JP2008188108A - Arousal level estimation device - Google Patents

Abstract

An object of the present invention is to provide a wakefulness estimation device capable of estimating a wakefulness level with high accuracy even when there is precipitation.
A wakefulness estimation device (1) for estimating a degree of arousal of a subject, a feature amount extraction means (12) for extracting a feature quantity that changes in accordance with the degree of wakefulness of the subject, and a feature extracted by the feature amount extraction means (12). The estimation means 13, 14, 15 for estimating the degree of arousal of the subject based on the amount and the precipitation state acquisition means 3, 10 for detecting the precipitation state are provided. The estimation means 13, 14, 15 are the precipitation state acquisition means 3. , 10 is characterized in that it is easier to estimate that the degree of arousal of the subject is higher than when there is little precipitation detected.
[Selection] Figure 1

Description

  The present invention relates to an arousal level estimation apparatus that estimates arousal level with high accuracy.

In order to make a driver of a vehicle perform safe driving, an apparatus for determining a driver's sleepiness (awakening level) has been developed. As a drowsiness determination device, for example, there is a device that extracts drowsiness by extracting a feature amount that changes according to drowsiness from heartbeat, brain waves, blinks, and the like, and compares the feature amount with a threshold value. In particular, the apparatus shown in Patent Document 1 acquires weather information and predicts that drowsiness becomes stronger as precipitation such as rain becomes more intense.
JP 2003-61939 A JP 2002-219968 A JP 2003-325489 A JP 2002-127780 A JP 2002-36905 A

  However, when there is precipitation, the driver actually becomes a stimulus from the outside, and the stimulus makes it difficult to fall asleep (the degree of arousal increases). Therefore, in the apparatus shown in Patent Document 1, when there is rain, there is a risk of erroneous determination that the driver is asleep.

  Therefore, an object of the present invention is to provide a wakefulness estimation device capable of estimating the wakefulness with high accuracy even when there is rain.

  The arousal level estimation device according to the present invention is a wakefulness level estimation device that estimates a subject's arousal level, and includes a feature amount extraction unit that extracts a feature amount that changes according to the subject's arousal level, and a feature amount extraction unit. The estimation means for estimating the awakening level of the subject based on the extracted feature amount and the precipitation state detection means for detecting the precipitation state, and the estimation means is small when there is a lot of precipitation detected by the precipitation state detection means. It is easy to estimate that the subject's arousal level is high compared to.

  In this arousal level estimation device, a feature amount that changes in accordance with a subject's arousal level (sleepiness) is extracted by a feature amount extraction unit. The feature quantity that changes according to the arousal level is, for example, a feature quantity extracted from a heartbeat, a feature quantity extracted from an electroencephalogram, a feature quantity extracted from respiration, a feature quantity extracted from blink, a feature quantity extracted from lip movement, It is a feature quantity extracted from body movement (back stretch, body twist, etc.). In the awakening level estimation device, the awakening level of the subject is estimated based on the feature amount by the estimation unit. In particular, in the arousal level estimation device, the precipitation state is detected by the precipitation state detection means. Precipitation is a phenomenon in which water vapor in the atmosphere changes and falls, and is precipitation due to rain, snow, hail, hail and the like. When there is precipitation, the risk of accidents increases due to changes in road surface conditions and visibility, and visual stimulation occurs due to contact with raindrops on the windshield. It can be inferred that these factors are external stimuli for the subject, and the characteristic amount (physiological index, etc.) changes, and the subject's arousal level increases (becomes difficult to fall asleep). Therefore, the awakening level estimation device makes it easy to estimate that the awakening level of the subject is higher when the precipitation is high than when the precipitation level is low. In this way, in the awakening level estimation device, when there is precipitation, the awakening level is estimated to be higher than usual with respect to the characteristic amount, so that the awakening level can be improved even in a situation where the characteristic amount is changed due to a stimulus due to precipitation. Can be estimated with high accuracy.

  The present invention makes it easy to estimate that the degree of arousal is high when there is precipitation, so that the degree of arousal can be estimated with high accuracy even when there is precipitation.

  Hereinafter, embodiments of an arousal level estimation apparatus according to the present invention will be described with reference to the drawings.

