JPH11151230A - Driver state measuring instrument for vehicle - Google Patents

Driver state measuring instrument for vehicle

Info

Publication number
JPH11151230A
JPH11151230A JP9333749A JP33374997A JPH11151230A JP H11151230 A JPH11151230 A JP H11151230A JP 9333749 A JP9333749 A JP 9333749A JP 33374997 A JP33374997 A JP 33374997A JP H11151230 A JPH11151230 A JP H11151230A
Authority
JP
Japan
Prior art keywords
driver
data
driving
vehicle
condition
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
JP9333749A
Other languages
Japanese (ja)
Inventor
Tatsumi Yanai
達美 柳井
Original Assignee
Nissan Motor Co Ltd
日産自動車株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nissan Motor Co Ltd, 日産自動車株式会社 filed Critical Nissan Motor Co Ltd
Priority to JP9333749A priority Critical patent/JPH11151230A/en
Publication of JPH11151230A publication Critical patent/JPH11151230A/en
Withdrawn legal-status Critical Current

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Classifications

    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/16Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/18Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state for vehicle drivers or machine operators

Abstract

PROBLEM TO BE SOLVED: To improve the reliability of the physical state measurement data of a vehicle driver, to store the measurement data for respective drivers and traveling condition sections and to retrieve the measurement data of a previous time. SOLUTION: The driver presses the touch panel switch of a driver specifying device 1 and inputs driver information and a traveling condition detector 2 prepares and outputs a section table composed of a time band section, a view field section, an average car speed section and a road kind section. In an electrocardiogram signal detector 3, the electrocardiogram signals of the driver are obtained from an electrode on a steering. In a pulsating interval variance calculation circuit 4, defective data including detection errors and noise are eliminated, a pulsating interval variance and an average heart rate are calculated from the electrocardiogram signals and a centroid position on a two-dimensional plane is obtained. In a mental fatigue level judgement circuit 5, the measured centroid position and the centroid position of the previous time in the same section of the same driver stored in a memory 6 are compared and a mental fatigue level is judged. The measured centroid position is stored in the memory 6 along with the driver information and the section table.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a vehicular driver condition measuring apparatus for measuring a vehicle driver's physical condition.

[0002]

2. Description of the Related Art Conventionally, as shown in FIG. 10, there is shown an electrocardiographic signal detecting apparatus comprising a positive electrode 22 and a negative electrode 23 arranged on a steering 21 and a detecting circuit 24, as shown in FIG. There is a vehicular driver state measuring device that measures the driver's electrocardiographic signal 25, calculates the standard deviation of the pulsation interval, and stores it in a storage device. The data stored in the storage device is read out as needed and used for determining the degree of fatigue of the driver.

[0003]

However, the conventional vehicle driver condition measuring device has no means for specifying the driver, and can be used only for measurement on a commercial vehicle in which the driver is specified. In addition, even in the measurement of the same driver, for example, when driving at high speed at night when it is raining, and when driving at low speed during the daytime when the weather is good, the driver's state is significantly different. However, since the running state of the vehicle is unknown, there is a problem that the measured driver state data cannot be fully utilized even in the determination of the degree of fatigue or the like.

[0004] Further, since an electrocardiographic signal is detected from the steering wheel, a detection error occurs due to the influence of a myoelectric potential generated when the driver releases the steering wheel while the vehicle is running, or when the hand is moved, or an electric signal is generated. There is a problem that noise may be generated, and the driver state data including such defect data is stored in the storage device as it is. In view of the above problems, the present invention measures a vehicle driver's biological signal, improves the reliability of driver state data obtained from the biological signal, stores the data in association with the driver and the driving situation classification, and It is an object of the present invention to provide a vehicle driver condition measuring device capable of retrieving stored data for each driver and driving condition category and utilizing the measured driver condition data sufficiently.

