequency component values

Ｎ：切り出された心拍ゆらぎ比値データの総数
ｉ：心拍ゆらぎ比値の番号
Ｗｉ：ｉ番目の心拍ゆらぎ比値
Ｗ_ave：心拍ゆらぎ比値Ｎ個の平均値 The calculation formula of the standard deviation of the heart rate fluctuation ratio value is as follows.

N: Total number of extracted heart rate fluctuation ratio value data
i: Heart rate fluctuation ratio number
Wi: i-th heart rate fluctuation ratio value
W _ave : Average value of N heart rate fluctuation ratio values

  Next, the standard deviation values of the heart rate value, the heart rate fluctuation low frequency component value, the heart rate fluctuation high frequency component value, and the heart rate fluctuation ratio value are corrected (step S17). FIG. 10 shows a functional block for executing the process of step S17.

  In FIG. 10, the functions for correcting the standard deviation values of the heart rate value, the heart rate fluctuation low frequency component value, the heart rate fluctuation high frequency component value, and the heart rate fluctuation ratio value include the correction target standard deviation value storage buffer 11 and the environment information acquisition. There are a unit 12, an environmental condition database 13, a correction execution determination unit 14, and a standard deviation correction calculation unit 15.

  The correction target standard deviation value storage buffer 11 stores a heart rate feature amount standard deviation value to be corrected and an instantaneous value (a heart rate feature amount value at the current time) used for correction. The heart rate feature amount standard deviation value is the heart rate standard deviation value, the heart rate fluctuation low frequency component standard deviation value, the heart rate fluctuation high frequency component standard deviation value, and the heart rate fluctuation ratio standard deviation value obtained in step S16. The instantaneous values used for correction are the heart rate value at the current time, the heart rate fluctuation low frequency component value, the heart rate fluctuation high frequency component value, and the heart rate fluctuation ratio value.

  The environment information acquisition unit 12 acquires travel environment information of the host vehicle based on the information of the navigation 3 and the output data of the surrounding environment recognition sensor 4 and the occupant detection sensor 5. The traveling environment information of the host vehicle includes information on the road on which the host vehicle travels, information on the surrounding environment of the host vehicle, and information on passengers including the driver. Information on the road on which the host vehicle travels is acquired from the navigation 3, information on the surrounding environment of the host vehicle is acquired based on output data of the surrounding environment recognition sensor 4, and passenger information is output by the passenger detection sensor 5. Obtained based on data.

  The environmental condition database 13 stores and holds environmental conditions in which the correction of the heart rate feature amount standard deviation value is not performed. The environmental conditions that do not correct the standard deviation value of the heart rate feature amount include conditions that the vehicle travels in urban areas, curved roads, roads on which the driver has not traveled, intersections, and other conditions around the vehicle. An environment in which attention consciousness is likely to be activated, such as the condition that there are moving objects such as vehicles, pedestrians, bicycles, etc., and the condition that the passenger is on the vehicle and the driver moves the body for some purpose. It is.

  The correction execution determining unit 14 compares the traveling environment information of the host vehicle acquired by the environment information acquiring unit 12 with the environmental conditions stored in the environmental condition database 13, and the traveling environment information of the host vehicle is stored in the environmental condition database 13. When the stored environmental condition matches, the correction processing of the heart rate feature amount standard deviation value is not performed. In other words, when the vehicle travels in an urban area, a curved road, a road where the driver has not traveled, or an intersection, or when a moving object exists around the vehicle, a passenger is on the vehicle. In addition, when the driver moves his / her body, correction processing for the standard deviation value of the heart rate feature amount is not performed. On the other hand, when the traveling environment information of the host vehicle does not match the environmental conditions stored in the environmental condition database 13, the correction processing of the heart rate feature amount standard deviation value is performed.

When the correction execution determining unit 14 determines to correct the heart rate feature amount standard deviation value, the standard deviation correction calculating unit 15 uses the following calculation formula to calculate the heart rate standard deviation value, the heart rate fluctuation low frequency component standard. By correcting the deviation value, heart rate fluctuation high frequency component standard deviation value and heart rate fluctuation ratio standard deviation value, heart rate standard deviation correction value, heart rate fluctuation low frequency component standard deviation correction value, heart rate fluctuation high frequency component standard deviation correction value and heart rate fluctuation A ratio standard deviation correction value is obtained.

  Here, an example of the heart rate standard deviation value and the heart rate value for each time stored in the correction target standard deviation value storage buffer 11 is shown in FIG. In this case, the heart rate standard deviation correction value calculated by the above formula is as shown in FIG.