  In the present embodiment, the wakefulness estimation device according to the present invention is applied to a dozing detection device that is mounted on a vehicle and detects a driver's dozing state. When the dozing detection device according to the present invention measures an index representing a driver's physiological state, detects a dozing state (a state of low arousal level) based on a feature amount extracted from the index, and detects a dozing state Tells the driver that he is asleep.

  Here, the relationship between precipitation and the driver's arousal level (sleepiness) will be described. When we investigated the weather when many drivers fell asleep during driving, few drivers fell asleep when there was rainfall such as rain or snowfall, and fell asleep during clear or overcast weather. The result that there were many drivers who became. This tendency is particularly noticeable when precipitation continues. In this way, the driver recognizes that the risk of accidents increases due to changes in road surface conditions and visibility conditions as a factor that reduces the incidence of dozing state during precipitation (the degree of arousal increases). Therefore, it is conceivable that the driver is more careful than usual and that the driver is given a visual stimulus by contact with raindrops on the windshield. Since these two factors affect the driver as a stimulus, the physiological state of the driver changes due to the influence of the stimulus. In other words, the physiological state when there is precipitation is different from the physiological state when there is no precipitation. As a result of the change in the physiological state in this way, it can be estimated that it becomes difficult for the patient to fall asleep (the degree of arousal is increased). Therefore, when detecting a dozing state based on physiological indices (features), it is necessary to change the detection sensitivity to changes in physiological indices, that is, the detection sensitivity (judgment condition) of the dozing state depending on the presence or absence of precipitation. It is necessary to lower the detection sensitivity.

  With reference to FIGS. 1-2, the dozing detection apparatus 1 which concerns on this Embodiment is demonstrated. FIG. 1 is a configuration diagram of a snoozing detection apparatus according to the present embodiment. FIG. 2 is a graph showing an example of the relationship between the drowsiness level, the size of the feature quantity for dozing determination when there is rain and when there is no precipitation, and the threshold value for dozing determination.

  The dozing detection device 1 compares the feature amount that changes according to the driver's sleepiness (arousal level) with the threshold for dozing determination, and determines that the feature amount is larger than the threshold value, so that the dozing state is determined. In particular, in the dozing detection apparatus 1, in order to determine the dozing state with high accuracy regardless of precipitation, the threshold for dozing determination is made variable. For this purpose, the dozing detection apparatus 1 includes an index measurement unit 2, a precipitation state acquisition unit 3, an output unit 4, and an ECU [Electronic Control Unit] 5. The ECU 5 includes a precipitation presence / absence determination unit 10, a precipitation presence / absence determination result storage buffer 11, A feature amount extraction unit 12, a precipitation presence / absence determination result determination unit 13, a dozing detection unit 14, a precipitation handling unit 15, a dozing detection presence / absence determination unit 16, and a dozing output unit 17 are configured.

  In the present embodiment, the precipitation state acquisition unit 3 and the precipitation presence / absence determination unit 10 correspond to the precipitation state detection unit described in the claims, and the feature amount extraction unit 12 includes the feature amounts described in the claims. Corresponding to the extraction means, the precipitation presence / absence judgment result determination section 13, the dozing detection section 14 and the precipitation handling section 15 correspond to the estimation means described in the claims.

  The index measuring means 2 is a means for measuring various indices representing the physical condition for judging the driver's sleepiness. The index measuring means 2 measures each index and transmits an index signal indicating the measured index to the ECU 5. The index measuring means 2 is, for example, a heartbeat sensor, an electroencephalogram sensor, a pulse wave sensor, a respiration sensor, a camera that images the driver's face or body, and an image processing device that processes the captured image. The means using the camera detects the blink of the driver's face, the movement of the lips, etc. from the captured image, or detects the driver's body stretching, twisting of the body, and the like.

  The precipitation state acquisition means 3 is a means for acquiring a precipitation state due to rain, snow, etc. For example, a raindrop sensor for directly detecting the amount of raindrops on the vehicle, a VICS [Vehicle Information and VICS for acquiring weather information from the outside, etc. Communication System]. The precipitation state acquisition means 3 acquires the precipitation state and transmits a precipitation state signal indicating the precipitation state to the ECU 5.