[0005]

SUMMARY OF THE INVENTION Accordingly, the present invention provides a driver specifying device for specifying a vehicle driver and a driving condition detecting device for detecting a driving condition of a vehicle and classifying the driving condition based on the driving condition. A driver state data detecting unit that measures a driver's biological signal and outputs driver state data based on the biological signal, and driver state data detected by the driver state data detecting unit. It is assumed that the apparatus includes a driver state storage unit that stores, for each driver specified by the apparatus, the state of the driver in association with the driving state classification classified by the driving state detecting apparatus.

[0006] The traveling condition detecting device may include a time period dividing means for measuring a time period during traveling and classifying the traveling condition based on the measured time period. In addition, the traveling condition detection device may include a visibility dividing unit that measures the traveling visibility and classifies the traveling condition based on the measured visibility. The traveling condition detection means may include an average vehicle speed classification means for measuring the average traveling vehicle speed and classifying the traveling condition based on the measured average vehicle speed. Further, the driving situation detecting means may include a road type dividing means for detecting a road type and classifying the driving situation based on the measured road type.

It is preferable that the driver state data detecting means has a defect data removing means for removing defect data including a detection error and electrical noise from the driver state data. The driver status storage means has the same condition data search means for searching the detected driver status data and the driver status data stored in association with the same driving situation category of the same driver. The driver state storage means stores the driver state data in association with the section specified by the driver specifying device and the section determined by the driving state detecting means, and stores the driver state data already stored in the corresponding section. There may be provided driver status data updating means for deleting driver status data and storing newly detected driver status data.

[0008]

In the vehicle driver state measuring device according to the present invention,
Even if the vehicle is driven by multiple drivers as needed,
By specifying an individual driver from the driver specifying device, it is possible to measure driver state data for each driver. Also, by storing the driver status data in association with the driver information and the driving status category at the time of measuring the data, the driving status can be considered when using the driver status data.

In accordance with the purpose of use of the measured driving state data, a division based on the traveling time zone, a division based on the traveling visibility, a division based on the traveling vehicle speed, and a division based on the road type can be used alone or appropriately in combination. By dividing the driving conditions, the driving conditions can be clearly classified. Further, by removing defect data including a detection error and electrical noise from the driver state data, accurate driver state data can be collected.

Further, the detected driver status data is stored in association with the driving situation category, and when new driver status data is detected, the driver which has been detected and stored by the same driver until the previous time is stored. From the state data, the driver state data stored in association with the same driving state division as the driving state division newly measured is read out and compared, so that individual differences between individual drivers and the driving state It is possible to know a change in the driver's state in accordance with the change. When storing the detected driver status data, by deleting the previously detected driver status data stored in the same driving situation category of the same driver, and storing the newly detected driver status data, The latest driver status data can always be stored.

[0011]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of the present invention will be described with reference to examples. FIG. 1 is a configuration diagram of a first embodiment of the present invention applied to a vehicle mental fatigue degree determination device that determines the degree of mental fatigue of a vehicle driver. Here, an electrocardiographic signal of a specific driver is detected, and a pulsation interval variance value and an average heart rate 2
The change in the degree of mental fatigue is determined by calculating the position of the center of gravity of the dimensional data and comparing the calculated position with the center of gravity measured last time for each running condition of the vehicle.

A vehicle mental fatigue degree determining device includes a driver specifying device 1 for specifying a driver at the time of measurement from a plurality of registered drivers, and a driving condition detecting device for detecting a driving condition of the vehicle. 2, an electrocardiographic signal detection device 3 for detecting an electrocardiographic signal which is a biological signal of the driver, and a pulse for calculating two-dimensional data comprising a variance value of a beat interval and an average heart rate from the electrocardiographic signal of the driver. A motion interval variance value calculation circuit 4, which is connected to the driver specifying device 1, the driving situation detection device 2, and the pulsation interval variance value calculation circuit 4, includes a memory 6, and determines a change in the degree of mental fatigue of the driver. It comprises a mental fatigue degree determination circuit 5.

The driver identification device 1 has a touch panel type switch arranged near the front panel, and different drivers are registered in advance for each switch. When the driver presses a registered switch, the driver information is output to the mental fatigue degree determination circuit 5. In the traveling situation detecting device 2, a time zone division is set based on traveling time information input from a clock, a visibility division is set based on wiper operation information, and an average is set based on distance information traveled within a predetermined time. The vehicle speed division is set, the road type division is set based on the position information measured by the navigation system, and a division table including each division is output to the mental fatigue degree determination circuit 5.

