JPH0710003A - Hindrance detecting device for railroad vehicle - Google Patents

Abstract

PURPOSE:To detect a hindrance located farther than braking distance and measure the distance to the hindrance for improving safety and preventing a system becoming complicated. CONSTITUTION:An infrared camera 11 for taking pictures of the front direction of a running railroad vehicle and a laser distance sensor 19 for measuring the distance to a hindrance existing on the course are installed. The image taken by the infrared camera 11 is memorized in an image memory 13 through an A/D converter 12. A vehicle speed sensor 1 for detecting the speed of a running vehicle and a mileage sensor 12 for detecting a present position and a route data memory 16 in which route data are memorized beforehand are provided. Based on the detected signals from the vehicle speed sensor 1 and the mileage sensor 15 and the route data memorized in the memory 16, whether or not hindrances exist in the image memorized in the image memory 13 is judged by an calculation device 14, and the result of this judgment and the distance to the hindrance measured by the laser distance sensor 19 mentioned above is output by an output device 17 and informed to the driver.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an obstacle detection device for a railway vehicle for detecting an obstacle existing on the course of a running railway vehicle.

[0002]

2. Description of the Related Art When operating trains, trains, and other railway vehicles,
In order to ensure the safety of the vehicle, it is possible to photograph the front of the traveling vehicle and determine the presence or absence of obstacles on the route from the photographed image. In this case, the problem is the distance La to the obstacle. When the braking distance of the vehicle is L, if L <La, the collision can be avoided. However, if L> La, a collision will occur even if an obstacle can be detected. Therefore, the obstacle is meaningless unless it is detected beyond the front L (braking distance) of the vehicle. Furthermore, considering the margin ΔL for the driver to judge, (L + Δ
It is necessary to detect obstacles beyond L).

The following two methods are conceivable as a method of determining the position of the rail ahead (L + ΔL) in the screen of the photographed image. (1) The distance between the two rails is constant. Using this feature, the number of pixels between rails in front of (L + ΔL) is calculated in advance from the angle of view of the camera, and the position of the rail in front of (L + ΔL) in the screen is obtained.

However, this method is effective only when the line is straight, and the accurate position cannot be calculated when the line is curved. Therefore, the following method can be considered. (2) The distance is calculated by triangulation using two cameras, and the position of the rail in front of (L + ΔL) is determined.

When the position of the front rail (L + ΔL) is known on the screen, an area (obstacle search area) for inspecting for the presence or absence of an obstacle is set around the position, and image processing is performed within the same area. The presence or absence of obstacles is determined by things.

[0006]

In the above methods (1) and (2), it is necessary to identify and recognize the position of the rail within the screen by performing image processing and the like. Moreover,
The position in front of (L + ΔL) is obtained by the methods (1) and (2). However, in (1), it is impossible to determine the position when the track is curved, and in (2), the distance between the cameras cannot be equal to or larger than the lateral width of the vehicle body. There is a problem in accuracy because the distance between cameras cannot be taken sufficiently.
Furthermore, since two cameras are used, the entire system becomes complicated, the time required for calculation is prolonged, and the cost is increased.

Further, the obstacle detected by performing the image processing in the obstacle searching area is in front of the vehicle (L
It is an obstacle existing between + ΔL) and the point at infinity (theoretically). The driver's response changes according to the distance La to the obstacle. If La is a little larger than (L + ΔL), it is necessary to brake suddenly. If La is considerably larger than (L + ΔL), you can ring the whistle and watch the situation.
As a method of knowing the distance to the obstacle, it is possible to calculate it from the size of the obstacle on the screen and the angle of view of the camera, but the size of the obstacle varies widely and this method is impossible. Is.

On the other hand, the distance (L + ΔL) is determined by the train speed, and it is more effective to change the distance (L + ΔL) depending on the vehicle speed. The present invention has been made in consideration of the above points, and from the vehicle speed, (L + ΔL)
Can calculate the distance of the rails, calculate the position of the rail in front of (L + ΔL) in the screen accurately and in a short time, set the obstacle search area, and accurately determine the existence of obstacles in the area. At the same time, it is an object of the present invention to provide an obstacle detection device for a rail vehicle that can measure the distance to an obstacle and notify the driver of the obstacle.

Further, according to the present invention, the area to be inspected for the presence of the preceding train can be accurately determined in a short time, the presence or absence of the preceding vehicle in the area beyond the braking distance can be reliably determined, and the speed limit is calculated. It is an object of the present invention to provide a highly safe obstacle detection device for a railway vehicle that can control vehicle speed.