  The output means 4 is a means for notifying the output target that the driver is asleep or for prompting the driver to rest or a means for increasing the driver's arousal level. When the output means 4 receives the output signal from the ECU 5, the output means 4 outputs according to each means. As the output means 4, for example, means for notifying by sound (buzzer, audio, radio, horn), means for notifying by light (meter lighting, room lighting), means for notifying by tactile sense or hot / cold sense (embedded in a sheet) Vibration device, air conditioner wind and temperature changes), smell notification means (fragrance injection), command output to the system. Examples of the output target include a driver, an occupant sitting outside the driver's seat, a manager who manages the operation of a business vehicle such as a truck or a taxi, and a vehicle control system.

  The ECU 5 includes a CPU [Central Processing Unit], a ROM [Read Only Memory], a RAM [Random Access Memory], and the like, and comprehensively controls the dozing detection device 1. When the ECU 5 is activated, it takes in the index signal from the index measuring means 2 and the precipitation state signal from the precipitation state acquisition means 3 at regular intervals, stores the indicator of the indicator signal and the precipitation state of the precipitation state signal in each buffer, It is stored as time-series data at regular intervals. Moreover, in ECU5, each process part 10,12-17 is comprised by running each program stored in ROM with CPU, and the process of each process part 10,12-17 is performed for every fixed time.

  The precipitation presence / absence determination unit 10 determines whether there is precipitation based on the precipitation state acquired by the precipitation state acquisition unit 3 at regular time intervals. For example, when the amount of raindrops is a predetermined amount or more, it is determined that there is precipitation. The predetermined amount is set by an experiment or the like, and a raindrop amount that is a stimulus to the driver and changes the physiological state is set. Then, the precipitation presence / absence determination unit 10 stores the determination result in the precipitation presence / absence determination result storage buffer 11.

  The precipitation presence / absence determination result storage buffer 11 is a buffer that is configured in a predetermined area of the RAM and stores the determination result of precipitation presence / absence in the precipitation presence / absence determination unit 10 in time series.

The feature quantity extraction unit 12 extracts a feature quantity for determining a doze state from each index measured by the index measurement unit 2. The extracted feature amount is a parameter that changes according to sleepiness (awakening level), and is set so that the value increases as sleepiness increases. Further, the feature extraction unit 12 obtains the basic value of the threshold TH ₂ for determining the dozing state from the feature of each indicator.

Here, a case will be described in which feature amounts are extracted from heartbeats measured by a heartbeat sensor as an example of extracting feature amounts. First, the feature amount extraction unit 12 performs a band pass filter process on the time series data of the heartbeat, and extracts a component of a predetermined pass band (for example, 0.1 Hz to 30 Hz) from the time series data of the heartbeat. Next, the feature quantity extraction section 12 cuts out when the threshold TH ₀ or more and going on corrugated portion for heartbeat timing detected from series data of the heartbeat. Then, the feature amount extraction unit 12 binarizes with the timing when the cut-out waveform portion is maximized as 1 and the other timing as 0. Next, the feature amount extraction unit 12 obtains a time from each heartbeat timing t _{i that} is binarized 1 to the next heartbeat timing t _{i + 1} that becomes 1, and sets the time interval (t _{i + 1} −t _i ) for each time interval. It is applied to the heart rate timing _{t i.} Time information given to the each heartbeat timing t _i becomes the information of the cardiac cycle. Next, the feature amount extraction unit 12 obtains a heartbeat period curve by complementing the information of the heartbeat period, and obtains time-series data of the heartbeat period.

  Next, the feature amount extraction unit 12 performs fast Fourier transform processing on time-series data of the heartbeat period in the analysis unit section width Tterm before the reference time T that is an arbitrary time stamp. Next, the feature amount extraction unit 12 performs an integration process on the amplitude power spectrum for each preset frequency band in the power spectrum obtained for each analysis unit interval width Tterm by fast Fourier transform. The frequency band may be a frequency band in which fluctuations (changes) appear strongly with respect to the measured heartbeat. Then, the feature amount extraction unit 12 performs fast Fourier transform processing on the time-series data of the heartbeat period in the analysis unit section width Tterm every time a fixed time elapses and reaches the reference time T, thereby obtaining the analysis unit section width Tterm. Every time, the integration processing is performed on the amplitude power spectrum. The time series data of the amplitude spectrum power obtained here is the time series data of heartbeat fluctuation.