As shown in FIG. 2, the electrocardiogram signal detecting device 3 comprises a steering right half portion 11 and a steering upper left half portion 1 each comprising three insulated conductive members.
2. Positive pole 14 and negative pole 1 installed on steering wheel 10 composed of lower left half 13 of steering wheel
An electrocardiographic signal is collected from the driver's hand by the detection circuit 5 and the reference electrode 16 and the detection circuit 17 connected to each electrode. Compared to the ECG signal as shown in Fig. 3 (a) obtained by attaching electrodes to the chest, the ECG signal collected from the palm has a baseline fluctuation observed as shown in Fig. 3 (b). However, the timing of the pulsation can be sufficiently detected.

Further, by adding a reference pole 16 in addition to the positive pole 14 and the negative pole 15, the baseline fluctuation of the electrocardiographic signal can be suppressed as much as possible. The steering wheel 10 is configured so that the driver's palm naturally comes into contact with the three poles while holding the normal steering wheel. In the case of ECG signals, electrodes need to be attached to the body, but since accurate waveform diagnosis for medical use is not the purpose here, the driver's ECG signals are measured from the electrodes attached to the steering wheel. I have.

The pulsation interval variance calculation circuit 4 calculates the RRI, which is the pulsation time interval, from the electrocardiogram signal collected from the driver.
(RR interval) data is detected and the RRI
It calculates and outputs two-dimensional data including the normalized variance RRV of the data and the average heart rate BEAT. The mental fatigue degree determination circuit 5 determines a change in the degree of mental fatigue of the driver identified by the driver identification apparatus 1 from the outputs of the driver identification device 1, the driving situation detection device 2, and the pulsation interval variance value calculation circuit 4. I do.

Next, the operation of the entire apparatus will be briefly described first. The driver presses a previously registered switch on the driver identification device 1 before starting driving the vehicle. The running situation detection device 2 creates a section table based on the four sections and outputs the table to the mental fatigue degree determination apparatus 5. First, the traveling time is detected, and a daytime or nighttime time zone is set. At night there is a tendency for awakening to decrease, and there are statistics that the incidence of single vehicle accidents increases extremely.Compared to daytime, vehicle driving requires more concentration on driving, and the driving situation is called "night" and " Daytime ". Next, the operation information of the wiper is input as the visibility information, and "bad" is set as the visibility division.
Or set "Good". If it's raining,
In addition to obstructing the field of view, slippage is also likely to occur, which is considered to be appropriate as a condition for dividing the driving situation.

Further, since there is a difference in the impact at the time of the accident depending on the average vehicle speed, the average vehicle speed in a relatively short time is measured, and the average vehicle speed classification is "50 km / h or more" and "50 km / h".
Less than ". Furthermore, when traveling on a highway where constant speed driving is often required and a certain level of tension is always required,
Obtain location information from the navigation system to distinguish between traveling on general roads where the degree of tension must be changed in response to various situation changes, and whether the vehicle is traveling on general roads or highways. Is determined, and the road type is classified into “expressway” and “general road”.

When the driver grips the steering wheel, an electrocardiographic signal of the driver as shown in FIG. 3B is measured by the electrodes of the electrocardiographic signal detecting device 3 provided on the steering wheel, and the pulsation interval is measured. Output to the variance value calculation circuit 4. The pulsation interval variance calculation circuit 4 calculates the pulsation interval RRI (RR) from the electrocardiogram signal.
interval), and accumulates RRI data for 30 seconds. Since a driver collects an electrocardiographic signal from an electrode on a steering wheel during a driving operation, a defect may occur due to a detection error, or an abnormal pulse may occur due to electric noise or the like.

