JP3492963B2 - Vehicle driving condition monitoring device - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a vehicle driving condition monitoring apparatus for monitoring a driving condition of a driver of a vehicle and issuing a warning if necessary.

[0002]

2. Description of the Related Art A response delay time of a driver and a deviation amount between a vehicle position and a driving lane are estimated based on a steering amount and a vehicle speed of a vehicle, and the estimated response delay time and deviation amount and a normal state A driving situation monitoring device that compares the response delay time and the deviation amount to determine the driving situation of a driver (for example, an abnormal steering state due to a decrease in driving ability due to the driver's drowsiness or fatigue) has been used in the past. It is known (Japanese Unexamined Patent Publication No. 5-85221).

Further, by detecting the yaw rate and the vehicle speed of the vehicle,
There is also known a vehicle driving condition monitoring device that determines a vehicle driving reference line based on the detected yaw rate and vehicle speed, and determines a vehicle driving condition abnormality using a parameter that indicates the deviation between the actual driving trajectory and the reference line. (JP-A-8-24960)
No. 0).

[0004]

However, in the monitoring device described in Japanese Patent Laid-Open No. 5-85221, the actual vehicle position and the traveling lane (reference vehicle position) are determined based on the steering amount and the vehicle speed. Is calculated, and the deviation is not calculated based on the physical quantity directly related to the behavior of the vehicle. Therefore, the deviation is caused by, for example, a change in characteristics of the vehicle (for example, characteristics of suspension or steering). There is a problem in that an error occurs in the amount and the accuracy of determination of the driving situation of the driver decreases.

On the other hand, in the device disclosed in Japanese Patent Laid-Open No. 8-249600, the yaw rate directly related to the behavior of the vehicle is used to judge the driving situation, so that the judgment accuracy is improved, but it becomes the judgment reference. There is a large amount of calculation for calculating the reference line, and it is necessary to provide a dedicated microcomputer for monitoring the operating condition. Therefore, it is difficult to reduce the cost by reducing the number of microcomputers used.

The present invention has been made in view of this point, and it is possible to accurately grasp the behavior of the vehicle with a smaller amount of calculation as compared with the prior art and to accurately determine the driving situation. An object is to provide a situation monitoring device.

[0007]

In order to achieve the above object, the invention according to claim 1 is a vehicle driving condition monitoring apparatus for monitoring a driving condition of a driver of a vehicle, and a behavior related to a lateral motion of the vehicle. Behavioral amount detection means for detecting the amount,
Vehicle speed detection means for detecting the vehicle speed of the vehicle, behavior reference setting means for obtaining a single regression line based on the change in the behavior amount, and setting this as a behavior reference, the behavior amount, the behavior reference, and the vehicle speed. A lateral displacement behavior amount calculation means for calculating a lateral displacement behavior amount of the vehicle based on the above; a driving situation determination means for determining whether or not the driving situation of the driver is appropriate based on the lateral displacement behavior amount; A road shape determining means for determining the shape of a road on which the vehicle is traveling, wherein the road shape determining means determines that the road shape is substantially linear or has a curved shape with a substantially constant radius of curvature. Only when it is determined that the driving situation of the driver is not appropriate by the driving situation determining means, it is determined that the driving situation of the driver is abnormal.

According to this configuration, the single regression line is obtained based on the change in the behavior amount related to the lateral movement of the vehicle, and the single regression line is set as the behavior reference. Further, the lateral displacement behavior amount of the vehicle is calculated based on the behavior amount, the behavior reference, and the vehicle speed, and based on the lateral displacement behavior amount, it is determined whether or not the driving situation of the driver is appropriate. And
Only when it is determined that the road on which the vehicle is traveling is substantially linear, or the radius of curvature is substantially constant, and the driving situation of the driver is not appropriate ,
It is determined that the driving situation of the driver is abnormal.

