JPH10198900A - Driving state supervisory device for vehicle - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a driving state supervisory device for vehicle that more accurately grasps the behavior of a vehicle with a relatively simple operation and can improve the accuracy of judging driver's driving state. SOLUTION: A yaw angle YA is calculated from a detected yaw rate YR and a modified yaw angle YRM is calculated (S12) by extracting the high frequency component. Lateral displacement differential quantity DYK is calculated (S13) from the modified yaw angle YAM, and furthermore, deviation quantity ΔDIF1 is calculated (S15) by performing time integration of the absolute value of the quantity DYK. When the quantity ΔDIF1 is prescribed deviation quantity ΔDIFLIM1 or more, it is judged that a driving state is abnormal, and an alarm is issued (S18).

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a driving condition monitoring device for a vehicle for monitoring a driving condition of a driver of a vehicle.

[0002]

2. Description of the Related Art A response delay time of a driver and a deviation amount between a vehicle position and a traveling lane are estimated based on a steering amount and a vehicle speed of a vehicle, and the estimated response delay time and the deviation amount are determined in a normal state. A driving condition monitoring device that compares a response delay time and a deviation amount to determine a driving condition of a driver (for example, an abnormal steering state due to a decrease in driving ability due to a driver falling asleep or fatigue) has been conventionally used. It is known (JP-A-5-85221).

[0003]

However, in the above conventional monitoring apparatus, the deviation between the actual vehicle position and the traveling lane (reference vehicle position) is calculated based on the steering amount and the vehicle speed. Since the deviation amount is not calculated based on the physical quantity directly related to the behavior of the vehicle, for example, it may be due to the road surface condition (for example, unevenness or inclination of the road surface) or the individual difference of the driver (for example, whether or not a beginner). Therefore, there is a problem that an error occurs in the deviation amount and the accuracy of determining the driving situation of the driver is reduced.

[0004] In order to solve this problem, the present applicant has already proposed a driving situation monitoring device which determines a driving situation using a parameter representing the behavior of a vehicle and a behavior reference which is a reference for this parameter. (For example, Japanese Patent Application Hei 7
In this device, there is a problem that the amount of calculation for calculating the behavior criterion is large.

The present invention has been made in view of this point, and is intended for a vehicle which can more accurately grasp the behavior of the vehicle by relatively simple calculation and improve the accuracy of determining the driving situation of the driver. An object of the present invention is to provide an operation status monitoring device.

[0006]

SUMMARY OF THE INVENTION In order to achieve the above object, the present invention relates to a vehicle driving condition monitoring device for monitoring a driving condition of a driver of a vehicle, wherein a driving condition value corresponding to the behavior of the vehicle is detected. Operating condition value detecting means, extracting means for extracting a high-frequency component of the detected operating condition value, and determining means for determining whether or not the driving condition of the driver is appropriate based on the extracted component. It is characterized by.

According to the present invention, a driving situation value corresponding to the behavior of the vehicle is detected, a high-frequency component of the detected driving situation value is extracted, and whether the driving situation of the driver is appropriate based on the extracted component is determined. It is determined whether or not.

[0008]

Embodiments of the present invention will be described below with reference to the drawings.

FIG. 1 is a block diagram showing a configuration of a vehicle operating condition monitoring apparatus according to a first embodiment of the present invention. The apparatus is driven by a prime mover such as an internal combustion engine or an electric motor and has a steering. Mounted on the vehicle.
In the figure, the input side of the microcomputer 1 has:
Yaw rate sensor 10 for detecting the yaw rate of the vehicle
And a vehicle speed sensor 12 for detecting a running speed of the vehicle. The output side of the microcomputer 1 is connected to an alarm unit 24 that issues an alarm when necessary while monitoring the driving condition of the driver. The alarm unit 24 includes, for example, a lamp, a buzzer, and a sound generator.

Signal memory unit 1 of microcomputer 1
4. High frequency component extraction unit 16, lateral displacement amount differential amount calculation unit 1
8. The deviation amount calculation unit 20 and the determination unit 22 show the functions of the microcomputer 1 as blocks.