In the pulsation interval calculation circuit 4, the pulsation interval RRI
Is calculated, if data greatly deviating from the RRI value calculated immediately before is detected, the accumulated RRI data including the RRI data is removed, and the beat interval RRI with high accuracy is calculated. Next, a normalized variance RRV and an average heart rate BEAT are calculated from the beat intervals RRI. It has been experimentally confirmed that the degree of mental fatigue and the degree of variation in RRI data have a close correlation. Here, the normalized variance RRV and the average heart rate BEAT are used as indices representing this variation. The calculated normalized variance RRV and average heart rate BEAT are output to the mental fatigue degree determination circuit 5 as RRV-BEAT two-dimensional data.

The mental fatigue degree determination circuit 5 stores the center of gravity of the RRV-BEAT two-dimensional data measured this time and the memory 6
Is compared with the position of the center of gravity of the previous measurement result in the same driver and the same traveling condition stored in the memory. In general, the RRV-BE is used when a driving operation requiring tension such as high-speed driving is performed at night and when low-speed driving is performed during daytime.
It is known that a difference occurs in the center of gravity of the AT two-dimensional data, and that there is an individual difference in the center of gravity of the RRV-BEAT two-dimensional data.

However, when the same driver drives in the same driving condition, the position of the center of gravity is unlikely to be significantly different in a normal state, and mental fatigue is accumulated by driving for a long time, and the awakening degree of the driver is reduced. RRV-BE
The position of the center of gravity of the AT two-dimensional data is greatly shifted. The mental fatigue determination circuit 5 determines a change in the degree of mental fatigue by comparing the position of the center of gravity of the RRV-BEAT two-dimensional data for each specified driving condition of the driver.

Next, the flow of operation in this embodiment will be described in more detail. First, the operation of the traveling situation detecting device 2 will be described with reference to the flowchart shown in FIG. In step 101, when the vehicle starts running, every predetermined time,
Input the time information when driving from the clock attached to the cabin. Also, time information obtained from the navigation system can be used, so that accurate traveling time can be obtained.
In step 102, it is determined from the time information whether or not the running time is during the day. If it is daytime, the process proceeds to step 103, and "daytime" is set as the time zone division. If it is not daytime, the process proceeds to step 104, and "nighttime" is set as a time zone division.

In step 105, wiper operation information is input to determine the field of view. In the case where the view is obstructed by rain, the driver can determine the view from the operation information of the wiper to operate the wiper. In step 106, it is determined whether or not the wiper is operating. If the wiper is operating, the process proceeds to step 107, where "defective" is set as the field of view. If it has not been activated, proceed to step 108,
Set "Good".

In step 109, travel distance information for each predetermined time is input. In step 110, the average vehicle speed within a predetermined time is calculated from the distance information, and the average vehicle speed is 50 km /
h is determined to be greater than or less than h. If it is 50 km / h or more, the routine proceeds to step 111, where the average vehicle speed classification is "5
0 km / h or more "is set. Average vehicle speed is 50km /
If it is less than h, the routine proceeds to step 112, where "less than 50 km / h" is set in the average vehicle speed division.

In step 113, position information is input from the navigation system. In step 114, it is determined from the position information whether the traveling road is an expressway. If it is a highway, the process proceeds to step 115, where "highway" is set as the road type classification. If it is not an expressway, the process proceeds to step 116, and “general road” is set as the road type classification. In step 117, these classification results are output to the mental fatigue degree determination circuit 5 in the form of a classification table shown in FIG.

Next, a flow of an operation of calculating and outputting a two-dimensional data set as driver state data from detection of an electrocardiographic signal as a driver's biological signal will be described with reference to a flowchart shown in FIG. In step 121, the electrocardiographic signal of the driver is measured by the electrocardiographic signal detecting device 3 and output to the pulsation interval variance calculating circuit 4. In step 122,
The pulse interval variance value calculation circuit 4 samples the electrocardiographic signal at 100 Hz or more and sequentially captures it as heartbeat equivalent data. Next, in step 123, heartbeat-equivalent data from which undesired signals such as noise have been removed is obtained by filtering or thresholding using a bandpass filter or a matched filter.