Since the single regression line as the behavior standard can be obtained with a relatively small amount of calculation, the amount of calculation can be reduced as compared with the conventional device, and the operating condition can be reduced without using a dedicated microcomputer. Can be monitored. As a result, it becomes possible to monitor the operating condition by using a microcomputer provided in another system (for example, a navigation system), and the cost can be reduced. If the straight regression line is used as the behavior criterion, considering that the lateral displacement behavior amount tends to increase when the road shape changes significantly, the road shape is almost linear, or the radius of curvature is almost Since the abnormality determination is performed on the condition that the curve is constant, it is possible to accurately determine the driving situation even by using the single regression line as the behavior reference.

The invention according to claim 2 is the same as that of claim 1.
In the driving condition monitoring device for vehicles listed above,
The determining means adjusts the vehicle speed and the regression coefficient of the single regression line.
The shape of the road is determined based on the estimated radius of curvature.
It is characterized by setting. According to this configuration, the vehicle speed and
Based on the radius of curvature estimated according to the regression coefficient of the single regression line
Then, the shape of the road is determined. The invention according to claim 3
Is a vehicle driving condition monitoring apparatus according to claim 1 or 2.
In the driving condition determination means,
Set the judgment threshold as a reference value to judge whether the situation is proper or not.
And comparing the judgment threshold value with the lateral displacement behavior amount for operation
While judging whether the situation is appropriate, the judgment threshold
Specially set according to the vehicle speed or the radius of curvature.
To collect. With this configuration, the vehicle speed or radius of curvature can be adjusted.
It was possible to make an appropriate judgment and issued an unnecessary alarm.
Or, conversely, avoid a situation where the alarm is delayed.
You can

According to a fourth aspect of the present invention, in the vehicle driving condition monitoring apparatus according to the first or second aspect, the driving condition determining means determines whether or not the driving condition of the driver is appropriate. With a determination threshold as a value, while determining whether the driving situation is appropriate by comparing the determination threshold and the lateral displacement behavior amount, from a plurality of measurement data of the lateral displacement behavior amount, at least an average value Is calculated, and the determination threshold is set based on the statistic. According to this configuration, it is possible to set the determination threshold value that is suitable for the maximum fluctuation range during normal driving that differs depending on the driver, and it is possible to more accurately determine the driving situation. The invention according to claim 5 is any one of claims 1 to 4.
The vehicle driving condition monitoring apparatus according to item 1, wherein the driving condition determining means, the driver of the vehicle to determine whether there is intention to the car line change, the intention of lane change to the driver It is characterized in that it is determined that the driving situation of the driver is not appropriate on the condition that there is no such situation. According to this configuration, it is determined that the driving situation is not appropriate on the condition that the driver has no intention of changing lanes.

[0012]

BEST MODE FOR CARRYING OUT THE INVENTION Embodiments of the present invention will be described below with reference to the drawings. (First Embodiment) FIG. 1 is a block diagram showing the configuration of a vehicle driving condition monitoring apparatus according to a first embodiment of the present invention. This apparatus is driven by a prime mover such as an internal combustion engine or an electric motor. It is installed in vehicles that have steering. In the figure, a yaw rate sensor 2 for detecting a yaw rate YR of the vehicle and a vehicle speed sensor 3 as a vehicle speed detecting means for detecting a traveling speed (vehicle speed) V of the vehicle are connected to an input side of the microcomputer 1. ing.
Further, the output side of the microcomputer 1 is connected to an alarm unit 21 that issues an alarm as needed during the monitoring of the driving condition of the driver. The alarm unit 21 is composed of, for example, a lamp, a buzzer, a sound generator and the like.

Signal memory unit 1 of the microcomputer 1
1, the behavior amount calculation unit 12, the behavior stability calculation unit 13, the road shape determination unit 14, and the determination unit 15 represent the functions of the microcomputer 1 as blocks. The signal memory unit 11 includes a yaw rate sensor 2 and a vehicle speed sensor 3.
The input signal from is stored, and yaw rate data and vehicle speed data for the past T1 seconds (for example, 10 seconds) from the present are stored in T2.
It is updated every second (for example, 5 seconds) and is output to the behavior amount calculation unit 12.

