JP2000158972A - Driving state monitor for vehicle - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a vehicle driving state monitor capable of accurately grasping vehicle behavior with a small arithmetic amount unlike before, and capable of precisely deciding a driving state. SOLUTION: By time-integrating a yaw rate YR detected for a monitoring time T1, plural yaw angles YA(i) are calculated (S12). A correlation coefficient COR between the yaw angles YA(i) and detection time t(i) corresponding to the yaw angles YA(i) is calculated (S13). When a lane change is not performed with the correlation coefficient COR not more than a threshold value CORLIM, it is decided that a driving state is abnormal, so that an alarm is given (S14, S15, 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 which monitors the driving condition of a driver of a vehicle and issues a warning when 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 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).

Further, the yaw rate and the vehicle speed of the vehicle are detected,
A driving condition monitoring device for a vehicle that determines a reference line for vehicle traveling based on the detected yaw rate and vehicle speed and determines abnormality in the vehicle driving condition using a parameter indicating a deviation between the actual traveling locus and the reference line is also known. (Japanese Unexamined Patent Publication No.
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 based on a physical quantity directly related to the behavior of the vehicle, and thus the deviation is not calculated due to, for example, a change in the characteristics of the vehicle (for example, the characteristics of the suspension or steering). There is a problem that an error occurs in the amount and the accuracy of determining the driving situation of the driver is reduced.

On the other hand, in the device described in Japanese Patent Application Laid-Open No. 8-249600, the driving condition is determined by using the yaw rate directly related to the behavior of the vehicle. The amount of calculation for calculating the reference line is large, and it is necessary to provide a dedicated microcomputer for monitoring the operation status. Therefore, it has been difficult to reduce the cost by reducing the number of microcomputers used.

The present invention has been made with a focus on this point, and a vehicle driving system capable of accurately grasping the behavior of a vehicle with a smaller amount of calculation than in the past and determining the driving state accurately. It is an object to provide a situation monitoring device.

[0007]

According to a first aspect of the present invention, there is provided a driving condition monitoring apparatus for monitoring a driving condition of a driver of a vehicle. Behavior amount detection means for detecting the amount,
A stability calculation unit that calculates a parameter indicating the stability of the behavior of the vehicle based on the behavior amount, and a determination unit that determines whether the driving situation of the driver is appropriate based on the parameter indicating the stability. Lane change determining means for determining whether or not the driver of the vehicle has changed lanes; driving when the lane change is determined not to be performed and the driving situation of the driver is not appropriate It is determined that the driving condition of the driver is abnormal.

According to this configuration, a parameter indicating the stability of the behavior of the vehicle is calculated from the amount of the behavior related to the lateral movement of the vehicle, and based on the parameter indicating the stability, whether the driving situation of the driver is appropriate or not is determined. Is determined. Then, it is determined that the lane change has not been performed, and when the driving situation of the driver is not appropriate, it is determined that the driving situation of the driver is abnormal. Since the parameter indicating the stability of the behavior of the vehicle can be calculated with a relatively small amount of calculation, it is necessary to accurately grasp the behavior of the vehicle with a smaller amount of calculation compared to the conventional device and to determine the driving condition accurately. Can be. As a result, for example, it is possible to monitor the operation status using a microcomputer of another system, and it is possible to reduce the cost.

According to a second aspect of the present invention, in the vehicle driving condition monitoring device according to the first aspect, the stability calculating means calculates a correlation coefficient between the behavior amount and a detection time of the behavior amount. It is characterized in that it is calculated as a parameter indicating the degree of stability. According to this configuration, the correlation coefficient between the lateral behavior amount of the vehicle and the detection time of the behavior amount is calculated as a parameter indicating the stability of the vehicle behavior.

[0010]

Embodiments of the present invention will be described below with reference to the drawings. (First Embodiment) 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. It is mounted on a vehicle having steering. In FIG. 1, a microcomputer 1 is connected to a yaw rate sensor 2 for detecting a yaw rate YR of the vehicle and a vehicle speed sensor 3 for detecting a running speed (vehicle speed) V of the vehicle. The output side of the microcomputer 1 is connected to an alarm unit 21 that issues an alarm when necessary while monitoring the driving condition of the driver. The alarm unit 21 includes, for example, a lamp, a buzzer, and a sound generator.