At step 124, from the heartbeat equivalent data,
Beat interval RRI (R-R int) which is the time interval of beats
erval). However, since the RRI data originally fluctuates, the RRI data must be irregularly sampled in time series. However, if necessary, regular sampling is performed in time series by interpolation. It is also possible to use a simple sampling. In step 125, the detected RRI data is accumulated.

At step 126, the beat time interval RRI
It is checked whether the data is within a predetermined range. Here, when detecting the new data RRI (1), the immediately preceding data RRI (0) is referred to, and the (1/2) R
A range from RI (0) to (3/2) RRI (0) is set as a detection range. If the pulsation is lost due to arrhythmia or a detection error, the RRI becomes about twice the normal value, so that it can be detected by this check. Also, due to pulses due to electrical noise,
Even when incorrect RRI data is detected, it can be detected by this check. That is, when the RRI (1) is within the above range, the process directly proceeds to step 128, but when the RRI (1) is not within the above range, it is regarded that the defect data has been detected, and
Proceeding to step 127, the stored RRI data is deleted, and the process returns to step 122 to perform new sampling.

In step 128, it is determined whether or not the accumulated RRI data exists for 30 seconds. If the time is not enough for 30 seconds, the process returns to step 122 to continue sampling, and from step 122 to step 12 until RRI data continuous for 30 seconds is accumulated.
Repeat up to 8. When the RRI data for 30 seconds has been accumulated, the process proceeds to step 129.

In steps 129 to 131,
The degree of variation in the RRI data is calculated. First, in step 129, the first 15 data points are cut out from the RRI data for 30 seconds as an analysis section. Next, in step 130, the normalized variance RRV and the average heart rate BEAT are calculated.

Next, the routine proceeds to step 131, where it is determined whether or not RRI data remains. If the RRI data remains, the process proceeds to step 132, where the analysis section is shifted by one point width, and then the processes of steps 129 to 131 are repeated. Here, the reason why the analysis section is shifted by one point width is that when analyzing the RRI data in a time series manner, it is possible to perform the analysis in a point-by-point manner in the most detailed manner. RR
If no I data remains, the process proceeds to step 133.
Next, in step 133, as driver state data,
The two-dimensional data on the two-dimensional plane of the normalized variance RRV and the average heart rate BEAT is obtained, and output to the mental fatigue degree determination circuit 5 as an RRV-BEAT two-dimensional data set.

Next, the operation flow of the mental fatigue degree determination circuit 5 will be described with reference to the flowchart shown in FIG.
First, in step 141, driver identification information is input from the driver identification device 1. In step 142, the RRV-BEAT two-dimensional data set is input from the beat interval variance value calculation circuit 4. In step 143, the driving situation detecting device 2
, Enter the driving situation classification table. Step 14
In step 4, the RRV-BEAT input in step 142
The position of the center of gravity of the two-dimensional data set is calculated.

In step 145, it is determined whether or not the center of gravity of the RRV-BEAT two-dimensional data set of the same driver in the same traveling section is stored in the memory 6. If it is stored, the process proceeds to step 146. If it is not stored, since this time is the first measurement, there is no value to be compared in the determination of the degree of mental fatigue, and it is impossible to determine. Therefore, the process proceeds from step 145 to step 149, and the current measurement value is set as the center of gravity position. The information is stored in the memory 6 in combination with the driver identification information and the classification table of the driving situation. Step 146
Then, the center of gravity of the RRV-BEAT two-dimensional data set in the same traveling section of the same driver stored in the memory 6 is read.

In step 147, the measured RRV-B
The center of gravity of the EAT two-dimensional data set is compared with the center of gravity of the previously measured RRV-BEAT two-dimensional data set stored in the memory 6 to determine a change in the degree of mental fatigue of the driver. The center of gravity position of the RRV-BEAT two-dimensional data set measured this time is the RRV-BEA previously measured in the same traveling section of the same driver stored in the memory 6.
If it is within a predetermined range from the position of the center of gravity of the AT two-dimensional data set, the degree of mental fatigue is determined to be unchanged from the previous time, and if it is outside the predetermined range, it is determined that there is a change in the degree of mental fatigue.