The behavioral quantity computing unit 12 integrates the input yaw rate YR (see FIG. 3A) with time to obtain a yaw angle YA.
(See (b) of the figure). That is, in the present embodiment, the yaw angle YA is used as the amount of behavior related to the lateral movement of the vehicle. The calculated yaw angle YA is
It is input to the behavior stability calculation unit 13. In addition, the behavior amount calculation unit 12 calculates the average vehicle speed V for T1 seconds from the input vehicle speed V.
m is calculated and output to the behavior stability calculation unit 13 and the determination unit 15.

The behavior stability calculation unit 13 obtains a single regression line serving as a vehicle behavior reference on the basis of the input yaw angle YA (obtains constants a and b of the equation YAR = a · t + b, which will be described later). The corrected yaw angle YAW is calculated based on the single regression line, and the traveling locus LOC (x (i), y (i)) of the vehicle is calculated using the corrected yaw angle YAW and the average vehicle speed Vm, and the lateral direction thereof is calculated. Maximum fluctuation width LOCWI (in the y direction)
DTH is calculated as a lateral displacement behavior amount indicating the stability of vehicle behavior.

Specifically, at times t (1), t (2),
, T (i), ..., t (n), yaw angle YA
(1), YA (2), ..., YA (i), ..., YA (n)
If the following data is obtained, first, the following equation (1)
According to (2), the single regression coefficient a and the constant b of the single regression line are calculated to obtain the single regression line.

[Equation 1]

In the above equation 1, tmean, YAm
ean is the average value of the time t (i) and the yaw angle YA (i), respectively. If the yaw angle YA on the straight regression line is YAR, then YAR = a.t + b (3) (see FIG. 3B).

Next, a yaw angle based on the single regression line, that is, a corrected yaw angle YAW (= YA-YAR) is calculated (see FIG. 7C). Next, the corrected yaw angle YAW and the average vehicle speed Vm are applied to the following equations (4) and (5) to obtain the traveling locus L.
OC (x (i), y (i)) is obtained (see FIG. 3 (d)).

[Equation 2]

Then, the lateral direction (y direction) of the traveling locus LOC.
The maximum fluctuation range LOCWIDTH of is output to the determination unit 15 as the lateral displacement behavior amount indicating the stability of the vehicle behavior. In addition, the vehicle stability calculation unit 13 outputs the average vehicle speed Vm and the single regression coefficient a to the road shape determination unit 14.

The road shape determination unit 14 determines the road shape based on the average vehicle speed Vm and the single regression coefficient a. Specifically, the average vehicle speed Vm input to the k-th and the single regression coefficient a calculated to the k-th are Vm (k) and a, respectively.
(K), the estimated curvature radius R is calculated by applying the following equation (6). R = Vm (k) / | a (k) | (6) The larger the absolute value of the single regression coefficient a, the larger the slope of the single regression line, and in other words, the larger the curve of the road. The estimated radius of curvature R becomes smaller. On the other hand, the estimated radius of curvature R increases as the average vehicle speed Vm increases.

Next, the change rate RR (k) of the estimated curvature radius R is calculated by the following equation (7). RR (k) = | R (k) −R (k−1) | / R (k) (7) Then, the estimated radius of curvature R (k) is the straight line determination threshold RLIM.
As described above, when it is determined that the road is substantially linear, or the estimated curvature radius R (k) is the linear determination threshold RLI.
Smaller than M and the rate of change RR (k) is equal to the estimated radius of curvature R
When (k) is equal to or less than the constant curve determination threshold RRLIM for determining that it is substantially constant, the determination unit 15 outputs a determination permission signal that permits the determination of the abnormality of the driving condition.

In the present embodiment, the reference line which is the reference for the determination is approximated by a single regression line. Therefore, if the change in the road shape is large, an erroneous determination is likely to occur. Therefore, the road is curved and the rate of change R of the estimated radius of curvature R is
When R is large, accurate determination is possible by prohibiting abnormality determination. The straight line determination threshold value RLIM and the constant curve determination threshold value RRLIM are experimentally determined so that an error does not occur in the determination in the determination unit 15 described later.

