JPH10232989A - Driving state monitor device for vehicle - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide the driving state monitor device for a vehicle which can improve the decision precision of the driving state of a driver by grasping the behavior of the vehicle more accurately through relatively easy operation. SOLUTION: A correction yaw angle YAM is calculated as a reference difference calculated from a 1st yaw angle YA1 obtained from a 1st detection data group sampled in a 1st sampling cycle TS1 and a 2nd yaw angle YA2 obtained from a 2nd detection data group sampled in a 2nd sampling cycle TS2 longer than the 1st sampling cycle TS1 (a zigzag component is extracted) (S12), a lateral displacement differential quantity DYK and a deviation quantity ΔDIF1 representing the behavior of the vehicle is calculated by using the correction yaw angle YAM (S13, S15), and the driving state is decided on the basis of the deviation quantity ΔDIF1 (S16).

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, the amount of calculation for calculating the behavior criterion may be 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]

According to the present invention, there is provided a vehicle driving condition monitoring apparatus for monitoring a driving condition of a driver of a vehicle. Operating state value detection means for detecting at one sampling period and at a second sampling period longer than the first sampling period; a first detection data group detected at the first sampling period; A determination unit that determines whether the driving situation of the driver is appropriate by comparing the second detection data group detected in a cycle with the second detection data group.

[0007] More specifically, the judging means includes a reference setting means for setting a reference for the operating condition value based on the second detection data group, the first detection data group, A lateral displacement behavior amount calculating means for computing a lateral displacement behavior amount of the vehicle in accordance with a deviation from a reference, and performing the determination based on the lateral displacement behavior amount.

According to the first aspect of the present invention, the driving situation value corresponding to the behavior of the vehicle is detected in the first sampling period and the second sampling period longer than the first sampling period, By comparing the first detection data group detected in the sampling period with the second detection data group detected in the second sampling period, it is determined whether the driving situation of the driver is appropriate.

According to the second aspect of the present invention, the reference of the driving situation value is set based on the second detection data group, and the reference is set according to the deviation between the first detection data group and the set reference. Then, the lateral displacement behavior amount of the vehicle is calculated, and the driving situation is determined based on the lateral displacement behavior amount.

[0010]

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:
A yaw rate sensor 10 for detecting a yaw rate as a driving situation value according to the behavior of the vehicle and a vehicle speed sensor 12 for detecting a traveling speed as a driving situation value according to the behavior of the vehicle are connected. 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. meandering component extraction unit 16, lateral displacement amount differential amount calculation unit 18,
The deviation amount calculation unit 20 and the determination unit 22 show the functions of the microcomputer 1 as blocks. In the present embodiment, the yaw rate sensor 10, the vehicle speed sensor 12, and the signal memory unit 14 constitute a driving situation value detection unit described in claims, and the meandering component extraction unit 1
6. The lateral displacement differential amount calculating unit 18, the deviation amount calculating unit 20, and the determining unit 22 constitute determining means (reference setting means, lateral displacement behavior amount calculating means).

The signal memory unit 14 includes the sensors 10 and 1
2 and the yaw rate data and the vehicle speed data for the past T1 seconds (for example, 20 seconds) from the present time are stored in the first sampling period TS1 (for example, 20 msec) and the second sampling period TS2 longer than the first sampling period. (For example, 1 second), and is updated every T2 seconds (for example, 10 seconds) and output to the meandering component extraction unit 16. A data group obtained by sampling at the first sampling period TS1 is called a first detection data group,
A data group obtained by sampling at the second sampling period TS2 is referred to as a second detection data group.

The meandering component extraction unit 16 calculates the yaw rate YR (FIG. 2) of the input first and second data groups, respectively.
(A) is time-integrated to obtain a yaw angle YA (FIG.
And FIG. 3), and a process of extracting a meandering component of the vehicle from the yaw angle YA data is performed. FIG.
, Solid lines and broken lines indicate transitions of the first yaw angle YA1 obtained from the first detection data group and the second yaw angle YA2 obtained from the second detection data group, respectively, and the meandering component is (YA1 -YA2). Here, since the number of data of the second yaw angle YA2 is smaller than the data of the first yaw angle YA1, the first yaw angle YA1 is set.
At the time when the second yaw angle YA2 corresponding to No. 1 does not exist, FIG.
The meandering component is calculated using the value obtained by performing the linear interpolation as indicated by the broken line. That is, the broken line in FIG. 3 corresponds to the “reference” described in the claims.

The meandering component is corrected for the yaw angle YAM.
This is output to the lateral displacement differential amount calculation unit 18 (see FIG. 2C). In the meandering component extraction processing, when the road on which the vehicle is traveling is curved and the yaw angle YA increases, the yaw angle data obtained from the detection data group sampled at a relatively long cycle is used. The present embodiment focuses on the point where the road shape appears, whereby it is possible to detect an accurate driving condition while eliminating the influence of a curved road on which the vehicle is traveling.

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
Is calculated on the basis of .DELTA.DIF1 (lateral displacement behavior amount). The deviation amount ΔDIF1 is calculated, for example, as the area of the hatched portion in FIG. 2D (the time integral value of the absolute value of the lateral displacement differential amount DYK). 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, the first yaw angle YA1 obtained from the first detection data group sampled in the first sampling period TS1 and the second yaw angle YA1 longer than the first sampling period TS1 Sampling period T
The corrected yaw angle YAM is calculated as a difference from the second yaw angle YA2 obtained from the second detection data group sampled in S2 (the meandering component is extracted), and the behavior of the vehicle is represented using the corrected yaw angle YAM. Lateral displacement differential amount DYK and deviation amount ΔD
Since IF1 is calculated and the driving situation is determined based on the deviation amount ΔDIF1, the behavior of the vehicle can be accurately grasped with a relatively small amount of computation, and the accurate driving situation can be determined.