In step 148, the position of the center of gravity of the previously measured RRV-BEAT two-dimensional data set stored in the memory 6 is deleted. In step 149, the center of gravity of the RRV-BEAT two-dimensional data set measured this time is stored in the memory 6 in combination with the driver identification information and the driving condition classification table, and the process returns to step 142 to wait for the next input.

Steps 101 to 104 of the flow chart shown in FIG. 4 are time zone dividing means of the invention, steps 105 to 108 are view dividing means, steps 109 to 112 are average vehicle speed dividing means, and step 113 is step 113. Step 116 constitutes a road classification unit. Steps 121 to 133 of the flowchart shown in FIG. 6 constitute a driver state data detecting means of the present invention, and particularly, steps 126 and 127 constitute a defect data removing means of the present invention. Step 14 of the flowchart shown in FIG.
5, 148 and 149 constitute the driver status storing means of the present invention. In particular, step 145 constitutes the same condition data retrieving means, and steps 148 and 149 constitute the driver status data updating means.

As described above, according to the present embodiment, first, the driver is specified by the driver specifying device 1.
Even if the vehicle is driven by a plurality of drivers as needed, driver data can be measured for each individual driver. In addition, in the pulsation interval variance calculation circuit 4, accurate driver state data can be collected by removing defect data including detection errors and electrical noise from the driver state data, thereby improving measurement accuracy. I do. In addition, the driver state data is divided into a section based on the driver information and the driving time zone input from the driver identifying device 1, a section based on the traveling field of view, a section based on the traveling vehicle speed, and a section based on the road type. By storing in combination with the classification table including the driving condition, the driving condition can be clearly classified, the driving condition can be considered when using the driver state data, and effective utilization becomes possible.

Further, the detected driver state data is stored in combination with the driver information and the driving situation classification table, and when new driver state data is detected, the same driver is detected by the previous time. From the stored driver state data, retrieve and read the driver state data stored in combination with the same division table as the division table of the driving situation when the driver state data is newly measured. By comparison, the change in the degree of fatigue of the driver can be known. Further, when storing the detected driver status data, the previously detected driver status data stored in combination with the same classification table of the same driver is deleted, and the newly detected driver status data is stored. Thus, the detected driver state data can always be compared with the driver state data detected last time, and the driver can reliably grasp the change in the degree of fatigue.

Next, a second embodiment of the present invention will be described. In this embodiment, the number of sections in the section table detected by the driving situation detecting device 2 'is increased, and a mental fatigue degree determination circuit 5' is provided.
Is an example in which the detected driver state data is compared with the average value of the same driver identical classification table stored up to the previous time stored in the memory 6 ', and other configurations are the same as those of the first embodiment. (See FIG. 1). FIG. 8 is a diagram illustrating a partition table according to the present embodiment. In the driving situation detecting device 6 ', a segmenting table in which the traffic information segment and the driving situation segment are added to the segmenting table used in the first embodiment shown in FIG. 3 is created and output to the mental fatigue degree judging device 5'. Is done.

First, as the traffic information division, when there is a change in the traffic condition ahead, the driver is required to be in a high tension state in order to cope with the change, so that the normal driving situation is continued. "Continuation" and "change" when VICS information (road traffic information system) is obtained from the navigation system and there is a sudden congestion, an accident or a faulty car ahead, or when it is determined that the vehicle is approaching an intersection or junction. ]. Next, the driving situation is classified into "severe" and "calm" based on the steering and accelerator operation information. Even for the same driver, the driving conditions are different depending on whether there is enough time or the like, and it is considered that the classification based on the driving conditions is also effective.

FIG. 9 is a flowchart showing the flow of the mental fatigue degree determination operation in this embodiment. Step 14 of the flowchart in the first embodiment shown in FIG.
Steps 151 to 155 are executed instead of steps 5 to 149. First, in step 141 and step 142, the driver identification information and the RRV-BE
The AT two-dimensional data set is input, and in step 143, the travel classification table shown in FIG. 8 created by the travel situation detecting device 2 'is input. In step 144, the position of the center of gravity of the RRV-BEAT two-dimensional data set input in step 142 is calculated.