When the determination permission signal is input from the road shape determination unit 14, the determination unit 15 determines that the maximum fluctuation range LOCWIDTH is greater than or equal to the determination threshold LOCLIM and the lane is not changed, the driving condition is changed. Is determined to be abnormal, and a signal for instructing to issue an alarm is output to the alarm unit 21.

Here, the determination threshold LOCLIM is the maximum fluctuation range LOC during normal operation based on the yaw angle YA detected when simulating normal operation and abnormal operation.
WIDTHNR and maximum change width LOCWI at abnormal operation
Calculate DTHAB, LOCWIDTHNR> LOCL
Experimentally set IM> LOCWIDTHAB. Considering that the maximum fluctuation range LOCWIDTH changes in accordance with the change in the vehicle speed V, in the present embodiment, the higher the vehicle speed average value m is, the higher the determination threshold LOCLIM is set in accordance with the detected average value Vm of the vehicle speed V. To grow
For example, it is set using the following equation (8). LOCLIM = α · Vm + β (α is a positive constant, β is a constant of 0 or more) (8)

Further, it is possible to determine whether or not the lane has been changed.
Do the following: That is, since it is known that the yaw rate YR changes as shown in FIG. 4 when the lane is changed, the yaw rate YR shows a peak in one direction (for example, the right direction) and then changes to the other direction (from the time when the yaw rate YR shows the peak). For example, a time T until a time point when a peak in the left direction) is shown and a difference (amplitude of yaw rate) W between those peak values are measured. Then, when the time T is within the predetermined time T1, T2 (T1> T2) range and the amplitude W is larger than the predetermined value W0, it is determined that the lane change has been performed. The predetermined times T1, T2 and the predetermined value W0 are experimentally set by measuring the yaw rate YR when the lane is actually changed.

As described above, in this embodiment, the yaw angle YA is calculated from the detected yaw rate YR, and the plurality of yaw angles YA (i) detected during the monitoring period T1 and the time t (i).
Since a single regression line of (i = 1 to n) is obtained and the driving situation is determined by using this as a reference line (behavior reference), a conventional method of increasing the order until the regression error becomes a predetermined value or less The calculation amount can be reduced as compared with (Japanese Patent Laid-Open No. 8-249600), and the operating condition can be monitored without using a dedicated microcomputer. As a result, it becomes possible to monitor the operating condition by using a microcomputer provided in another system (for example, a navigation system), and the cost can be reduced. In addition, when the road shape is determined and an erroneous determination is likely to occur, that is, the road is curved and the rate of change R of the estimated curvature radius R is R.
When R is large, the abnormality determination is not performed, so that an accurate determination can be performed using the single regression line as a reference.

FIG. 2 is a flow chart showing the procedure of processing in the microcomputer 1. The above-mentioned behavior amount calculating section 12, behavior stability calculating section 13, and road shape determining section 1 are described above.
4 and the function of the determination unit 15 are specifically realized by the processing of FIG. 2 in the CPU of the microcomputer 1.

First, in step S11, the yaw rate YR and vehicle speed V for T1 seconds are fetched every T2 seconds, and then the yaw rate YR is integrated over time to calculate the yaw angle YA (step S12). Next, the time-series data of the yaw angle YA, that is, the yaw angle YA corresponding to the time t (i).
(I) and (1) and (2) are applied to the single regression line,
That is, the single regression coefficient a and the constant b are obtained (step S
13).

In the following step S14, the corrected yaw angle YA is set.
W (= YA-YAR) is calculated, and then the average vehicle speed Vm and the corrected yaw angle YAW are applied to the equations (4) and (5),
The travel locus LOC (x (i), y (i)) and its maximum fluctuation range LOCWIDTH are calculated (step S15).
In step S16, the determination threshold LOCLIM is set according to the average vehicle speed Vm using, for example, the formula (8), and in step S17, the estimated radius of curvature R of the road and its estimated radius of curvature R are calculated using the formulas (6) and (7). The rate of change RR is calculated.