Signal memory unit 1 of microcomputer 1
1. The behavior amount calculation unit 12, the behavior stability calculation unit 13, and the determination unit 14 show the functions of the microcomputer 1 as blocks. The signal memory unit 11 stores the input signals from the yaw rate sensor 2 and the vehicle speed sensor 3, and updates the yaw rate data and the vehicle speed data for the past T1 seconds (for example, 10 seconds) from the present every T2 seconds (for example, 5 seconds). Output to the behavior amount calculation unit 12.

The behavior amount calculation unit 12 integrates the input yaw rate YR (see FIG. 3A) with respect to time to obtain a yaw angle YA.
(See FIG. 2B). That is, in the present embodiment, the yaw angle YA is used as the behavior amount related to the lateral movement of the vehicle. In addition, the behavior amount calculation unit 12
Calculates the average vehicle speed Vm for T1 seconds from the input vehicle speed V, and outputs it to the determination unit 14.

The behavior stability calculation unit 13 calculates a correlation coefficient R as a parameter indicating the stability of the vehicle behavior based on the input yaw angle YA according to the following equation 1, and calculates the absolute value of the correlation coefficient R. The COR is output to the determination unit 14. The correlation coefficient R is, for example, the time when the yaw angle YA (i) is detected is t.
(I) (time t uses, for example, the elapsed time from the start of monitoring period T1 which is a unit for calculating correlation coefficient R), time t (i) and yaw angle YA (i) Where “n” in Equation 1 is the number of data to be calculated.

(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, and StYA is the time t (i) and the yaw angle YA
The covariance of (i), St and SYA are each at time t
(I) and the standard deviation of the yaw angle YA (i). In addition,
In the following description, the absolute value COR of the correlation coefficient R is simply referred to as “correlation coefficient”.

The determining unit 14 determines that the correlation coefficient COR is equal to the threshold value CO.
When it is equal to or less than RLIM1 and the lane change is not performed, it is determined that the driving situation is abnormal, and a signal for instructing to issue an alarm is output to the alarm unit 21.

The threshold CORLIM1 is a yaw angle YA detected when simulating normal operation and abnormal operation.
, A correlation coefficient CORNR during normal operation and a correlation coefficient CORAB during abnormal operation are calculated, and CORNR> C
It is experimentally set so that ORLIM1> COLAB. In consideration of the fact that the correlation coefficient COR changes in accordance with the change in the vehicle speed V, in the present embodiment, the threshold CORLIM1 decreases as the average vehicle speed Vm increases in accordance with the average value Vm of the detected vehicle speed V. It is set to become.

The determination as to whether or not the lane change has been performed is performed as follows. That is, since it is known that when the lane is changed, the yaw rate YR changes as shown in FIG. 4, so that the yaw rate YR shows a peak in one direction (for example, rightward) and then the other direction (for example, rightward). For example, a time T until a peak (in the left direction) is indicated and a difference a (amplitude of the yaw rate) a between the peak values are measured.
When the time T is within the range of the predetermined times T1 and T2 (T1> T2) and the amplitude a is larger than the predetermined value A, it is determined that the lane change has been performed. The predetermined times T1, T2 and the predetermined value A are experimentally set by measuring the yaw rate YR when the lane is actually changed.

As described above, in the present 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) are calculated.
Since the driving situation is determined based on the correlation coefficient COR (i = 1 to n), the amount of calculation can be reduced as compared with the conventional method of calculating the reference line, and a dedicated microcomputer is used. It is possible to monitor the driving situation without any problem. As a result, the operation status can be monitored using a microcomputer provided in another system (for example, a navigation system), and cost can be reduced.

FIG. 2 is a flowchart showing the procedure of processing in the microcomputer 1. The functions of the above-described behavior amount calculation unit 12, behavior stability calculation unit 13 and judgment unit 14 are specifically implemented by the CPU of the microcomputer 1. Is realized by the processing of FIG.

First, in step S11, the yaw rate YR and the vehicle speed V for T1 seconds are captured every T2 seconds, and then the yaw rate YA 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 the correlation coefficient COR are calculated (step S13),
It is determined whether or not the calculated correlation coefficient COR is equal to or smaller than a threshold CORLIM1 (step S14). And COR ≦ CO
If RLIM1 and the correlation of the yaw angle YA with the elapsed time is low, it is determined whether or not the lane has been changed (step S15). If the lane has not been changed, the driving situation is determined to be abnormal and an alarm is issued to issue an alarm. A command is issued to the unit 21 (step S16). On the other hand, when COR> CORIM1 and the correlation of the time-series data is high, or when the lane change is performed, the present process is immediately terminated. FIGS. 3A and 3B
FIG. 4 is a diagram showing an example of a transition of a yaw rate YR and a yaw angle YA obtained by time-integrating the yaw rate YR.

