JPH10307998A - Automatic steering device for vehicles - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an automatic steering device for vehicles which enables an automatic steering having high quality matched to various travelling environment without using extra components by using position data of the front vehicle when lane mark data of the travelling road cannot effectively be used. SOLUTION: A photographing device 10 photographs the front of its own car on a traveling road which displays a white line as image data. Laser data 30 measures distance distribution between objects moving toward the frond of its own vehicle as distance distribution data. A microcomputer 60 forms lane mark data on the basis of picture data, position data of the front vehicle placed in front of its own car on the basis of distance distribution data and judges whether lane mark data and position data are correct or not. A steering actuator 80 executes its automatic steering control on the basis of relevant lane mark data when it judges lane mark data to be correct, and it does the same on the basis of position data when it judges only position data to be correct.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an automatic steering device for a vehicle.

[0002]

2. Description of the Related Art Conventionally, in a vehicle automatic steering system of this type, a lane mark indicating a traveling path of the vehicle is detected by image processing based on a photographed image obtained by photographing the front of the vehicle. There is a vehicle in which the steering control of the vehicle is performed by utilizing the method described in Japanese Patent Application Laid-Open No. 4-273.
No. 301).

[0003]

However, in the above-mentioned automatic steering system, for example, when there is no lane mark on the traveling road surface, when the traveling road surface is dirty, when there is backlight from the traveling road surface, or when there is heavy rain, the lane There is a problem that a mark cannot be detected and automatic steering cannot be performed. For this,
A magnet is buried every 1 m on the traveling road surface, and by measuring the magnetic force of each magnet when the vehicle passes over each magnet, the traveling position of the vehicle on the traveling road surface is detected,
It is also conceivable to perform automatic steering control of the vehicle in accordance with the detected position.

[0004] However, in this case, the work of burying each magnet on the traveling road surface becomes enormous, and much time and cost are required to realize it. In order to address the above, the present invention utilizes the position data of the preceding vehicle when the lane mark data of the traveling road surface cannot be used effectively. It is an object of the present invention to provide an automatic steering device for a vehicle that enables high-quality automatic steering that matches a driving environment.

[0005]

In order to solve the above-mentioned problems, according to the first and second aspects of the present invention, the photographing means displays an image of the front of the host vehicle on the road surface on which a lane mark such as a white line is displayed. Shoot as data. The measuring means measures the distance distribution of the object located ahead of the host vehicle as distance distribution data.

The lane mark data forming means forms lane mark data based on the image data, and the position data forming means generates position data of a preceding vehicle located ahead of the own vehicle based on distance distribution data measured by the measuring means. Form. When the determining means determines that the lane mark data is correct, the steering control means performs automatic steering control of the own vehicle based on the lane mark data. On the other hand, when the determination means determines that only the position data of the preceding vehicle is correct, the steering control means performs automatic steering control of the own vehicle based on the position data.

[0007] Thus, when the lane mark data on the traveling road surface cannot be used effectively, the position data of the preceding vehicle is used to perform the automatic steering control of the own vehicle. As a result, it is possible to perform high-quality automatic steering control of the own vehicle that matches various traveling environments without using extra members. Here, according to the second aspect of the invention, when both the lane mark data and the position data are determined to be incorrect by the determining means, the warning means informs that the automatic steering control of the own vehicle is impossible. Alert.

[0008] Thus, the driver of the own vehicle can know the timing of switching from the automatic steering control to the manual steering correctly and with good timing. Thereby, even after the automatic steering control of the own vehicle is disabled, safe driving of the own vehicle can be reliably realized.

[0009]

DESCRIPTION OF THE PREFERRED EMBODIMENTS One embodiment of the present invention will be described below with reference to the drawings. FIG. 1 shows an example of an automatic steering device for a vehicle according to the present invention. The automatic steering device includes a photographing device 10, and the photographing device 10 is mounted on a front portion of the vehicle to photograph the front of the vehicle and generate a photographed output. Note that the photographing device 10 is configured by a CCD camera.

A yaw rate sensor 20 is provided at an appropriate position of the vehicle and detects a yaw rate of the vehicle. The laser radar 30 is of a scanning type and is provided at the front of the vehicle. Then, this laser radar 30 sequentially
Scans and emits light, measures the scan angle at the time of emission, and the propagation delay time from when the light is emitted and the light is reflected back to the reflector, returning from the measured time and the speed of light. The distance to the body is calculated and output as distance distribution data ahead.

