JPH11342764A - Operation state detection device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To detect accurately the doze of a driver from a detected steering amount. SOLUTION: The lateral G detected by a lateral G sensor 10 and the car speed detected by a car speed sensor 12 are supplied to a ECU 16. A map data is supplied to the ECU 16 by a navigation system 14. The lateral G caused by a road shape based on the map data and car speed data is estimated by the ECU 16 and removed form the detected lateral G. Thereby, only the lateral G component caused by an amended steering is extracted and the doze is judged. At the detection time of the doze, for example, a vehicle is conducted to a service area by the order of the navigation system 14.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

TECHNICAL FIELD The present invention relates to an operating state detecting device,
In particular, the present invention relates to a device for detecting an abnormal steering state of a driver.

[0002]

2. Description of the Related Art Hitherto, a device for detecting a driver falling asleep, etc., has been known. As a method of detecting a drowsy driving, a photograph is taken of a vehicle driver's face to detect an eye condition (blinking), a white line of a road is photographed using a CCD camera, and a lateral displacement of the vehicle from the white line is detected. To detect the deviation from the traveling zone, or to detect the correction steering cycle of the vehicle from the steering angle, yaw rate, lateral G, etc. of the vehicle,
For example, there is a comparison with a reference cycle.

[0003] In the technique of detecting an eye condition by photographing a vehicle driver, it is necessary to consider that the driver may be wearing glasses and that the driving position differs for each driver. There is a problem that processing becomes complicated.
Another problem is that hardware such as a camera and an image processing device is required.

The technique of detecting the lateral displacement of a vehicle from a white line is susceptible to the environment, such as making it difficult to obtain a white line image in rainy weather, and has the problem of an increase in hardware.

On the other hand, the technology of detecting the corrected steering cycle of a vehicle from the steering angle, yaw rate, lateral G, etc. of the vehicle and comparing it with a reference cycle can use existing hardware regardless of the weather. Therefore, it is considered to be advantageous in terms of cost.

Japanese Unexamined Patent Publication No. Hei 8-268190 discloses a technique in which a predetermined frequency component of a drowsiness change in a steering angle detection signal is extracted, and the absolute value of the extracted frequency component is added (integrated) to obtain a predetermined value. There is disclosed a technique for detecting a drowsy driving when the time until the value reaches a predetermined value is within a predetermined range.

[0007]

However, in order to extract the component resulting from the drowsy driving from the steering angle of the vehicle, other components included in the steering angle signal, for example, the component derived from the road shape, must be extracted. Must be separated.

In the above prior art, a relatively low frequency component which must be steered inevitably due to a curved road is removed by a filter. However, since the curvature of the curve is various, a steering signal is included. The frequencies of the included road shape components are also variously different, and it is difficult to completely separate them with a predetermined filter.

SUMMARY OF THE INVENTION The present invention has been made in consideration of the above-mentioned problems of the related art, and has as its object to reliably remove a component caused by a road shape from detected steering (steering angle, yaw rate, lateral G) components. It is another object of the present invention to provide an apparatus capable of detecting abnormal steering of a driver with high accuracy and detecting a drowsiness and the like.

[0010]

In order to achieve the above object, the present invention provides a steering detecting means for detecting a steering of a vehicle driver, a vehicle speed detecting means, a navigation system having map data, and the map data. And estimating means for estimating a time change of steering from the detected vehicle speed, and abnormality detecting means for detecting abnormal steering based on a difference between the estimated time change of the steering and the detected time change of the steering. It is characterized by the following. Instead of using a filter to remove a predetermined frequency component caused by the road shape as in the related art, a steering component caused only by the road shape is estimated using map data, and a difference from the detected steering component is determined. Specifically, since abnormal steering is detected based on the difference between the two, high-precision detection is possible regardless of the road shape.

[0011]

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

FIG. 1 is a block diagram showing the configuration of this embodiment. Lateral G sensor 10, vehicle speed sensor 12, navigation system 14, and ECU (electronic control unit) 1
6 is included.

The lateral G sensor 10 is a sensor for detecting the lateral G (lateral acceleration) of the vehicle caused by the driver's steering operation.
The detected lateral G is sequentially supplied to the ECU 16.

The vehicle speed sensor 12 sequentially supplies the detected vehicle speed to the ECU 16.