Next, the feature amount extraction unit 12 performs a differentiation process on the time series data of heartbeat fluctuation. Next, the feature amount extracting unit 12 sets an analysis section width A to terminate td before time from the current time t _0. Then, the feature quantity extraction section 12 calculates an average value mean and the standard deviation sd of the time-series data of the differential value of the heartbeat fluctuation of the analysis section width A, and calculates the threshold value TH ₁ by the equation (1).

By setting the three-fold away the standard deviation sd from the mean value mean the threshold value TH _1, the heartbeat fluctuation differential value exceeds the threshold value TH ₁ will be impossible 99%, a statistically significant difference A characteristic change of heartbeat fluctuation can be detected.

Next, the feature amount extraction unit 12 determines whether the differential value of the heartbeat fluctuation exceeds a threshold value TH ₁ for each time. Constant time prior to the current time t ₀ in the past, if the differential value of the heartbeat fluctuation does not exceed the threshold value TH _1, the feature amount extraction unit 12, and updates the threshold value TH ₁ at the next time t _{0 + 1.} On the other hand, a certain time prior to the current time t ₀ in the past, if the differential value of the heartbeat fluctuation exceeds the threshold value TH _1, the feature amount extraction unit 12, without updating the threshold TH ₁ at the next time t _{0 + 1} In addition, the threshold value TH ₁ at the current time t ₀ is maintained as the threshold value TH ₁ . This prevents the use of the differential value of the heartbeat fluctuation exceeding the threshold value TH ₁ to the threshold setting, it becomes possible to accurately determine the change in the driver drowsiness.

Next, the feature amount extraction unit 12, for each frequency band, determines whether the differential value of the heartbeat fluctuation exceeds a threshold value TH _1. Then, the feature amount extraction unit 12, a predetermined time interval, counting the characteristic change of the heartbeat fluctuation exceeding the threshold value TH _1, obtaining the time-series data of the above threshold density. The time-series data of the density exceeding the threshold is the occurrence frequency of the characteristic change of the heartbeat fluctuation, and is the final feature amount for determining the dozing state.

Next, the feature quantity extraction unit 12 extracts time-series data of the specific section A in the first half of the time-series data from the time-series data having the density exceeding the threshold. Then, the feature amount extraction unit 12 extracts the above threshold density maximum value MM from time-series data in a specific section A, calculates a threshold value TH ₂ by the equation (2). Threshold TH ₂ obtained by the formula (2) is a basic value of the threshold value for determining the dozing state.

  scale is an arbitrary real number, param is an arbitrary variable (example 1: MM, example 2: standard deviation of density exceeding threshold in specific interval A), and the values of scale and param are statistically significant fluctuations Adjustments may be made so that a characteristic change is detected.

  The precipitation presence / absence determination result determination unit 13 determines whether or not there is currently precipitation based on the determination result of precipitation presence / absence stored in the precipitation presence / absence determination result storage buffer 11. When there is no precipitation, the precipitation presence / absence determination result determination unit 13 causes the dozing detection unit 14 to execute processing without executing the processing in the precipitation handling unit 15. On the other hand, if there is precipitation, the precipitation presence / absence determination result determination unit 13 causes the precipitation handling unit 15 to execute processing and then causes the dozing detection unit 14 to execute processing.

Doze detecting section 14 determines whether the feature quantity of each indicator extracted by the feature amount extraction unit 12 exceeds the threshold value TH _2. As the threshold TH ₂ , when the process in the precipitation handling unit 15 is executed, the threshold TH ₂ (correction value) obtained by the precipitation handling unit 15 is used, and the process in the precipitation handling unit 15 is executed. If not, the threshold TH ₂ (basic value) obtained by the feature quantity extraction unit 12 is used. When the characteristic amount does not exceed the threshold value TH _2, it determines doze detecting section 14 is not a doze state (high degree of awakening). When the characteristic amount exceeds the threshold value TH _2, doze detecting section 14 measures the time that has exceeded the threshold value TH _2, snooze state (awakening degree when the measured time exceeds the time threshold TH ₃ Low).