  Returning to FIG. 2, after executing step S17 as described above, the heart rate standard deviation correction value, the heart rate fluctuation low frequency component standard deviation correction value, the heart rate fluctuation high frequency component standard deviation correction value, and the heart rate fluctuation ratio standard deviation correction value are obtained. It is used to determine whether the driver has shallow sleepiness (step S18).

  FIG. 12 shows an example of a method for determining drowsiness based on the heartbeat fluctuation low frequency component standard deviation correction value. In the method shown in the figure, the heartbeat fluctuation low frequency component standard deviation correction value is compared with a preset shallow drowsiness detection threshold, and when the heartbeat fluctuation low frequency component standard deviation correction value is higher than the shallow drowsiness detection threshold, When it is determined that there is shallow sleepiness and the heartbeat fluctuation low frequency component standard deviation correction value is lower than the shallow sleepiness detection threshold, it is determined that there is no sleepiness.

  It should be noted that the presence or absence of shallow sleepiness can also be determined in the same manner when using the heart rate standard deviation correction value, the heart rate fluctuation high frequency component standard deviation correction value, and the heart rate fluctuation ratio standard deviation correction value.

  When it is determined in step S18 that there is no drowsiness by the above method, the process returns to step S11 and the processes of steps S11 to S18 are repeatedly executed. On the other hand, when it is determined in step S18 that there is shallow sleepiness, the alarm device 7 is controlled to notify the driver of the occurrence of sleepiness (step S19), and then the procedure returns to step S11.

  In the above, the physiological index measuring device 2 constitutes a measuring means for measuring the driver's heartbeat or pulse. The above steps S11 to S13 in the sleepiness detection ECU 6 constitute a heartbeat feature amount extraction means for extracting a heartbeat feature amount from the heartbeat or pulse of the driver. The procedures S14 to S16 constitute standard deviation calculation means for obtaining the standard deviation of the heartbeat feature amount. The navigation 3, the surrounding environment recognition sensor 4, the occupant detection sensor 5, and the environment information acquisition unit 12 in step S17 in the drowsiness detection ECU 6 constitute a travel environment detection means for detecting the travel environment of the vehicle. In the drowsiness detection ECU 6, the correction target standard deviation value storage buffer 11, the environmental condition database 13, the correction execution determination unit 14, and the standard deviation correction calculation unit 15 in step S17 described above calculate the standard deviation of the heartbeat feature amount according to the running environment of the vehicle. A correction means for correcting with the heartbeat characteristic amount is configured. The procedure S18 constitutes sleepiness level determination means for determining the driver's sleepiness level using the distribution of the standard deviation of the corrected heart rate feature value obtained by the correction means.

  As described above, in the present embodiment, focusing on the heart rate affected by the autonomic nervous activity related to the occurrence of sleepiness, the heart rate and heart rate fluctuation are extracted by measuring the heart rate or pulse of the driver, The standard deviation value of the heart rate and the heart rate fluctuation is obtained, and the driver's sleepiness is determined using the standard deviation value. At this time, the drowsiness level of the driver can be determined as an index of the physiological state when driving while having shallow drowsiness to endure drowsiness and return to the awake state.

  By the way, since there are individual differences in heart rate values and heart rate standard deviation values, if the heart rate standard deviation value is used as it is for drowsiness determination, the determination result may vary depending on the driver. By correcting for each driver, the influence of the fluctuation of the heart rate value for each driver on the sleepiness determination result is eliminated. The same applies to the heartbeat fluctuation low frequency component value, the heartbeat fluctuation high frequency component value, and the heartbeat fluctuation ratio value.

  At this time, factors that cause fluctuations in the heart rate characteristic amount such as the heart rate are due to a driver's internal condition that affects sleepiness and due to a change in the driving environment of the vehicle. Changes in heart rate features associated with sleepiness last for several minutes to several hours in the body, but changes in heart rate features caused by changes in the driving environment of the vehicle are instantaneous, ranging from several seconds to several tens of seconds. It is. Specifically, the driver's heart rate, etc., suddenly increases when the vehicle travels in urban areas, curved roads, roads with no driving experience, intersections, or when there are moving objects around the vehicle. May rise. For this reason, the change in the heart rate feature amount caused by the change in the running environment of the vehicle causes noise (false sleepiness detection) when correcting the standard deviation value of the heart rate feature amount. Therefore, it is necessary to correct the standard deviation value of the heartbeat feature amount only when the factor that causes the heartbeat feature amount to change depends on the state of the driver.