The navigation system 14 has map data storage means and means for detecting the current location of the vehicle, and displays the detected current location and map data on a display device such as a CRT (not shown). In addition, the navigation system 1
The device 4 may have a function of searching for a route to a destination set by the driver and guiding the driver by outputting the obtained route by display or voice. Map data is CD-RO
M or DVD, and the current location of the vehicle is GP
The detection can be performed by using the absolute position detection by S and the relative position detection based on the vehicle speed sensor and the direction sensor alone or in combination. This navigation system 14
The road shape data ahead of the predetermined distance including the detected current position is read from the CD-ROM or DVD and is sequentially supplied to the ECU 16. It is preferable that the road shape data be supplied including road type data.

The ECU 16 is constituted by a microcomputer, and estimates a temporal change in the lateral G caused by the road shape based on the map data supplied from the navigation system 14 and the vehicle speed data supplied from the vehicle speed sensor 12. Then, the estimated time change of the lateral G and the lateral G sensor 10
The time change of the actual lateral G supplied from the vehicle is compared, and the temporal change of the lateral G caused by the road shape is removed, and the temporal change of the lateral G not caused by the road shape, that is, caused by the correction steering by the driver. The temporal change of the horizontal G is extracted. The obtained time-varying component of the lateral G resulting from the corrected steering is compared with a reference value, and when a drowsy state of the driver is detected, a predetermined control signal is output. The predetermined control signal may be any signal as long as it urges the driver to wake up. For example, the control signal is output to the navigation system 14 to guide the user to the service area by voice. Is changed in a more stable direction.

FIG. 2 shows the ECU 16 according to this embodiment.
Is shown. First, based on the road type data supplied from the navigation system 14, it is determined whether or not the vehicle is traveling on a highway (S101). This determination takes into account the accuracy of map data on general roads, and this step is not necessary if the accuracy of a general road is comparable to that of a highway.

When the vehicle is traveling on a highway,
The horizontal G data is fetched from the horizontal G sensor 10 (S10
2) Detect a temporal change in the horizontal G.

FIG. 3 schematically shows a vehicle traveling on a highway having a curved road, and FIG. 4 shows an example of a temporal change of the lateral G when traveling on such a highway. It is shown. In FIG. 4, the horizontal axis is time, and the vertical axis is horizontal G.

Returning to FIG. 2 again, after detecting the time change of the lateral G, based on the map data (highway map data) supplied from the navigation system 14 and the vehicle speed data supplied from the vehicle speed sensor 12, the road speed is determined. The temporal change of the horizontal G caused by the shape is estimated (S103).

FIG. 5 shows an example of map data supplied from the navigation system 14. The map data can be expressed as a group of points (xi, yi) indicating the center position of the road at predetermined intervals. In the figure,
The point group of the expressway shown in FIG. 3 is schematically shown.

FIG. 6 shows the horizontal G estimated based on the map data (road shape data) and the vehicle speed shown in FIG.
Is shown over time. In the figure, the horizontal axis is time t, and the vertical axis is the estimated horizontal G. Generally, the lateral G is calculated from the curvature radius ρ of the road and the vehicle speed V.

[Number 1] can be calculated by the lateral G = V ² / ρ. That is, FIG. 6 shows the lateral G that would occur when steering along the road shape without correction steering.

Returning to FIG. 2 again, after estimating the time change of the lateral G caused by the road shape from the expressway map, the estimated value is compared with the actually detected lateral G. In the present embodiment, a wavelet function is used for comparison. That is, first, a wavelet function is created from the estimated temporal change of the lateral G (S104), and the wavelet function component is subtracted from the actually detected temporal change of the lateral G (S105).

FIG. 7 shows the estimated lateral G wavelet function shown in FIG. FIG. 8 also shows FIG.
7 shows a time change obtained by subtracting the wavelet function shown in FIG. 7 from the time change of the detection lateral G shown in FIG. The temporal change of the detected lateral G shown in FIG. 4 includes the lateral G change caused by the road shape and the lateral G change caused by the correction steering, and the temporal change of the estimated lateral G shown in FIG. Only the lateral G change due to the shape is included. Therefore, the lateral G change shown in FIG. 8 indicates only the lateral G change caused by the correction steering.

After the lateral G change caused by the road shape is removed from the temporal change of the detected lateral G as described above, it is determined whether or not the driver is drowsy using this data (S106). . Specifically, a frequency spectrum is calculated by performing FFT (Fast Fourier Transform) on a temporal change of the lateral G caused by the correction steering shown in FIG. 8, and attention is paid to a specific frequency component.

FIG. 9 shows a spectrum obtained by performing an FFT process on the lateral G change caused by the correction steering shown in FIG. In the figure, the horizontal axis is frequency and the vertical axis is intensity. When the driver does not fall asleep, the frequency of the correction steering is about 0.2 Hz, and when the driver falls asleep, the correction steering is stopped and the frequency is reduced to about 0.2 Hz.
It is about 1 Hz. Therefore, focusing on, for example, 0.1 Hz in the obtained frequency spectrum, if this frequency component is equal to or more than a predetermined value, it can be determined that the vehicle is dozing.