Here, as an example of detecting the dozing state, a case where the dozing state is detected using the above-described heartbeat feature amount (time-series data of density exceeding the threshold) will be described. The dozing detection unit 14 extracts time series data after the specific section A in the first half of the time series data from the time series data of the density exceeding the threshold value of the heart rate obtained by the feature amount extraction unit 12. Then, in the doze detecting section 14 determines whether or exceeds the threshold value TH ₂ at the time series data after specific section A. If it does not exceed the threshold value TH _2, the doze detecting section 14 determines that not doze state. On the other hand, if the threshold is exceeded TH _2, the doze detecting section 14 measures the time that has exceeded the threshold value TH _2. Data exceeds this threshold TH ₂ is a characteristic change in the statistically a significant difference fluctuation indicates that drowsiness is strong. Then, it is determined whether the doze detecting section 14, the time exceeds the threshold value TH ₂ exceeds the time threshold TH _3. If it does not exceed the time threshold TH _3, the doze detecting section 14 determines that not doze state. On the other hand, if it exceeds the time threshold value TH _3, the doze detecting section 14, the driver determines the doze state. The time exceeding the threshold value TH _2, the may be the time that has exceeded continuously, or may be integrated time of time that exceeds discretely within a predetermined time.

Precipitation corresponding unit 15 adds the threshold value correction amount B to the basic value of the threshold TH ₂ obtained by the feature amount extraction unit 12 and the addition value becomes a correction value of the threshold TH _2. The threshold correction amount B is set in advance based on an experiment for a plurality of subjects based on the relationship between changes in the intensity of drowsiness in the presence of precipitation and in the absence of precipitation and the change in the feature amount of each index. . When there is precipitation, as described above, it can be predicted that the physiological state of the driver changes due to the stimulation by precipitation, and the feature amount becomes relatively large. Therefore, by increasing the threshold value TH _2, hard to detect dozing state than without precipitation.

Here, the case of obtaining the threshold value TH ₂ by using the feature amount of the heartbeat described above as an example of the precipitation correspondence. The rainfall corresponding section 15, by using the threshold value correction amount B set for above threshold density maximum value MM and heart in a specific section A, calculates a threshold value TH ₂ by the equation (3). Threshold TH ₂ obtained by the formula (3) is a correction value of the threshold value for determining the dozing state.

Here, the threshold value TH ₂ (correction value) is obtained by Expression (3), but the threshold value correction amount B may be added to the threshold value TH ₂ (basic value) obtained by Expression (2).

  The dozing detection presence / absence determination unit 16 determines whether the dozing detection unit 14 has detected a dozing state. When the dozing state is not detected by the dozing detection unit 14, the dozing detection presence / absence determination unit 16 does not execute the process in the dozing output unit 17. On the other hand, when the dozing state is detected by the dozing detection unit 14, the dozing detection presence determination unit 16 causes the dozing output unit 17 to execute processing.

  The dozing output unit 17 transmits an output signal to the output unit 4 in order to notify that the dozing state is in the dozing state.

FIG. 2 shows the drowsiness level F (dotted line) of the driver during driving, the characteristic amount C (solid line) when there is no precipitation, the characteristic amount C ′ (dashed line) when there is precipitation, and the threshold TH _{2 of the} basic value. An example of a time change between the (solid line) and the threshold value TH ₂ ′ (broken line) of the correction value is shown. The sleepiness level F is a sleepiness level obtained by sensory evaluation of sleepiness based on a driver's face image, and is expressed in six stages from D0 to D5, with D0 being the weakest (state of highest arousal). Yes, D5 is the strongest (state with the lowest arousal level). The feature amount is a feature amount that increases when sleepiness is strong. In particular, when there is precipitation, the feature value C ′ is relatively larger than the feature value C when there is no precipitation.