In step 151, it is determined whether or not the average position of the center of gravity of the RRV-BEAT two-dimensional data set of the same driver in the same section table is stored in the memory 6 '. If it is stored, the process proceeds to step 152. If it is not stored, this time is the first measurement, and there is no value to be compared in the determination of the degree of mental fatigue. Therefore, since it is impossible to make a determination, the process proceeds from step 151 to step 154, and the current measurement value is stored in the memory 6 'as an average center of gravity position in combination with the driver identification information and the division table. In step 152, the average gravity center position of the RRV-BEAT two-dimensional data set stored in the memory 6 'for the same driver in the same traveling section is read.

In step 153, the measured RRV-B
The center of gravity of the EAT two-dimensional data set and the RRV-BEAT2 measured up to the previous time stored in the memory 6 '
The degree of mental fatigue of the driver is determined by comparing with the average position of the center of gravity of the dimensional data set. RRV-BEAT measured this time
If the position of the center of gravity of the two-dimensional data set is within a predetermined range from the average position of the center of gravity of the RRV-BEAT two-dimensional data set previously measured in the same driving section of the same driver stored in the memory 6 'up to the previous time, mental fatigue The degree is determined to be unchanged from the driver's normal state, and if it is outside the predetermined range, the degree of mental fatigue is determined to be different from the normal state.

In step 154, the RRV-BEA measured this time is added to the average gravity center position of the RRV-BEAT two-dimensional data set measured up to the previous time stored in the memory 6 '.
A new average center-of-gravity position is calculated by adding the center-of-gravity position of the two-dimensional data set. In step 155, step 154
Is stored in the memory 6 'in combination with the driver specific information and the driving condition classification table.

In this embodiment, by creating a section table in which traffic information sections and driving situation sections are added,
The driver's state data can be classified according to more detailed classified driving situations, and more effective use becomes possible. In addition, by comparing the center of gravity position of the measured RRV-BEAT two-dimensional data set with the average center of gravity position of the RRV-BEAT two-dimensional data set thus far measured, the driver's state at the time of measurement can be compared with a normal state, If the driver's degree of fatigue deviates from a normal state, it can be determined immediately.

In each of the above embodiments, a touch panel type switch is used as the driver identification device. However, the present invention is not limited to this. Individual recognition by signal pattern recognition is also possible, as long as the individual can be easily identified.

[0048]

As described above, in the vehicle driver condition measuring apparatus according to the present invention, even if the vehicle is driven by a plurality of drivers as required, each driver can be identified by the driver specifying apparatus. By specifying, driver state data for each driver can be measured. In the present invention, by storing the driver status data in association with the driver information and the driving status category at the time of measuring the data, the driving status can be considered when using the driver status data, which is effective. Utilization becomes possible.

In accordance with the purpose of use of the measured driving state data, a division based on the traveling time zone, a division based on the traveling visibility, a division based on the traveling vehicle speed, and a division based on the road type can be used alone or in appropriate combination. By dividing the driving conditions, the driving conditions can be clearly classified, and more appropriate driver state data can be used. Further, by removing defect data including a detection error and electric noise from the driver state data, accurate driver state data can be collected, and the reliability of the driver state data can be improved.

Further, for example, the detected driver status data is stored in association with the driving situation category, and when new driver status data is detected, the driving status detected and stored by the same driver up to the previous time is stored. From the driver status data, the driver status data stored in association with the same driving status category as the newly measured driving status category is read out and compared, so that individual differences and driving status of individual drivers can be obtained. It is possible to know the change in the driver's state in accordance with the change in the driver. When storing the detected driver status data, the previously detected driver status data stored in the same driving situation category of the same driver is deleted, and the newly detected driver status data is stored. The latest driver status data can be stored, and more effective use is possible.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a configuration of a first exemplary embodiment of the present invention.

FIG. 2 is a diagram illustrating a configuration of an electrocardiographic signal detection device.

FIG. 3 is a diagram showing an example of an electrocardiographic waveform.