[0010]

[Means for Solving the Problems]

(First Invention) An obstacle detection device for a railroad vehicle according to a first invention includes an image input device installed so as to capture the front of a traveling railcar, and an image captured by the image input device. An image memory for storing, position detecting means for detecting the current position of the traveling vehicle, speed detecting means for detecting the speed of the traveling vehicle, and how the rail is in the image captured by the image input device. The route data memory that stores in advance as route data corresponding to the vehicle position detected by the position detection means, and the distance at which a collision with an obstacle can be avoided by the speed signal detected by the speed detection means. Further, the obstacle search area is displayed in the screen obtained by the image input device from the position detection signal detected by the position detecting means and the route data stored in the route data memory. Setting means for setting an obstacle search area, determining means for determining whether or not there is an obstacle in the obstacle search area for the image stored in the image memory, and vibration for detecting the vibration of the vehicle. The angle detection means, the distance measurement means for measuring the distance from the vehicle to the obstacle, the position of the obstacle on the screen obtained by the image input device, and the vibration angle obtained by the vibration angle detection means Calculation means for calculating the direction of the object, distance measurement direction control means for controlling and driving the distance measurement direction of the distance measurement means based on the direction information obtained by this calculation means, and obstacles determined by the determination means. It is characterized in that it is provided with notifying means for notifying the driver of the presence or absence and the distance to the obstacle measured by the distance measuring means.

(Second Invention) An obstacle detection device for a railway vehicle according to a second invention is an image input device installed so as to capture the front of a traveling railway vehicle, and an image captured by this image input device. An image memory for storing an image, a position detecting means for detecting the current position of the traveling vehicle, a speed detecting means for detecting the speed of the traveling vehicle, and a rail in the image captured by the image input device. It is possible to avoid a collision with a preceding train by a route data memory that stores in advance how the vehicle looks like as route data corresponding to the vehicle position detected by the position detecting unit and a speed signal detected by the speed detecting unit. Within the screen obtained by the image input device from the position detection signal detected by the position detecting means and the route data stored in the route data memory. A preceding train search area setting means for setting a row train search area, a judgment means for judging whether or not there is a preceding train in the preceding train search area for the image stored in the image memory, and a preceding train from the vehicle. And a means for limiting the vehicle speed by calculating a speed limit based on the distance to the preceding train measured by the distance measuring means.

[0012]

[Action]

(First invention) Route data obtained by converting the laying condition of a railway into data is stored in advance in a route data memory, and the current position (for example, the distance from a reference point) of the running vehicle is detected by the position detecting means. By reading the stored contents of the route data memory, it is possible to confirm the laying condition of the line in front of the vehicle. That is, when three-dimensional coordinates with the current position as the origin are set, the position p of the rail ahead of the arbitrary distance R
The coordinates of can be uniquely determined. An image input device installed so as to photograph the front of the traveling vehicle, for example, an infrared camera, can be used to find where on the screen the point p is seen by mathematical coordinate conversion calculation. Therefore, it is possible to know where the rail ahead of the arbitrary distance R can be seen in the screen. Therefore, an obstacle search area with a finite size is set around the position of the rail in the screen when R = (L + ΔL), and image processing is performed in the area to determine the presence or absence of obstacles. judge. Furthermore, the direction of the obstacle is calculated from the position of the obstacle on the screen and the output of the vibration angle detection sensor, and the distance to the obstacle is measured by controlling and driving the distance measuring direction of the distance measuring means. Then, the driver is informed of the presence or absence of an obstacle based on the above determination result and the measured distance to the obstacle.

By using the route data as described above, even if the front line is straight or curved, (L
+ ΔL) It is possible to accurately calculate the position of the front rail in the screen. Further, the braking distance L can be calculated from the speed of the vehicle obtained by the speed detecting means, and the margin ΔL can be obtained from the braking distance L. For example, the margin ΔL is set to a value of about 30% of the braking distance L. Thereby, (L + ΔL) can be set according to the vehicle speed, and the obstacle can be detected more efficiently. Further, by performing image processing or the like, there is no need to identify and recognize the position of the rail within the screen, and the calculation time can be greatly reduced. Further, since the distance to the obstacle is known by performing the distance measurement, the driver can take appropriate measures according to the distance to the obstacle.