FIG. 4 is a flowchart showing the procedure of processing in the microcomputer 1. The functions of the meandering component extraction unit 16, lateral displacement amount differential amount calculation unit 18, deviation amount calculation unit 20, and judgment unit 22 are described in detail. Specifically, it is realized by the processing of FIG. 4 in the CPU of the microcomputer 1.

First, in step S11, the first time T1 seconds
And the first and second detection data groups sampled at the second sampling periods TS1 and TS2, and then using the respective data groups to obtain the first and second yaw angles Y
The corrected yaw angle YAM is calculated by calculating A1, YA2 and extracting the meandering component (step S1).
2), and the lateral displacement differential amount DYK from the corrected yaw angle YAM
Is calculated (step S13).

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. 5 is a diagram showing a configuration of a vehicle driving condition monitoring apparatus according to a second embodiment of the present invention. The monitoring apparatus according to this embodiment is different from the first embodiment in the lateral displacement differential amount. 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. In the present embodiment, the meandering component extraction unit 1
6. The lateral displacement amount calculating section 19, the deviation amount calculating section 20, and the judging section 22 constitute the judging means (reference setting means, lateral displacement behavior amount calculating means) described in the claims.

FIG. 6 is a flowchart showing a procedure of processing executed by the microcomputer 1 of the present embodiment, and the operation of the monitoring apparatus of the present embodiment will be described with reference to FIG.

First, in steps S21 and S22, similarly to steps S11 and S12 in FIG. 3, the detection data group is fetched and the corrected yaw angle YAM is calculated by extracting the meandering 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 (lateral displacement behavior amount) is calculated. This deviation ΔDIF2
Is calculated, for example, as the area of the hatched portion in FIG. 2E (the time integral of the absolute value of the lateral displacement YK).
The standard deviation of the K 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, a process for extracting a meandering component from the detected yaw angle YA is performed in the same manner as in the first embodiment, and the corrected yaw angle YAM is used for the vehicle. Lateral displacement YK and deviation ΔDIF representing the behavior of
2 is calculated, and the driving situation is determined based on the deviation amount ΔDIF2, so that the same effect as in the first embodiment can be obtained.

FIG. 7 is a diagram showing the configuration of a vehicle driving condition monitoring apparatus according to a third embodiment of the present invention. 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. In the present embodiment, the meandering component extracting unit 16, the lateral displacement calculating unit 19, and the deviation calculating unit 2
0, the driving ability estimating unit 21 and the determining unit 22 constitute the determining means (reference setting means, lateral displacement behavior amount calculating means) described in the claims.

FIG. 8 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 difference 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 capacity levels A to D are determined as shown in FIG. 9 according to 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 determined, 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 に I, the overall driving ability is determined as shown in FIG. That is, the average value Δ of n ΔDIF values
If the predetermined threshold value of DIFAVE3 is ΔDIF3TH,
Level A, B and E, or ΔDIFAVE3 <ΔDI
When F3TH, 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. 8, in step S32, it is determined whether or not the driving ability is low, that is, whether or not the driving ability estimated in step S31 is at 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, 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. Alternatively, the steering angle sensor for detecting the output of the wheel speed sensor and the vehicle speed sensor, or the steering angle of the steering, and the lateral direction may be used. The yaw rate may be calculated using the output of the acceleration sensor or the like.

[0042]

As described above in detail, according to the present invention, a driving situation value corresponding to the behavior of a vehicle is detected in a first sampling period and a second sampling period longer than the first sampling period. By comparing the first detection data group detected in the first sampling period with the second detection data group detected in the second sampling period, it is determined whether the driving condition of the driver is appropriate. Therefore, the behavior of the vehicle can be grasped more accurately by a 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 diagram showing a transition of yaw angle data having different sampling periods.

FIG. 4 is a flowchart illustrating a procedure of processing executed by the microcomputer of FIG. 1;

FIG. 5 is a block diagram showing a configuration of a vehicle driving condition monitoring device according to a second embodiment of the present invention.

FIG. 6 is a flowchart showing a procedure of processing executed by the microcomputer of FIG. 5;

FIG. 7 is a block diagram showing a configuration of a vehicle driving condition monitoring device according to a third embodiment of the present invention.

FIG. 8 is a flowchart showing a procedure of processing executed by the microcomputer of FIG. 7;

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

FIG. 10 is a diagram showing a map for determining a driving ability level of a driver.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Microcomputer 10 Yaw rate sensor (operating condition value detecting means) 12 Vehicle speed sensor (operating condition value detecting means) 14 Signal memory unit (operating condition value detecting means) 16 Meandering component extracting unit (determining means) 18 Lateral displacement differential amount calculating unit (Determining means) 20 Deviation amount calculating section (Determining means) 22 Determining section (Determining means) 24 Alarm section

Claims (2)

[Claims] 1. A vehicle driving situation monitoring device for monitoring a driving situation of a driver of a vehicle, comprising: a driving situation value corresponding to a behavior of the vehicle, wherein the driving situation value is a first sampling cycle and a first sampling cycle longer than the first sampling cycle. Operating state value detection means for detecting at a second sampling period; comparing a first detection data group detected at the first sampling period with a second detection data group detected at the second sampling period; And a determining means for determining whether or not the driving condition of the driver is appropriate. 2. The method according to claim 1, wherein the determination unit is configured to set a reference for the operating condition value based on the second detection data group, and a deviation between the first detection data group and the set reference. 2. The vehicle driving method according to claim 1, further comprising: a lateral displacement behavior amount calculating unit configured to calculate a lateral displacement behavior amount of the vehicle in accordance with the vehicle speed, and performing the determination based on the lateral displacement behavior amount. Condition monitoring device.