The estimated radius of curvature R and its rate of change RR
Based on the above, whether the road shape is substantially linear, or whether the estimated radius of curvature R is substantially constant curved line, specifically, the estimated radius of curvature R is equal to or greater than the linear determination threshold value RLIM, or Estimated radius of curvature R <RLIM and rate of change R
It is determined whether R is less than or equal to the constant curve determination threshold RRLIM (step S18). When the answer is negative (NO), that is, when the road shape is not a straight line shape or a fixed curve shape, the process is immediately ended to avoid erroneous determination.

On the other hand, when R ≧ RLIM, or R <
When RLIM and RR ≦ RRLIM, that is, when the road shape is substantially straight or curved with a substantially constant radius of curvature, it is determined whether or not the maximum fluctuation width LOCWIDTH is equal to or greater than the determination threshold LOCLIM (step S1.
9), when LOCWIDTH <LOCLIM,
The operating condition is judged to be proper and the process is terminated. Also, LO
When CWIDTH ≧ LOCLIM, it is determined whether or not the lane is changed (step S20). If the lane is not changed, the driving condition is determined to be abnormal and the alarm unit 21 is instructed to issue an alarm (step S21). .

FIGS. 3 (a) and 3 (b) are views showing an example of the yaw rate YR and the transition of the yaw angle YA obtained by time integration of the yaw rate YR. FIG. 3 (b) shows the calculation. The single regression line (YAR) obtained by the above is also shown. (C) of the figure shows a transition of the corrected yaw angle YAW with the single regression line of the figure (b) as a reference line, and (d) of the figure shows a traveling locus calculated from the corrected yaw angle YAW and the average vehicle speed Vm. The LOC is shown.

In the present embodiment, the yaw rate sensor 2 and the behavior amount calculation unit 12 (steps S11 and S12 in FIG. 2).
Corresponds to the behavior amount detecting means, and the behavior stability calculation unit 13
(Steps S13, S14, S15 in FIG. 2) correspond to the behavior reference setting unit and the lateral displacement behavior amount calculation unit, and the road shape determination unit 14 (Step S17 in FIG. 2) corresponds to the road shape determination unit. The determination unit 15 corresponds to the driving condition determination means. As shown in FIG. 5, the output of the blinker switch 4 is supplied to the microcomputer 1 to determine whether or not the lane has been changed (FIG. 2, step S20) based on the output of the blinker switch 4. Good.

(Second Embodiment) FIG. 6 is a block diagram showing the configuration of a vehicle driving condition monitoring apparatus according to a second embodiment of the present invention. In the configuration shown in FIG. 6, a threshold value calculation unit 16 is provided between the road shape determination unit 14 and the determination unit 15, which is different from the device of the first embodiment shown in FIG. .

The threshold calculation unit 16 includes a vehicle behavioral quantity calculation unit 1
The average vehicle speed Vm is supplied from 2, and the estimated curvature radius R of the road is supplied from the road shape determination unit 14. Then, the threshold calculation unit 16 calculates the determination threshold LOCLIM based on the vehicle speed Vm and the estimated curvature radius R, and supplies the determination threshold LOCLIM to the determination unit 15.

7 and 8 are flowcharts showing the procedure of processing in the microcomputer 1 of the present embodiment. The behavior amount calculation unit 12, the behavior stability calculation unit 13, and the road shape determination unit described in the first embodiment. 14 and determination unit 15
In addition to this, the function of the threshold value calculation unit 16 is realized by the processing of FIGS. 7 and 8 in the CPU of the microcomputer 1.

The process of FIG. 7 is performed by step S1 of the process of FIG.
6 is changed to step S16a, and steps S18 and S
It has been inserted between No. 19 and FIG.
The process is the same as that of. That is, when it is determined in step S18 that the shape of the road on which the vehicle is traveling is linear or the curvature is substantially constant, the LOCLIM calculation process shown in FIG. 8 is executed, and the process proceeds to step S19.