The A / D converter 40 converts the photographing output of the photographing device 10, the detection output of the yaw rate sensor 20, and the scanning output of the laser radar 30 into digital signals and outputs them to the microcomputer 60. The vehicle speed sensor 50 detects the vehicle speed of the vehicle and outputs a pulse signal to the microcomputer 60 at a frequency proportional to the detected vehicle speed.

The microcomputer 60 executes the main control program according to the flowcharts shown in FIGS. 2 and 3, and executes the interrupt control program according to the flowchart shown in FIG. During the execution, the microcomputer 60 performs arithmetic processing required to drive and control the driving circuits 80a and 90a connected to the steering actuator 80 and the alarm 90, respectively. The main control program and the interrupt control program are stored in the ROM of the microcomputer 60 in advance.

The steering actuator 80 includes a drive circuit 80
a to control the steering angle of the front wheels of the vehicle. The alarm 90 is constituted by a speaker, and the alarm 90 is driven by a drive circuit 90a to sound and alarm. In this embodiment configured as described above, it is assumed that the vehicle is traveling on a traveling road surface. When the microcomputer 60 is in the operating state, the main control program is executed according to the flowcharts of FIGS. 2 and 3, and the interruption process of the interruption control program is executed at predetermined interruption times according to the flowchart of FIG. The predetermined interruption time is specified by each predetermined time value measured by a timer built in the microcomputer 60.

During the execution of the interrupt control program, at step 200, digital conversion data (hereinafter, referred to as "hereinafter") of the A / D converter 40 with respect to the photographing output of the photographing apparatus 10 is output.
Image data) is input to the microcomputer 60. Then, in step 210, white line data extraction processing is performed as follows.

The white line on the running road surface has a certain width and is characterized by being lighter than the asphalt on the running road surface. Therefore, only white line data is extracted using this feature. Specifically, it is assumed that the image data input to the microcomputer 60 in step 200 is, for example, the one shown in FIG. Here, the white line C indicating the center line on the traveling road surface is constituted by a plurality of white line portions Co drawn linearly at predetermined intervals. Each of the white lines located on the left and right sides of the traveling road surface is constituted by a single line as a left white line SL and a right white line SR.

Since the luminance of the white line SL and each white line portion Co on the traveling road surface is higher than the luminance of other portions on the traveling road surface, the luminance PL of the white line SL and the luminance P of the white line portion Co are obtained.
As shown in FIG. 6, Co is kept high in a pulse shape. In this case, the luminance of the white line SR is also high and is the same as the luminance PL. The luminance Q between the white line SL and the white line portion Co is:
It is lower than each brightness PL, PCo. In addition, each white line part Co
Similarly, the luminance during the period is low and is the luminance Q.

Therefore, in step 210, the white line S
The luminance changes at both ends in the width direction of L and SR are sequentially extracted along the white lines SL and SR as paired data as shown in FIG. Further, each change in luminance at both ends in the width direction of each lane mark portion Co is sequentially extracted along the white line C as a pair of data as shown in FIG. In this case, since the lane mark is located farther from the host vehicle as it goes to the upper part of the screen, the pulse-like luminance width of the white lines SL and SR and the white line part Co also becomes narrower as it goes to the upper part of the screen. .

When the processing in step 210 is completed in this way, in the next step 220, the general white line on the image becomes linear at a short distance, and is generally used as a straight line detection method. By the well-known Hough transform processing, each white line data of the white lines SL and C (see FIG. 7)
, Straight lines L1 and L2 are calculated as shown in FIG.

Thereafter, in step 230, a white line search process is performed. That is, the straight lines L1, L
2, the white line SL extracted in step 210 earlier,
The white line data of C is searched. When the white line data is bent, there is no white line data near both straight lines L1 and L2. For this reason, the search is performed while changing the search direction based on the inclination of the white line data found so far by the search (see the arrow shown in FIG. 9). Thus, a white line SL and a white line C ′ (corresponding to the white line C) are formed.