When there is precipitation, the feature value C ′ increases. Therefore, if the threshold value TH ₂ is maintained at the basic value, the feature value C ′ exceeds the threshold value TH ₂ (basic value) for a predetermined time at the location indicated by the reference symbol A1, and the doze state Is detected. Although the driver's sleepiness level F is actually weak at this point, the dozing state is erroneously detected in the determination based on the threshold value TH ₂ (basic value).

Therefore, when the correction value is changed to the threshold value TH ₂ ′, the feature amount C ′ does not exceed the threshold value TH ₂ ′ (correction value) at the location indicated by the reference symbol A1, so that the dozing state is not detected. Thus, when the drowsiness level F of the driver is weak, the dozing state is not erroneously detected in the determination based on the threshold value TH ₂ ′ (correction value). On the other hand, the drowsiness level F is increased at the locations indicated by the reference signs A2 and A3, and the feature value C ′ is also increased correspondingly. At this location, the feature value C ′ is discrete from the threshold value TH ₂ ′ (correction value). The accumulated time when the time exceeds the predetermined time exceeds a predetermined time, and a dozing state is detected. Thus, when the driver's drowsiness level F actually increases, the dozing state is detected even in the determination based on the threshold value TH ₂ ′ (correction value).

Here, the target level detected as the sleepiness state is the D2 level of the sleepiness level. Therefore, at the time of rainfall when not increase the threshold TH _2, be less than drowsiness level D2 will detect a doze state. However, by increasing the threshold value TH ₂ at the time of rainfall, it is possible to suppress erroneous detection of less than drowsiness level D2.

  With reference to FIG. 1, operation | movement of the dozing detection apparatus 1 is demonstrated. In particular, the processing in the ECU 5 will be described with reference to the flowchart in FIG. FIG. 3 is a flowchart showing the flow of processing in the ECU of FIG.

  When the driver starts the engine, the dozing detection device 1 is activated. And the dozing detection apparatus 1 repeats the following operations after activation.

  The precipitation state acquisition means 3 acquires the precipitation state and transmits the acquired precipitation state to the ECU 5 as a precipitation state signal. The ECU 5 receives the precipitation state signal and stores the precipitation state data in a predetermined buffer (S1). Then, the ECU 5 determines whether or not there is currently precipitation based on the acquired precipitation state, and stores the determination result in the precipitation presence / absence determination result storage buffer 11 (S2).

The index measuring means 2 measures an index from the driver and transmits the measured value to the ECU 5 as an index signal. The ECU 5 receives the index signal and stores the index data in a predetermined buffer (S3). Thereby, time-series data of the index is generated. Then, the ECU 5, extracts the feature amount from the time series data of the index to determine the basic values of the threshold TH ₂ for determining the dozing state from the feature amount (S4).

The ECU 5 determines whether or not there is currently precipitation based on the result of the presence or absence of precipitation (S5). If it is determined that there is no precipitation at S5, the ECU 5, to accommodate dozing determination during precipitation, obtaining a correction value of the threshold TH ₂ by adding the threshold value correction amount B to the basic value of the threshold TH ₂ (S6 ).

When executing the processing in the case or S6 is determined that there is no precipitation in S5, it is determined whether or not the ECU 5, the feature amount exceeds a threshold value TH ₂ (S7). When running the process in S6 (case with precipitation), as the threshold TH ₂ is a correction value (large value), the detection level of the dozing state is stricter than usual. (Without precipitation) if not executing the processing of S6, a basic value as a threshold value TH ₂ (low value), the detection level of the dozing state is normal level. When the characteristic amount does not exceed the threshold value TH _2, ECU 5 determines that it is not the doze state (S7). If the feature amount exceeds the threshold value TH _2, the ECU 5, when the time exceeds the threshold value TH ₂ exceeds the time threshold TH ₃ is determined to dozing state detection, does not exceed the time threshold TH ₃ In this case, it is determined that the device is not in a dozing state (S7).

  The ECU 5 determines whether or not a dozing state is detected in the process at S7 (S8). If it is determined in S8 that no dozing state is detected, the ECU 5 ends the current process and performs the next process after a predetermined time has elapsed. If it is determined in S8 that the dozing state is detected, the ECU 5 transmits an output signal to the output means 4 (S9). When the output signal is received, the output means 4 performs an output for notifying that the driver is in a dozing state. By this output, the driver notices that the driver is dozing, or a person other than the driver knows that the driver is dozing and alerts the driver. As a result, the sleepiness of the driver is weakened or the driver takes a rest.