FIG. 8 is a flowchart of the process for calculating the determination threshold LOCLIM referred to in step S19. First, in step S31, the average vehicle speed Vm and the estimated curvature radius R are taken in, and then the average vehicle speed Vm and the estimated curvature radius R are acquired. Accordingly, the KLOC map shown in FIG. 9 is searched, and the correction coefficient KLOC is calculated (step S32). KLO
In the C map, the average vehicle speed Vm is the predetermined vehicle speed V0 (for example, 1
At the operating point P0 corresponding to the case where the road shape is linear (R = ∞) at 00 km / h).
With C = 1.0 (no correction value), the higher the average vehicle speed Vm and the smaller the radius of curvature R, the correction coefficient KL.
The OC is set to increase. Further, when the estimated radius of curvature R is smaller than the predetermined radius of curvature R1, or when the average vehicle speed Vm is higher than the predetermined vehicle speed V2 or lower than the predetermined vehicle speed V1, the driving situation is not determined.

In the following step S33, the following equation ( 9 )
Then, the determination threshold LOCLIM is calculated. LOCLIM = KLOC × LOCLIM0 ( 9 ) Here, LOCLIM0 is a reference threshold LOCLIM0 (for example, 0.5 m) corresponding to the operating point P0 shown in FIG.

Determination threshold LOC calculated as described above
By using the LIM for the determination in step S19 of FIG. 7, it is possible to perform an appropriate determination according to the vehicle speed Vm and the road shape (curvature radius R), and to issue an unnecessary alarm or conversely delay the alarm. The situation can be avoided.

FIG. 10 is a flowchart of a modified example of the LOCLIM calculation process of FIG. 8, and step S4 of FIG.
1, S46 and S47 are steps S31 and S3 in FIG.
This is the same processing as 2 and S33. In this modified example, the reference threshold LOCLIM0 is set to an average value LOCWIDT of a plurality of maximum fluctuation widths LOCWIDTH measured during normal operation.
It is set according to Hm and the standard deviation LOCWIDTHσ. The measurement data of the maximum fluctuation width LOCWIDTH used for calculating the average value and the standard deviation is stored in the memory.

In step S42 of FIG. 10, it is determined whether or not the correction of the reference threshold value LOCLIM0 is completed. At first, this answer is negative (NO), so the maximum fluctuation range L
A certain number N of OCWIDTH measurement data (for example, 20
It is determined whether or not the number has reached or exceeded (step S4).
3). Since the answer is negative (NO) at first, the process immediately proceeds to step S46 to calculate the correction coefficient KLOC, and further to calculate the determination threshold LOCLIM by the equation ( 9 ). At this time, the reference threshold LOCLIM0 uses an initial value (for example, 0.5 m).

When the number of pieces of measurement data reaches N in step S43, the average value LOCWID of N pieces of maximum fluctuation width data
THm and standard deviation LOCWIDTHσ are calculated (step S44), and then the reference threshold value L is calculated by the following equation ( 10 ).
OCLIM0 is corrected (step S45). Here, the expression (10) is an expression obtained by experiment, the constant c is set to 0.73, and the constant d is set to 0.2, for example.
It is set to (m).

When the correction of the reference threshold value LOCLIM0 is completed by executing steps S44 and S45, the process immediately proceeds from step S42 to step S46. As described above, according to the modification shown in FIG.
N maximum fluctuation widths LOCWID measured during normal operation
Average TH LOCWIDTHm and standard deviation LOCW
Correct the reference threshold LOCLIM0 according to IDTHσ,
Using the corrected reference threshold LOCLIM0, the determination threshold LO
Since the CLIM is calculated, it is possible to set a determination threshold value that is suitable for the maximum fluctuation range LOCWIDTH during normal driving that varies depending on the driver, and it is possible to more accurately determine the driving situation.

In the present embodiment, the yaw rate sensor 2 and the behavioral amount calculation unit 12 (steps S11 and S12 in FIG. 7).
Corresponds to the behavior amount detecting means, and the behavior stability calculation unit 13
(Steps S13, S14, S15 in FIG. 7) correspond to the behavior reference setting means and the lateral displacement behavior amount calculation means, and the road shape determination unit 14 (step S17 in FIG. 7) corresponds to the road shape determination means. Judgment unit 15 (step S16 in FIG. 7)
a, S18 to S20, and the processing of FIG. 8) correspond to the driving condition determination means.