The signal memory unit 14 includes the sensors 10 and 12
The input signal from the switch 11 is stored, and the yaw rate data and the vehicle speed data for the past T1 seconds (for example, 30 seconds) from the present are updated every T2 seconds (for example, 10 seconds) and output to the high frequency component extraction unit 16. High frequency component extraction unit 16
Converts the input yaw rate YR (see FIG. 2A) over time and converts it into a yaw angle YA (see FIG. 2B).
Further, a high frequency component of the yaw angle YA (for example, 0.1H
A process of extracting components of z or more) is performed. This process is performed by, for example, extracting a low-frequency component by a well-known smoothing operation or averaging operation, and subtracting it from the original data.
Then, the processed data is converted into a corrected yaw angle YAM (FIG. 2C).
(See Reference) is output to the lateral displacement differential amount calculation unit 18. This processing focuses on a point that appears as a low-frequency component of the yaw angle data when the road on which the vehicle is traveling is curved and the yaw angle YA increases, whereby Accurate driving condition detection can be performed while eliminating the influence of road bends.

The lateral displacement differential amount calculating section 18 calculates the corrected yaw angle Y
AM and the vehicle speed V are applied to the following equation to calculate the lateral displacement differential amount DYK.
(See FIG. 2D). This lateral displacement differential amount DY
K represents a temporal change rate of the lateral displacement amount of the vehicle from which the influence of the turning of the traveling road is removed, and is a parameter that increases when the driving situation is abnormal.

The deviation amount calculating section 20 calculates a lateral displacement differential amount DYK
The deviation amount ΔDIF1 is calculated based on Deviation ΔDI
F1 is calculated, for example, as the area of the portion hatched in FIG. 2D (time integral of the absolute value of the lateral displacement differential amount DYK), and the standard deviation of the DYK value or the difference between the maximum value and the minimum value. May be used.

When the deviation .DELTA.DIF1 is equal to or larger than the predetermined deviation .DELTA.DIFLIM1, the judging section 22 judges that the operation state is abnormal, and outputs a signal for instructing the alarm section 24 to issue an alarm.

As described above, in the present embodiment, a process of extracting a high-frequency component from the detected yaw angle YA is performed.
Using the corrected yaw angle YAM after the processing, the lateral displacement differential amount DYK representing the behavior of the vehicle and the deviation amount ΔDIF1 are calculated, and the driving situation is determined based on the deviation amount ΔDIF1, so that the calculation amount is relatively small. Thus, the behavior of the vehicle can be accurately grasped, and an accurate driving situation can be determined.

FIG. 3 is a flowchart showing the procedure of processing in the microcomputer 1. The functions of the high-frequency component extraction unit 16, the lateral displacement amount differentiation amount calculation unit 18, the deviation amount calculation unit 20, and the judgment unit 22 are concretely described. Specifically, the processing is realized by the processing of FIG.

First, in step S11, the yaw rate YR and the vehicle speed V for T1 seconds are fetched every T2 seconds, and then the yaw angle YA is calculated from the yaw rate YR and the high-frequency component of the yaw angle YA is extracted to obtain the corrected yaw angle YA.
M is calculated (step S12), and the corrected yaw angle YA is further calculated.
The lateral displacement differential amount DYK is calculated from M (step S1).
3).

In the following step S15, the deviation ΔDIF
1 and then it is determined whether or not the difference ΔDIF1 is equal to or greater than a predetermined difference ΔDIFLIM1 (step S1).
6). When ΔDIF1 <ΔDIFLIM1, this process is immediately terminated, and ΔDIF1 ≧ ΔDIFL
If it is IM1, it is determined that the driving situation is abnormal, and a signal instructing to issue an alarm is output to the alarm unit 24.

FIG. 4 is a diagram showing the configuration of a vehicle driving condition monitoring apparatus according to a second embodiment of the present invention. A lateral displacement calculator 19 is provided in place of the calculator 18, and the deviation calculator 20 calculates the deviation based on the lateral displacement instead of the lateral displacement differential. The other points are the same as the first embodiment.

FIG. 5 is a flowchart showing a procedure of processing executed by the microcomputer 1 of the embodiment, and the operation of the monitoring apparatus of the embodiment will be described with reference to this flowchart.