According to this drowsiness detection device 1, by making it difficult to detect a dozing state than usual when there is rain, it is possible to suppress erroneous detection of a dozing state even in a situation where a driver's feature amount is increased due to a stimulus due to precipitation. This improves the accuracy of detection of falling asleep. Further, according to the doze detecting device 1, the simple way simply by changing the threshold value TH ₂ for determination dozing it can be difficult to detect the dozing state than normal.

  As mentioned above, although embodiment which concerns on this invention was described, this invention is implemented in various forms, without being limited to the said embodiment.

  For example, in this embodiment, the present invention is applied to a drowsiness detection device that determines the drowsiness of a driver of a vehicle. However, the drowsiness of various people such as other vehicle drivers, various plant supervisors, and nighttime workers is determined. May be used to

  In the present embodiment, the present invention is applied to a device for determining doze. However, the present invention is applied to a program stored in a storage medium such as a CD-ROM or a program that can be used via a network such as the Internet. It is good also as a structure which determines a dozing by running a program on a computer.

  In the present embodiment, an example of determining a doze state using the feature amount of the heartbeat has been described. However, the sleepiness (wakefulness) obtained from brain waves, pulse waves, breathing, blinking, face and body actions, and the like is shown. A configuration may be adopted in which a dozing state is determined using feature amounts that change in response, or a configuration in which a dozing state is determined by combining several of these feature amounts may be employed. In addition, a method other than the method shown in the example can be applied to the method for obtaining the heartbeat feature amount.

In the present embodiment, the threshold TH ₂ is increased by one step depending on whether the precipitation is present or not, but the threshold TH ₂ is increased in a plurality of steps according to the amount of precipitation. It is good also as a structure to go. That is, the greater the amount of precipitation, the greater the driver's irritation and the greater the feature amount is predicted to change, so a larger value is set as the threshold correction amount B.

Also, except in this embodiment it has a configuration to make it difficult to detect the dozing state by changing the threshold value TH ₂ for the feature quantity, which changes the threshold value TH ₂ for the feature quantity as a way to make it difficult to detect a doze state also is applicable in the process, for example, may be difficult to detect the dozing state by changing the time threshold TH ₃ versus time characteristic amount exceeds the threshold value TH _2, when determining the characteristic quantity You may make it obtain | require as a feature-value which is hard to be detected as a dozing state.

In the present embodiment, the threshold TH ₂ for determining the dozing state is obtained from the feature amount. However, the threshold TH ₂ may be set in advance.

  Further, in the present embodiment, it is configured to detect whether or not it is a dozing state (a state of low arousal level), but it may be configured to estimate the arousal level (drowsiness level) step by step.

It is a block diagram of the dozing detection apparatus which concerns on this Embodiment. It is a graph which shows an example of a drowsiness level, the magnitude | size of the feature-value for dozing determination in the case where there is precipitation, and the case where there is no precipitation, and the threshold value for dozing determination. It is a flowchart which shows the flow of the process in ECU of FIG.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 ... Dozing detection apparatus, 2 ... Index measurement means, 3 ... Precipitation state acquisition means, 4 ... Output means, 5 ... ECU, 10 ... Presence / absence judgment part, 11 ... Presence / absence judgment result storage buffer, 12 ... Feature quantity extraction part , 13 ... Precipitation presence / absence determination result determination part, 14 ... Dozing detection part, 15 ... Precipitation corresponding part, 16 ... Dozing detection existence determination part, 17 ... Dozing output part

Claims (1)

A wakefulness estimation device for estimating a subject's wakefulness,
Feature quantity extraction means for extracting feature quantities that change according to the degree of arousal of the subject;
Estimating means for estimating the arousal level of the subject based on the feature amount extracted by the feature amount extracting means;
A precipitation state detecting means for detecting the precipitation state,
The estimation means makes it easier to estimate that a subject's arousal level is higher when there is a lot of precipitation detected by the precipitation state detection means than when the precipitation level is low.

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