FIG. 4 is a flowchart illustrating a flow of a driving situation detection operation in the first embodiment.

FIG. 5 is a diagram illustrating a partition table according to the first embodiment.

FIG. 6 is a flowchart illustrating an operation flow of calculating a pulsation interval variance value from an electrocardiographic signal in the first embodiment.

FIG. 7 is a flowchart showing a flow of a procedure for determining a degree of mental fatigue in the first embodiment.

FIG. 8 is a diagram illustrating a partition table according to the second embodiment.

FIG. 9 is a flowchart illustrating a flow of a procedure for determining a degree of mental fatigue according to the second embodiment.

FIG. 10 is a configuration diagram of a conventional example.

FIG. 11 is a diagram showing an example of an electrocardiographic waveform.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Driver identification device 2, 2 'Running condition detection device 3 Electrocardiogram signal detection device 4 Beating interval dispersion value calculation circuit 5, 5' Mental fatigue degree determination circuit 6, 6 'Memory 10, 21 Steering 11 Steering right half part 12 Upper left half of steering 13 Lower left half of steering 14, 22 Positive pole 15, 23 Minus pole 16 Reference pole 17, 24 Detection circuit 25 Electrocardiogram signal

Claims (8)

[Claims]
1. A driver specifying device for specifying a vehicle driver, a driving condition detecting device for detecting a driving condition of a vehicle and classifying the driving condition based on the driving condition, and measuring a biological signal of the driver. Driver status data detecting means for outputting driver status data based on the biological signal; and driver status data detected by the driver status data detecting means for each driver specified by the driver specifying device. A driver status storage device for storing the driver status storage means for storing the driving status in association with the driving status classification divided by the driving status detecting device.
2. The driving condition detecting device according to claim 1, further comprising a time period dividing unit that measures a time period during traveling and classifies the traveling condition based on the measured time period.
The vehicle driver state measuring device according to any one of the preceding claims.
3. The vehicle driver according to claim 1, wherein the driving situation detection device includes a visibility dividing unit that measures a traveling visibility and classifies the traveling situation based on the measured visibility. Condition measuring device.
4. The vehicle according to claim 1, wherein said traveling condition detecting means includes an average vehicle speed dividing means for measuring an average traveling vehicle speed and classifying traveling conditions based on the measured average vehicle speed. Driver status measurement device for vehicles.
5. The driving condition detecting device according to claim 1, further comprising a road type classifying device for detecting a road type and classifying the driving condition based on the measured road type.
5. The vehicle driver state measuring device according to 2, 3, or 4.
6. The apparatus according to claim 1, wherein said driver state data detecting means includes defect data removing means for removing defect data containing a detection error or electrical noise from said driver state data. The vehicle driver state measuring device according to any one of claims 3, 4, and 5.
7. The driver condition storage means includes a same condition data search means for searching the detected driver state data and the driver state data stored in association with the same driving situation category of the same driver. The vehicle driver condition measuring device according to claim 1, 2, 3, 4, 5, or 6, further comprising:
8. The driver status storage means, when storing the driver status data in association with a category specified by the driver specifying device and determined by the driving situation detection means, corresponds to the driver status data. 5. The apparatus according to claim 1, further comprising driver status data updating means for deleting the driver status data stored in the section and storing the newly detected driver status data. 8. The vehicle driver state measuring device according to 6 or 7.
JP9333749A 1997-11-19 1997-11-19 Driver state measuring instrument for vehicle Withdrawn JPH11151230A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP9333749A JPH11151230A (en) 1997-11-19 1997-11-19 Driver state measuring instrument for vehicle

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
JP9333749A JPH11151230A (en) 1997-11-19 1997-11-19 Driver state measuring instrument for vehicle

Publications (1)

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JPH11151230A true JPH11151230A (en) 1999-06-08

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JP9333749A Withdrawn JPH11151230A (en) 1997-11-19 1997-11-19 Driver state measuring instrument for vehicle

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CN105975106A (en) * 2016-04-28 2016-09-28 大连海事大学 Intelligent health mouse and hand health massage method thereof
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