In the present embodiment, the yaw rate sensor 2, the behavior amount calculation unit 12 (steps S11 and S12 in FIG. 2) and the behavior stability calculation unit 13 (step S13 in FIG. 2)
They correspond to behavior amount detection means and stability calculation means, respectively.
Determination unit 14 (Steps S14, S15, S16 in FIG. 2)
Correspond to the determination means and the lane change determination means.

(Second Embodiment) FIG. 5 is a block diagram showing a configuration of a vehicle driving situation monitoring apparatus according to a second embodiment of the present invention. The driving ability estimating unit 15 is added to the device of the form,
4 is replaced with a determination unit 14a. The other points are the same as the first embodiment.

The driving ability estimating unit 15 estimates the driving ability of the driver based on the correlation coefficient COR. This estimation is specifically performed as follows. First, the correlation coefficient COR is calculated m times (for example, 10 times) by changing the sampling time of the yaw rate YR, and the minimum value COR of the m COR values is calculated.
Calculate min and average value CORmean. And
Minimum value CORmin and two threshold values CORLIM3, COR
LIM4 (CORLIM3> CORLIM4), average value CORmean and threshold CORLIM2
Driving ability levels A to E shown in FIG.
To determine.

That is, the minimum value CORmin of the correlation coefficient
When CORmin ≧ CORLIM3, it is estimated that the driving ability is high (level A), and COR
When LIM3> CORmin ≧ CORIM4,
Estimated as moderate driving ability (Level B), CORmin
When <CORLIM4, it is estimated that the driving ability is low (level C). On the other hand, the average value of the correlation coefficient CORm
When CORmean ≧ CORLIM2, it is estimated that the driving ability is high (level D),
When CORmean <CORLIM2, it is estimated that the driving ability is low (level E).

Based on the driving ability levels A, B, C and D, E, a comprehensive judgment of the driving ability is performed as shown in FIG. That is, when the level is A or D, it is determined to be "normal", when the levels are B and E, it is determined to be "warning level 1", and when the levels are C and E, it is determined to be "warning level 2".

Here, each threshold CORLIM2, CORL
IM3 and CORLIM4 calculate a correlation coefficient CORNR during normal operation and a correlation coefficient CORAB during abnormal operation based on the yaw angle YA detected when simulating normal operation and abnormal operation, and CORNR> CORLI
M2> CORAB, CORNR>CORLIM3> CO
It is experimentally set so that RLIM4> CORAB. In consideration of the fact that the correlation coefficient COR changes in accordance with the change in the vehicle speed V, in the present embodiment, as the vehicle speed average value Vm increases, the thresholds CORLIM2, CORLIM3, CORLIM4
Is set to be small.

In this way, by determining the driving ability of the driver based on the minimum value CORmin and the average value CORmean of the plurality of correlation coefficients COR, it is possible to more accurately determine (estimate) the driving ability. .

The determining unit 14a determines that the driving ability is at the warning level 1
Alternatively, when the warning level is 2 and the lane change is not performed, it is determined that the driving situation is abnormal, and a signal for instructing to issue a warning is output to the warning unit 21.

FIG. 6 is a flowchart showing a procedure of processing executed by the microcomputer 1 of the present embodiment. The behavior amount calculating section 12 and the behavior stability calculating section 1 shown in FIG.
3. The functions of the driving ability estimating unit 15 and the determining unit 14a are specifically described in FIG.
Is realized by the processing of.

First, in steps S21, S22, and S23, the same processing as in steps S11, S12, and S13 of FIG. 2 is performed to calculate the correlation coefficient COR. Next, an average CORN of the m correlation coefficients COR and a minimum C
ORmin is calculated (step S24). Step 2
At 5, it is determined whether the average CORmean is smaller than the threshold CORLIM2, and CORmean <CORLI.
If M2, the minimum value CORmin is further reduced to a threshold value C
It is determined whether it is smaller than ORLIM3 (step S
26). If the answer to step S25 or S26 is negative (NO), that is, CORmean ≧ CORL
IM2 (level D) or CORmin ≧ CORIM
When it is 3 (level A), the operation status is determined to be normal, and this process is immediately terminated.