Then, in the overhead conversion process in step 240, these white lines SL and C are converted from the two-dimensional image after the search in FIG. 9 into an overhead view (FIG. 8) as viewed from directly above the traveling road surface. Then, in step 250, both white lines S
The central position representing the central position between L and C is between 5 m and 50 m every 5 m in the traveling direction of the vehicle, and the traveling direction of the lane mark data M in the center of the vehicle width direction (arrow S in FIG. 10). Is calculated as point-like lane mark data M based on the calculation of the amount of deviation (travel road data ΔD) from the reference lane mark (see FIG. 2).

Next, at step 260, the previous lane mark data M1 is updated with the following lane mark data M2, and at step 270, the current lane mark data M is updated with the lane mark data M1. Also,
The execution of the main control program according to the flowcharts of FIGS. 2 and 3 is performed as follows.

At step 300, lane mark data M1 is read. Next, in step 310, the approximate curve data Lc is calculated based on the lane mark data M1 (see FIG. 11). Specifically, the approximate curve data Lc is calculated by applying an approximate expression such as a cubic curve to the ten point-like data from 5 to 50 m constituting the lane mark data M1 by the least square method. You.

Next, at step 320, the residual ΔG between the lane mark data M1 and the approximate curve data Lc is calculated. Then, in step 330, it is determined whether or not the residual ΔG is equal to or smaller than the allowable residual ΔGo. Here, if the residual ΔG is not equal to or smaller than the allowable residual ΔGo, step 330
Is NO, in step 331 the flag is set to F = 0. This flag F = 0 indicates that the residual ΔG
Is incorrect and the approximate curve data Lc is incorrect. Therefore, the shape of the white line SL is not correct without continuity (smoothness).

On the other hand, the determination in step 330 is YE
If S, the residual ΔG is appropriate and the approximate curve data Lc is correct. This means that the shape of the white line SL has continuity (smoothness) and is correct. Therefore, in step 332, the lane mark data M2 is read, and in step 333, the existence range R of the lane mark data M1 is calculated based on the lane mark data M2.

Then, at step 340, it is determined whether or not the lane mark data M1 is within the existence range R. Regarding this determination, the system processing cycle 100 ms
In consideration of the fact that there is only a movable range of the vehicle within 100 ms (FIG. 12), when the white line data changes by a predetermined amount or less, the lane mark data M
1 is determined to be correct. Specifically, the amount of change for each distance is calculated, and when the amount of change is equal to or less than a predetermined amount, it is determined to be correct.

Then, if the determination in step 340 is NO, the flag F is set to F = 0. On the other hand, if the determination in step 340 is YES, the flag F is set to 1 in step 341. This F = 1 means that the existence range of the lane mark data M1 is correct under the appropriate residual ΔG. Thereafter, in step 350, it is determined whether or not the flag F = 1. Here, if the flag F = 1, that is, if the self-steering control of the own vehicle can be performed based on the lane mark data M1, the determination in step 350 is YES.

Along with this, in step 351, control processing of the steering actuator 80 based on the lane mark data M1 is performed. Therefore, the drive circuit 80a controls the drive of the steering actuator 80 based on the control output of the microcomputer 60. Thereby, the steering control of the own vehicle can be automatically performed on the lane mark data M1.

On the other hand, if NO is determined in step 350, it is considered that the lane mark data M1 is incorrect because no white line on the road surface is detected.
For this reason, in step 352, the digital conversion output of the A / D converter 40 with respect to the measurement output of the laser radar 30 is input to the microcomputer 60 as distance distribution data ahead of the vehicle (see FIG. 13).

Then, in step 353, the presence of a forward object is determined based on the high-density area in the distance distribution data. Next, in step 354, the digital conversion output of the A / D converter 40 with respect to the detection output of the yaw rate sensor 20 is input to the microcomputer 60 as a yaw rate output. The vehicle speed of the own vehicle is calculated based on the vehicle speed.

Thereafter, based on the yaw rate output and the vehicle speed, the traveling locus of the own vehicle is calculated in step 356, and the traveling locus of the forward object is calculated in step 357. Here, the calculation of the running locus of the front object can be obtained by adding the relative position of the front object to the running locus of the own vehicle. Then, when the traveling locus of the own vehicle overlaps the traveling locus of the front object, it is determined that the front object is the front vehicle after t seconds (see FIG. 14).