(Second Invention) An image in front of the vehicle taken by the image input device is digitized and stored in the image memory. The image data stored in the image memory is binarized by various known image processing algorithms, the rail portion is extracted as a binary image, and the binary image is recorded in the image memory. Further, the current position of the vehicle is read from the position detecting means, and the route data corresponding to this current position is read from the route data memory. Then, the preceding train search area is set in the screen using the binary image and the route data, and various image processing is performed in the set area to determine the presence or absence of the preceding train.

When the preceding train is detected, the distance measuring means calculates the distance to the preceding train. That is, the distance measuring means measures the distance to the preceding train by image processing using the route data read from the route data memory. According to the distance to the preceding train obtained by this measurement processing, the speed limit of the vehicle is calculated by calculation processing,
Limit the vehicle speed to the proper speed. This allows the distance from the preceding train to be kept at a safe distance.

[0016]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described below with reference to the drawings. (First Embodiment) FIG. 1 shows the construction of an obstacle detection device for a railway vehicle according to a first embodiment of the present invention, which is equipped with an infrared camera 11 as an image pickup means. The infrared camera 11 is attached to the head position of the train car 10 as shown in FIG. 2, and photographs the front of the car. A video signal captured by this camera is converted into digital data by an A / D (analog / digital) converter 12. The output signal from the A / D converter 12 is written in the image memory 13 based on the instruction from the arithmetic unit 14.

The arithmetic unit 14 has a vehicle speed signal from a vehicle speed sensor 1 for detecting the speed of the vehicle 10, a kilometer distance (distance from a reference point) signal from a kilometer sensor 15 for detecting the current position of the vehicle 10, Further, the route data stored in advance in the route data memory 16, the vibration angle of the vehicle 10 from the gyro sensor 18, and the distance to the obstacle from the laser distance measuring sensor 19 are supplied.

The vehicle speed sensor 1 and the distance sensor 15
Is attached to the wheels of the vehicle 10 as shown in FIG. 2, and outputs, for example, speed data and distance data corresponding to the number of revolutions thereof.

Further, the route data memory 16 stores route data showing a line shape corresponding to a kilometer as shown in FIGS. 3 (a) and 3 (b). In the example shown in FIG. 3, there is a right (R) curve with a radius of 50 m and an intersection angle of 90 ° from 0 to 100 km, a straight line of 400 m follows, and a radius of 100 from 500 to 700 km.
It is a left (L) curve with m and an intersection angle of 60 °, then 100
A straight line of m continues. In FIG. 3 above, BTC (Bi
ginning of Transition Curve) is the starting point of the relaxation curve, BC
C (Biginning of Circular Curve) is the starting point of the circular curve, E
CC (END of Circular Curve) is the end of the circular curve, ETC
(END of Transition Curve) indicates the end point of the relaxation curve. Although the route data shown in FIG. 3 is for the horizontal direction, the route data for the vertical direction is also recorded in the route data memory 16 in the same way, for example, the slope of the line corresponding to about kilometer.

The gyro sensor 18 and the laser distance measuring sensor 19 are installed near the infrared camera 11 as shown in FIG. 2, and the gyro sensor 18 outputs the yawing angle and the pitching angle of the vehicle 10 at the camera mounting position. To do.

The arithmetic unit 14 in FIG. 1 is roughly divided into four parts, that is, a binarizing means 14a, an obstacle search area setting means 14b, and an obstacle presence / absence determining means 14c.
And a laser beam direction setting means 14d.
The binarization unit 14 a obtains image data from the image memory 13, binarizes the image data by various image processing algorithms, and records the binary image in the image memory 13. The obstacle search area setting means 14b uses the vehicle speed signal from the vehicle speed sensor 1, the kilometer distance signal from the kilometer distance sensor 15, the route data from the route data memory 16 and the binary image from the image memory 13 to determine whether there is an obstacle. The area (obstacle search area) to be inspected is set on the screen. The obstacle presence / absence determining unit 14c determines presence / absence of an obstacle by performing various image processes in the area (obstacle search area). The laser beam direction setting means 14d calculates the direction of the obstacle from the position of the obstacle on the screen and the output of the gyro sensor 18, and outputs it to the laser distance measuring sensor 19.

The laser distance measuring sensor 19 comprises a laser beam direction setting means 19a and a laser distance measuring means 19b. The laser beam direction setting means 19a brakes the direction of the laser beam based on the direction of the obstacle outputted by the laser beam direction setting means 14d. The laser distance measuring means 19b is the laser beam direction control driving means 1 described above.
Laser distance measurement is performed in the direction determined by 9a, and the distance to the obstacle is output to the arithmetic unit 14.