  In the present embodiment, the travel environment information of the host vehicle is acquired based on the information of the navigation 3, the output data of the surrounding environment recognition sensor 4 and the occupant detection sensor 5, and the host vehicle is an urban area, a curved road, a road with no travel experience, When it is determined that the vehicle travels at one of the intersections, when it is determined that there is a moving body around the host vehicle, when it is determined that the passenger is on the host vehicle and the driver is moving the body The standard deviation value of the heartbeat feature value is not corrected. Thereby, the driver's shallow sleepiness can be detected with high accuracy and without depending on the driver. Therefore, it is possible to effectively prevent a drowsy driving by encouraging the driver to restore normal consciousness or rest when there is shallow sleepiness.

  The present invention is not limited to the above embodiment. For example, in the above embodiment, the driver's drowsiness determination is performed using four heart rate feature values of a heart rate value, a heart rate fluctuation low frequency component value, a heart rate fluctuation high frequency component value, and a heart rate fluctuation ratio value. At least one of the heartbeat feature values may be used.

It is a block diagram which shows schematic structure of one Embodiment of the drowsiness detection apparatus concerning this invention. It is a flowchart which shows the detail of the drowsiness detection process procedure performed by drowsiness detection ECU shown in FIG. It is a wave form diagram which shows an example of the output waveform and binarization waveform of the physiological index measuring device shown in FIG. It is a wave form diagram which shows an example of the section width of a binarization waveform, and a period time series. It is a wave form diagram which shows an example of the period time series of a heart rate. It is a wave form diagram which shows an example of the waveform obtained by carrying out FFT processing with respect to the period time series of heart rate. It is a wave form diagram which shows the state which set the two frequency band bands with respect to the waveform obtained by FFT processing. It is a wave form diagram which shows an example of the period time series of heartbeat fluctuation. It is a wave form diagram which shows the method of setting the reference area width | variety of a heart rate feature-value. It is a figure which shows the functional block which performs the standard deviation value correction process shown in FIG. It is a table | surface which shows an example of a heart rate standard deviation value, a heart rate value, and a heart rate standard deviation correction value. It is a wave form diagram which shows an example of the method of determining drowsiness by the heartbeat fluctuation low frequency component standard deviation correction value.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 ... Sleepiness detection apparatus, 2 ... Physiological index measuring device (measurement means), 3 ... Navigation (travel environment detection means), 4 ... Ambient environment recognition sensor (travel environment detection means), 5 ... Crew detection sensor (travel environment detection means) ), 6 ... Sleepiness detection ECU (heart rate feature amount extraction means, standard deviation calculation means, correction means, sleepiness level determination means), 11 ... correction target standard deviation value storage buffer (correction means), 12 ... environmental information acquisition unit (running) (Environment detection means), 13 ... environmental condition database (correction means), 14 ... correction execution determination section (correction means), 15 ... standard deviation correction calculation section (correction means).

Claims (5)

A drowsiness detection device for detecting drowsiness of a driver of a vehicle,
A standard deviation calculating means for extracting a heartbeat characteristic amount from a heartbeat or a pulse measured from the driver and obtaining a standard deviation of the heartbeat characteristic amount;
The sleepiness degree is determined by correcting the standard deviation of the heartbeat feature quantity with the heartbeat feature quantity in accordance with the driving environment of the vehicle, and determining the sleepiness degree of the driver using the distribution of the standard deviation of the heartbeat feature quantity after the correction. A drowsiness detection device comprising: a determination unit.   The drowsiness level determination means does not execute correction of the standard deviation of the heartbeat feature amount when it is detected that the vehicle travels in an urban area, a curved road, a road with no traveling experience, or an intersection. The drowsiness detection device according to claim 1, wherein:   2. The sleepiness level determination unit according to claim 1, wherein when the presence of a moving body is detected around the vehicle, the sleepiness level determination unit does not execute correction of a standard deviation of the heartbeat feature amount. Sleepiness detection device.   The drowsiness level determination means does not execute correction of the standard deviation of the heartbeat feature amount when it is detected that a passenger is on the vehicle and the driver is moving the body. The drowsiness detection device according to claim 1.   A drowsiness detection device characterized by correcting a standard deviation of a heartbeat feature amount by a heartbeat feature amount obtained by excluding a heartbeat feature amount acquired in a driving environment in which attention consciousness is likely to be activated.

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