When it is determined that the vehicle is ahead of the vehicle, YES is determined in step 360. And
In step 361, the position data of the preceding vehicle is calculated based on the data on the own vehicle. Then
At step 362, the control processing of the steering actuator 80 is performed based on the position data of the preceding vehicle, and is output to the drive circuit 80a as control data.

Thus, the automatic steering control of the own vehicle can be performed based on the position data of the preceding vehicle instead of the lane mark data M1. As described above, when the lane mark data M1 is correct, the automatic steering control of the own vehicle is performed based on the lane mark data M1.
If data such as a white line on the road surface is not correctly obtained due to dirt on the road surface and the lane mark data M1 is not correct, the lane mark data M1 is replaced with the lane mark data M1. Steering control is performed.

As a result, without using extra members,
It is possible to perform high-quality automatic steering control of the vehicle in accordance with various driving environments. If the determination in step 360 is NO, both the lane mark data M1 and the position data of the preceding vehicle are unavailable. Therefore, in step 363, a sounding process of the alarm 90 is performed.

Based on this, the alarm 90 is driven by the drive circuit 90
a, and sounds to notify the driver that the automatic steering control of the own vehicle is impossible. As a result, the driver of the own vehicle can know the timing of switching from the automatic steering control to the manual steering correctly and with good timing. Thereby, even after the automatic steering control of the own vehicle is disabled, safe driving of the own vehicle can be reliably realized.

[Brief description of the drawings]

FIG. 1 is a block diagram showing an embodiment of the present invention.

FIG. 2 is a first part of a flowchart of a main control program executed by the microcomputer of FIG. 1;

FIG. 3 is a latter part of the flowchart of the main control program.

FIG. 4 is a flowchart of an interrupt control program executed by the microcomputer of FIG. 1;

FIG. 5 is a diagram showing a photographed image of a photographing device.

6 is a graph showing the luminance of a white line in the captured image of FIG. 5 in relation to the width direction position of a traveling road surface.

FIG. 7 is a diagram showing a state where a white line is extracted from the data of the captured image of FIG. 5;

8 is a diagram illustrating a state where each straight line is calculated based on the white line extraction data of FIG. 7;

FIG. 9 is a diagram showing a state in which a white line is searched based on each straight line in FIG. 8;

10 is a bird's-eye view after the search in FIG. 9;

FIG. 11 is a diagram for calculating approximate curve data Lc.

FIG. 12 is a diagram showing a state in which the existence range of lane mark data M1 is checked.

FIG. 13 is a diagram illustrating an example of output distance distribution data of a laser radar.

FIG. 14 is a diagram illustrating a process of determining a forward vehicle based on the traveling trajectories of the forward vehicle and the host vehicle.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 10 ... Photographing apparatus, 20 ... Yaw rate sensor, 30 ... Laser radar, 50 ... Vehicle speed sensor, 60 ... Microcomputer, 80 ... Steering actuator, 90 ... Alarm.

 ────────────────────────────────────────────────── ─── Continuing on the front page (72) Inventor Tetsuya Abe 1-1-1, Showa-cho, Kariya-shi, Aichi Prefecture Inside DENSO Corporation

Claims (2)

[Claims] 1. An image capturing means (10) for capturing, as image data, the front of an own vehicle on a traveling road surface displaying a lane mark such as a white line, and a distance distribution of an object located in front of the own vehicle as distance distribution data. Measuring means (30) for measuring; lane mark data forming means (200 to 270) for forming lane mark data based on the image data; and a front vehicle located ahead of the own vehicle based on distance distribution data by the measuring means. Position data forming means (353 to 357) for forming the position data, determination means (350, 360) for determining whether or not the lane mark data and the position data are correct, and steering control for automatically steering the own vehicle. Means (80), wherein the steering control means performs the automatic steering control based on the lane mark data by the determination means. An automatic steering apparatus for a vehicle, wherein the determination is performed based on the lane mark data based on the lane mark data, and the determination is performed based on the position data when determined that only the position data is correct. 2. An alarm unit (90) for alarming that the automatic steering control of the own vehicle is impossible when the determination unit determines that both the lane mark data and the position data are incorrect. The vehicle automatic steering device according to claim 1.