(Other Modifications) The present invention is not limited to the above-described embodiment, but various modifications can be made. For example, in the above-described embodiment, the maximum fluctuation width LOCWIDTH of the traveling locus LOC is used as the lateral behavior amount indicating the stability of the vehicle behavior, but the present invention is not limited to this, and a diagonal line in FIG. The area of the area indicated by the reference numeral (a straight line that passes through almost the center of the traveling locus LOC and the traveling locus L
The area of a region surrounded by OC) may be used.

In the above-described embodiment, the road shape is determined by using the estimated curvature radius R and its rate of change RR, but the present invention is not limited to this. For example, the reciprocal of the estimated curvature radius or a single value is used. The absolute value of the regression coefficient a may be used as the road shape determination parameter PR.
In this case, the larger the curve of the road, the more the road shape determination parameter PR increases. Therefore, when the PR value is equal to or less than the straight line determination threshold PRLIM, it is determined that the road is substantially straight and the road shape determination parameter PR The rate of change RPR is
When the value is equal to or less than the constant curve determination threshold value RRLIM, it is determined that the radius of curvature is substantially constant.

Further, in the above-described second embodiment, the maximum fluctuation width LOCWID is used to calculate the reference threshold value LOCLIM0.
Average TH LOCWIDTHm and standard deviation LOCW
Although IDTHσ is used as the statistic, the variance may be used instead of the standard deviation. Also, the reference threshold LOCLIM0
May be calculated by the following equation (10a). LOCLIM0 = LOCWIDTHm + c ′ × LOCWIDTHσ (10a) Here, c ′ is a constant that is set to a value of, for example, about 1 to 2 by an experiment.

Also in the second embodiment, the output of the blinker switch 4 is supplied to the microcomputer 1 to determine whether or not the lane change has been performed (FIG. 7, step S).
20) may be performed based on the output of the turn signal switch 4. Further, when the maximum fluctuation width LOCWIDTH is used as the parameter indicating the stability of the vehicle behavior, the x coordinate of the traveling locus LOC does not necessarily have to be calculated.

Further, the warning to the driver is one which appeals to the driver's sight or hearing, but it is not limited to this, and a method of directly acting on the driver, for example, vibrating the seat or seat belt is used. May be applied with tension, or a specific scent may be released into the vehicle compartment, or the operating state of the air conditioner may be changed. As a result, the driver can be more surely notified of the deterioration of the driving situation.

In the above-described embodiment, the yaw rate is detected by the yaw rate sensor 2, but instead of this, the outputs of the wheel speed sensor and the vehicle speed sensor, or the steering angle sensor for detecting the steering angle of the steering wheel and the lateral acceleration. The yaw rate may be calculated using the output of the sensor or the like. In the case of a vehicle equipped with a navigation system, the information indicating the road shape (curvature radius) is
It may be obtained from the current position of the vehicle and the map information of the navigation system.

[0052]

As described in detail above, according to the first aspect of the present invention, a simple regression line is obtained based on the change in the amount of behavior related to the lateral movement of the vehicle. A regression line is set as the behavior standard. Further, the lateral displacement behavior amount of the vehicle is calculated based on the behavior amount, the behavior reference, and the vehicle speed, and based on the lateral displacement behavior amount, it is determined whether or not the driving situation of the driver is appropriate. Then, only when the vehicle is determined to road running is substantially either straight, or a radius of curvature substantially constant curved, and the driving situation of the driver is determined to be not appropriate, the operation It is determined that the driving situation of the person is abnormal.

Since the single regression line as the behavior reference can be obtained with a relatively small amount of calculation, the amount of calculation can be reduced as compared with the conventional device, and the operating condition can be reduced without using a dedicated microcomputer. Can be monitored. As a result, it becomes possible to monitor the operating condition by using a microcomputer provided in another system (for example, a navigation system), and the cost can be reduced. If a single regression line is used as the behavior criterion, considering that the lateral displacement behavior amount tends to increase when the road shape changes significantly, the road shape is almost linear or the radius of curvature is almost Since the abnormality determination is performed on the condition that the curve is constant, it is possible to accurately determine the driving situation even by using the single regression line as the behavior reference. According to the invention of claim 3, the vehicle speed or
Appropriate judgment can be made according to the radius of curvature, and unnecessary
A situation where an alarm is issued or conversely the alarm is delayed
Can be avoided. According to the invention of claim 4,
For example, it is suitable for the maximum fluctuation range during normal operation that varies depending on the driver.
It is possible to set the judgment threshold that
Can be done more accurately.