If it is determined that the driver is falling asleep (YES in S107), the process shifts to a process for awakening (or resting) the driver. That is,
Instruct the navigation system 14 to guide the vehicle to a nearby service area by voice (S108).
A command to increase the gain is issued to the 4WS control system (S109). Of course, other measures such as sounding an alarm may be taken.

After executing the process for awakening the driver, it is determined whether or not the driver has actually taken a break (S11).
0). This determination can be made based on, for example, whether or not the vehicle has stopped in the service area for a predetermined time or more. If the driver has not taken a break, the processes of S108 and S109 are repeated to urge the driver to wake up.

On the other hand, when the driver takes a break, it is further determined whether or not the driver has left the expressway (S11).
1). This determination can be made based on the road type supplied from the navigation system 14 as in S101. When the vehicle has left the expressway, the gain increased in S109 is returned to the original gain (S11).
2) Return to normal running. When it is determined that the driver has taken a break, the gain of 4WS may be returned to the original value regardless of whether the driver is traveling on the highway.

As described above, in the present embodiment, since the lateral G component caused by the road shape is estimated based on the map data, no matter what the road shape is (ie, what is the curvature radius of the curve) It is possible to reliably extract only the lateral G component caused by the correction steering.

In the present embodiment, the wavelet function is created from the estimated lateral G and is subtracted from the detected lateral G. However, it is also possible to use only the FFT processing without using the wavelet function.

FIG. 10 shows a spectrum obtained by performing the FFT processing on the time change of the detected lateral G shown in FIG. In the figure, the horizontal axis is frequency and the vertical axis is intensity. On the other hand, FIG. 11 shows the time change of the estimated lateral G shown in FIG.
The spectra obtained after processing are shown. The spectrum of FIG. 10 includes a frequency component caused by the correction steering and a frequency component caused by the road shape, and FIG. 11 includes only the frequency component caused by the road shape.
By subtracting FIG. 11 from 0, it is possible to extract only the frequency components caused by the correction steering. Then, by paying attention to a specific frequency component (for example, 0.1 Hz) of the frequency component caused by the corrected steering and comparing the frequency component with a reference value, it is possible to determine whether or not the driver is dozing.

When the lateral G and the vehicle speed are detected at the positions corresponding to the respective points of the point group indicating the road shape data shown in FIG. 5, the time change of the detected lateral G shown in FIG. It is also possible to directly subtract the temporal change of the estimated lateral G shown to extract only the corrected steering component. In this case, a wavelet function creation process for extracting a corrected steering component or an FFT
No processing is required.

Although the embodiment of the present invention has been described with respect to the horizontal G, the present invention is not limited to this, and any physical quantity representing steering, such as a steering angle and a yaw rate, can be used.

[0035]

As described above, according to the present invention,
Abnormal steering of the driver can be reliably detected regardless of the road shape.

[Brief description of the drawings]

FIG. 1 is a configuration block diagram of an embodiment.

FIG. 2 is an overall processing flowchart of the embodiment.

FIG. 3 is an explanatory diagram showing an example of a road shape.

FIG. 4 is a graph showing a temporal change of a lateral G detected during traveling on a road.

FIG. 5 is an explanatory diagram showing road shape data of map data.

FIG. 6 is a graph showing a temporal change of the horizontal G estimated from map data (road shape data).

FIG. 7 is a graph showing an estimated lateral G wavelet function shown in FIG. 6;

8 is a graph showing a temporal change of the lateral G obtained by subtracting the estimated lateral G of FIG. 7 from the detected lateral G of FIG. 4;

9 is a spectrum diagram obtained by subjecting the graph of FIG. 8 to FFT transformation.

FIG. 10 is a spectrum diagram obtained by performing FFT transform on the graph diagram of FIG. 4;

FIG. 11 is a spectrum diagram obtained by performing FFT on the graph diagram of FIG. 6;

[Explanation of symbols]

10 lateral G sensor, 12 vehicle speed sensor, 14 navigation system, 16 ECU.

Claims (1)

[Claims] 1. A steering detecting means for detecting steering of a vehicle driver, a vehicle speed detecting means, a navigation system having map data, and an estimating means for estimating a time change of steering from the map data and the detected vehicle speed. And an abnormality detection means for detecting abnormal steering based on a difference between the estimated time change of the steering and the detected time change of the steering.