First, in steps S21 and S22, similarly to steps S11 and S12 in FIG. 3, data is fetched and a corrected yaw angle YAM is calculated by extracting a high-frequency component of the yaw angle YA. In step S23, the lateral displacement differential DYK is calculated from the corrected yaw angle YAM and the vehicle speed V, and the DYK value is integrated over time to calculate the lateral displacement YK (see FIG. 2E).

In the following step S25, the deviation ΔDIF
2 is calculated. This deviation amount ΔDIF2 is, for example, as shown in FIG.
(E) is calculated as the area of the shaded portion (time integral of the absolute value of the lateral displacement YK), but the standard deviation of the YK value or the difference between the maximum value and the minimum value may be used.

Next, it is determined whether or not the difference ΔDIF2 is equal to or larger than a predetermined difference ΔDIFIM2 (step S2).
6), when ΔDIF2 <ΔDIFLIM2, this processing is immediately terminated, while ΔDIF2 ≧ ΔDIFLIM
When the number is 2, the driving condition is determined to be abnormal, and a signal for instructing to issue an alarm is output to the alarm unit 24.

As described above, in the present embodiment, processing for extracting a high-frequency component from the detected yaw angle YA is performed.
Using the corrected yaw angle YAM after the processing, the lateral displacement YK and the deviation ΔDIF2 representing the behavior of the vehicle are calculated, and the deviation Δ
Since the driving condition is determined based on the DIF 2, the same effect as in the first embodiment can be obtained.

FIG. 6 is a diagram showing a configuration of a vehicle driving condition monitoring apparatus according to a third embodiment of the present invention. The monitoring apparatus according to this embodiment is different from the second embodiment in calculating the deviation amount. A driving ability estimating section 21 for estimating the driving ability of the driver is added between the section 20 and the judging section 22. The other points are the same as the second embodiment.

FIG. 7 is a flowchart of a process corresponding to the functional block diagram of FIG.
Step 25 is the same as the processing in FIG.

In step S31, the driving ability of the driver is estimated based on the deviation ΔDIF2 calculated in step S25. This estimation is specifically performed as follows.

First, the deviation amount ΔDIF2 is calculated m times (for example, 4 times) and n times (for example, 8 times) by changing the sampling time of the yaw rate YR and the vehicle speed V.
An average value ΔDIFAVE and a standard deviation σDIF of two values and an average value ΔDIFAVE3 of n ΔDIF values are calculated. Then, the average value ΔDIFAVE is equal to the predetermined deviation amount ΔDI
The driving capability levels A to D are determined as shown in FIG. 8 depending on whether or not the standard deviation σDIF is larger than the predetermined threshold σTH. Where ΔDIFA
When VE ≦ ΔDIFTH and σDIF ≦ σTH, it is estimated that the driving ability is the highest because the deviation amount is small on average and the variation is small (level A). On the other hand, ΔDIFAVE> ΔDIFTH and σDI
When F ≦ σTH, the deviation is large on average and the variation is small, so that it is estimated that the driving ability is the lowest (level D). Also, when σDIF> σTH, it is estimated that the smaller the ΔDIFAVE value is, the higher the driving ability is. The level B is set when ΔDIFAVE ≦ ΔDIFTH is set, and the level C is set when ΔDIFAVE> ΔDIFTH is set.

Further, the number NOV (= 0 to m) of the m ΔDIF2 values exceeding a predetermined value is obtained, and this NO
The driving ability levels E to I are determined according to the V value. That is, when m = 4, the driving capabilities are set to E, F, G, H, and I, respectively, corresponding to NOV = 0, 1, 2, 3, and 4.

The driving ability levels A to C and E
Based on .about.I, the overall driving ability is determined as shown in FIG. That is, the average value ΔD of n ΔDIF values
Assuming that the predetermined threshold value of IFAVE3 is ΔDIF3TH, levels A, B and E, or ΔDIFAVE3 <ΔDIF
At 3TH, it is determined to be “normal”, and when levels A, B and F, G and ΔDIFAVE3 ≧ ΔDIF3TH, or when levels C and E, F, G and ΔDIFAVE3 ≧ Δ
In the case of DIF3TH, it is determined to be “warning level 1”, and the levels A, B, C and H, I and ΔDIFAVE3 ≧ ΔDI
At F3TH, or at level D and ΔDIFAVE3
When ≧ ΔDIF3TH, it is determined to be “warning level 2”.