[Brief description of drawings]

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

FIG. 2 is a flowchart of arithmetic processing that realizes the functions of the blocks shown in FIG.

FIG. 3 is a diagram showing an example of a yaw rate of a vehicle and a transition of parameters calculated from the yaw rate.

FIG. 4 is a diagram showing a change in yaw rate when changing lanes.

5 is a block diagram showing a modified example of the configuration shown in FIG.

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

FIG. 7 is a flowchart of a calculation process that realizes the functions of the blocks shown in FIG.

FIG. 8 is a flowchart of determination threshold (LOCLIM) calculation processing.

9 is a diagram showing a map used in the processing of FIG.

10 is a flowchart of a modified example of the process of FIG.

[Explanation of symbols]

1 Microcomputer 2 Yaw rate sensor (behavior amount detecting means) 3 Vehicle speed sensor (behavior amount detecting means) 12 Behavior amount calculating part (behavior amount detecting means) 13 Behavior stability calculating part (behavior reference setting means, lateral displacement behavior amount calculating means) ) 14 road shape determination unit (road shape determination unit) 15 determination unit (driving condition determination unit) 21 alarm unit

Continuation of front page (58) Fields investigated (Int.Cl. ⁷ , DB name) B60K 28/00-28/16 G08B 21/00 G08B 21/06 G08G 1/16

Claims (5)

(57) [Claims] 1. A vehicle driving condition monitoring apparatus for monitoring a driving condition of a driver of a vehicle, a behavior amount detecting means for detecting a behavior amount related to a lateral motion of the vehicle, and a vehicle speed detecting device for detecting a vehicle speed of the vehicle. Means and a behavior standard setting means for determining a single regression line based on the change in the behavior amount and setting it as a behavior standard; and a lateral displacement behavior of the vehicle based on the behavior amount, the behavior standard and the vehicle speed. A lateral displacement behavior amount computing means for computing the amount, a driving situation determination means for determining whether or not the driving situation of the driver is appropriate based on the lateral displacement behavior amount, and a shape of a road on which the vehicle is traveling. And a road shape determining unit that determines whether the road is substantially linear or has a curved shape with a substantially constant radius of curvature, and the driving condition determining unit. More only when the operating condition of the driver is determined not appropriate for a vehicle driving condition monitoring apparatus characterized by operating conditions of the driver is determined to be abnormal. 2. The road condition determination means determines the shape of the road based on a radius of curvature estimated in accordance with the vehicle speed and the regression coefficient of the single regression line. Vehicle driving condition monitoring device. 3. The driving condition judging means has a judgment threshold value as a reference value for judging whether the driving condition of the driver is proper, and compares the judgment threshold value with the lateral displacement behavior amount. The vehicle driving condition monitoring apparatus according to claim 1, wherein it is determined whether or not the driving condition is appropriate, and the determination threshold is set according to the vehicle speed or the radius of curvature. 4. The driving condition judging means has a judgment threshold value as a reference value for judging whether the driving condition of the driver is proper, and compares the judgment threshold value with the lateral displacement behavior amount. While determining whether or not the driving situation is appropriate, from a plurality of measurement data of the lateral displacement behavior amount, a statistic including at least an average value is calculated, and the determination threshold is set based on the statistic. The vehicle driving condition monitoring device according to claim 1 or 2. Wherein said driving condition determining means, the driver of the vehicle to determine whether there is intention to the car line changes, the condition that there is no intention to change lanes to the driver, the driver of the vehicle for the driver state monitoring device according to the operating conditions and it determines that it is not appropriate from claim 1, wherein in any one of four.