If the answers in steps S25 and S26 are both affirmative (YES), the minimum value CORmin
Is smaller than the threshold CORLIM4,
If Rmin ≧ CORIM4, warning level 1
(Step S28), and CORmin <CORL
If it is IM4, it is determined that the warning level is 2 (step S29). Next, it is determined whether or not a lane change has been performed (step S30). If the lane change has been performed, this process is immediately terminated. If the lane change has not been performed,
The alarm unit 21 determines that the driving situation is abnormal and issues an alarm.
(Step S31).

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 sounding.
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 present embodiment, the average value CORmean and the minimum value CO of a plurality of correlation coefficients COR are obtained.
By determining the driving ability of the driver based on Rmin, it is possible to more accurately determine (estimate) the driving ability, and it is possible to perform more detailed warning and fail-safe action.

In the present embodiment, the yaw rate sensor 2, the behavior amount calculation unit 12 (steps S21 and S22 in FIG. 6) and the behavior stability calculation unit 13 (step S23 in FIG. 6)
They correspond to behavior amount detection means and stability calculation means, respectively.
The driving ability estimating unit 15 and the determining unit 14a (steps S24 to S31 in FIG. 6) correspond to a determining unit and a lane change determining unit.

(Other Embodiments) The present invention is not limited to the above-described embodiment, and various modifications are possible. For example, in the embodiment described above, the correlation coefficient COR as a parameter indicating the behavior stability of the vehicle is calculated by using the yaw angle YA as the behavior amount related to the lateral movement of the vehicle, but the yaw rate YR or the lateral displacement amount is calculated. The DIF may be used as a behavior amount related to the lateral movement of the vehicle. Here, the lateral displacement DIF is the average value Vm of the vehicle speed and the yaw angle Y.
A is calculated by time-integrating the lateral displacement differential amount DYK calculated by applying A to the following equation.
In the example corresponding to (a) and (b), the transition is as shown in FIG.

DYK = Vm × sin (YA) The warning to the driver used was one that appeals to the driver's sight or hearing. However, the warning is not limited to this. For example, a method that acts directly on the driver, for example, The seat 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 2. Alternatively, 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 acceleration may be used. The yaw rate may be calculated using the output of a sensor or the like.

[0038]

As described above in detail, according to the present invention, a parameter indicating the stability of the behavior of the vehicle is calculated from the amount of the behavior related to the lateral movement of the vehicle, and the driver is determined based on the parameter indicating the stability. It is determined whether or not the driving situation is appropriate. Then, it is determined that the lane change has not been performed, and when the driving situation of the driver is not appropriate, it is determined that the driving situation of the driver is abnormal. Since the parameter indicating the stability of the behavior of the vehicle can be calculated with a relatively small amount of calculation, it is necessary to accurately grasp the behavior of the vehicle with a smaller amount of calculation compared to the conventional device and to determine the driving condition accurately. Can be. As a result, for example, it is possible to monitor the operation status using a microcomputer of another system, and it is possible to reduce the cost.

[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 flowchart of an arithmetic process for realizing the functions of the blocks shown in FIG.

FIG. 3 is a graph showing a behavior amount (YR, Y) related to a lateral motion of a vehicle.
FIG. 6 is a diagram illustrating an example of a transition of (A, DIF).

FIG. 4 is a diagram showing a transition of a yaw rate when a lane is changed.

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

6 is a flowchart of an arithmetic process for realizing the functions of the blocks shown in FIG.

FIG. 7 is a diagram for explaining a method of determining driving ability.

[Explanation of symbols]

 Reference Signs List 1 microcomputer 2 yaw rate sensor (behavior amount detecting means) 12 behavior amount calculating section (behavior amount detecting means) 13 behavior stability calculating section (stability calculating means) 14 determining section (determining means, lane change determining means) 15 driving capability Estimation unit (judgment means) 21 Alarm unit

Claims (2)

[Claims] 1. A vehicular driving situation monitoring device for monitoring a driving situation of a driver of a vehicle, comprising: a behavior quantity detecting means for detecting a behavior quantity relating to a lateral motion of the vehicle; Stability calculating means for calculating a parameter indicating stability; determining means for determining whether or not the driving situation of the driver is appropriate based on the parameter indicating stability; and a lane change by the driver of the vehicle. A lane change determining means for determining whether or not the vehicle has been changed; determining that the lane change has not been performed; and determining that the driving condition of the driver is abnormal when the driving condition of the driver is not appropriate. An operating condition monitoring device for a vehicle, characterized by making a determination. 2. The vehicle according to claim 1, wherein the stability calculating means calculates a correlation coefficient between the behavior amount and a detection time of the behavior amount as a parameter indicating the stability. Operation status monitoring device.