Note that the average value ΔDIF of n ΔDIF values
Without using AVE3, when the level is A, B and E, it is determined to be "normal", and when the level is A, B and F or G, or when the level is C and E, F or G, "warning level 1" is determined. Judgment, when the level is A, B, C and H, I or when the level is D
In this case, it may be determined to be "warning level 2".

In this manner, a plurality of deviation amounts ΔDIF2
The driving ability is more accurately determined (estimated) by determining the driving ability of the driver based on the average value and the variation of the driving force.
can do.

Returning to FIG. 7, in step S32, it is determined whether the driving ability is low, that is, whether the driving ability estimated in step S31 is at the warning level 1 or 2.
As a result, if the driving ability is not at the warning level 1 or 2, this process is immediately terminated, while if the driving ability is at the warning level 1 or 2, it is determined that the driving situation is abnormal and an alarm is issued. Is output to the alarm unit 24.

In this case, it is desirable to make the warning sound louder at the warning level 2 than at the warning level 1 or to make both the lamp lighting and the buzzer sound.
Further, when the warning level is 2, a fail-safe action such as reducing the vehicle speed may be performed.

As described above, according to the third embodiment,
By judging the driving ability of the driver based on the average value and the variation of the plurality of deviation amounts ΔDIF2, it is possible to more accurately judge (estimate) the driving ability, and more detailed warning and fail-safe action are possible. Becomes

In the above-described embodiment, the yaw angle YA
The extraction processing of the high-frequency component is performed by a well-known high-pass filter processing, but is not limited thereto.
The fast Fourier transform may be performed by using an SP (Digital Signal Processor), and the inverse fast Fourier transform may be performed by removing low frequency components. If necessary high-frequency components are included, band-pass filtering may be performed.

Further, in the above-described embodiment, a warning to the driver is used that appeals to the driver's sight or hearing. However, the present invention is not limited to this. May be vibrated, tension may be applied to the seat belt, a specific scent may be released into the vehicle interior, or the operating state of the air conditioner may be changed. This makes it possible to more reliably notify the driver of the deterioration of the driving situation.

In the above-described embodiment, the yaw rate is detected by the yaw rate sensor 10. Instead, the output of the wheel speed sensor and the vehicle speed sensor, or the steering angle sensor for detecting the steering angle of the steering, and the lateral direction are provided. The yaw rate may be calculated using the output of the acceleration sensor or the like.

[0039]

As described in detail above, according to the present invention, a driving situation value corresponding to the behavior of a vehicle is detected, a high-frequency component of the detected driving situation value is extracted, and based on the extracted component. Since it is determined whether the driving situation of the driver is appropriate, the behavior of the vehicle can be grasped more accurately by relatively simple calculation, and the accuracy of determining the driving situation of the driver can be improved.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a configuration of a vehicle driving condition monitoring device according to a first embodiment of the present invention.

FIG. 2 is a diagram showing detection data and transition of parameters calculated based on the detection data.

FIG. 3 is a flowchart showing a procedure of processing executed by the microcomputer of FIG. 1;

FIG. 4 is a block diagram showing a configuration of a vehicle driving condition monitoring device according to a second embodiment of the present invention.

FIG. 5 is a flowchart showing a procedure of processing executed by the microcomputer of FIG. 4;

FIG. 6 is a block diagram showing a configuration of a vehicle driving condition monitoring device according to a third embodiment of the present invention.

FIG. 7 is a flowchart showing a procedure of processing executed by the microcomputer of FIG. 6;

FIG. 8 is a diagram showing a map for determining a driving ability level of a driver.

FIG. 9 is a diagram showing a map for determining a driving ability level of a driver.

[Description of Signs] 1 Microcomputer (driving condition value detecting means, extracting means, determining means) 10 Yaw rate sensor (driving condition value detecting means) 12 Vehicle speed sensor 24 Alarm section

Claims (1)

[Claims] 1. A driving condition monitoring device for a vehicle for monitoring a driving condition of a driver of a vehicle, comprising: a driving condition value detecting means for detecting a driving condition value according to a behavior of the vehicle; An operating condition monitoring device for a vehicle, comprising: extracting means for extracting a high-frequency component; and determining means for determining whether or not the driving condition of the driver is appropriate based on the extracted component.