Then, the presence / absence of the obstacle and the distance to the obstacle determined by the arithmetic unit 14 are sent to the output device 17 to notify the driver. FIG. 4 shows a flow of operations in the arithmetic unit 14. In step 101,
Set the distance in kilometers corresponding to the current position of the vehicle. In the next step 102, the output of the infrared camera 11 is digitized by the A / D converter 12 and stored in the image memory 13. In step 103, the image data stored in the image memory 13 is binarized by various known image processing algorithms to extract the rail portion as a binary image, and the binary image is recorded in the image memory 13.

In step 104, the kilometer corresponding to the current position of the vehicle is read from the kilometer sensor 15. In step 105, the route data corresponding to this kilometer is read from the route data memory 16. Then in step 120,
The vehicle speed signal (vehicle speed) detected by the vehicle speed sensor 1 is read. In step 106, an obstacle search area is set on the screen according to a method described later using the binary image and the route data. In step 107, the presence / absence of an obstacle is determined by performing various kinds of image processing described later in the obstacle search area.

Then, in the next step 108, it is judged whether or not the obstacle is detected in the above step 107, and if the obstacle is detected, the process proceeds to step 109, and the direction of the laser beam is set by the method described later. , Step 1
Laser distance measurement is performed at 10 to measure the distance to the obstacle. In step 111, output display is executed to inform the driver of the existence of the obstacle and the distance to the obstacle. In step 112, it is judged whether or not the operation is completed. If the operation is completed, this processing is ended.

Next, a method for setting the obstacle search area in step 106 will be described. FIG. 5 shows an example of the processing flow of the above method. Step 201 corresponds to steps 104 and 105. By this operation, the laying condition of the track ahead in the traveling direction can be known. Step 205 is a braking distance L corresponding to the vehicle speed.
Then, the sum of the margins ΔL for the driver to judge is calculated. The braking distance L can be calculated from the vehicle speed, the vehicle body weight, the braking characteristics, etc., and the margin ΔL may be set to about 30% of the braking distance L, for example.

Next, in step 202, the three-dimensional coordinate system 20 as shown in FIG. 6 is set, and the coordinates (x, y, z) of the center point p21 of the left and right rails in front of (L + ΔL) m are calculated. . At step 203, the infrared camera 11 makes a point p2.
When 1 is photographed, the position (m, n) 24 where the point p is visible in the image 23 is calculated as shown in FIG.

FIG. 7 shows a digital image stored in the image memory 13 after being photographed by the infrared camera 11.
The coordinates (m, n) 24 are given by a mathematical coordinate conversion calculation using the camera mounting position, the angle, and the angle of view of the camera, which can be uniquely determined by the three-dimensional coordinate system 20, as parameters. In step 204, the obstacle search area 25 is set around the position (m, n) 24 where the point p can be seen on the screen. The size of the obstacle search area is arbitrary, but if it is too large, it is meaningless, and if it is too small, the area to be inspected is not included, which is a problem.

An example of a method for setting the size of the obstacle search area will be described below. Considering the construction limit of the vehicle, it is considered that there will be no problem if there is no obstacle in the area of 3.8 m × 3.8 m on the track. Therefore, the size of the 3.8 m × 3.8 m region in front of (L + ΔL) m in the screen is calculated. For example, (L + ΔL) is 1500
Consider the case of m. This depends on the number of pixels in the horizontal and vertical directions and the angle of view in the horizontal and vertical directions of the camera. The number of pixels is 640x486, and the angle of view is 3.5 ° x2.7.
If the angle is °, the area of 3.8 m × 3.8 m ahead of 1500 m has a size of 27 × 27 (pixels) in the screen. Therefore, as shown in FIG. 7, an obstacle search area 25 having a size of 27 × 27 (pixels) is set around the coordinate (m, n) 24. Even when the distance (L + ΔL) is other than 1500 m, the size of the obstacle search area on the screen can be determined in the same manner.

In this embodiment, the coordinate (m, n) 24 is calculated every time using the data showing the laying condition of the track as shown in FIG. 3 as the track data.
It is also possible to store the coordinate (m, n) 24 obtained in 03 in association with the distance of about kilometer and use it as route data. That is, the route data may be stored in any form as long as the coordinates (m, n) 24 corresponding to the kilometer are known.

The method shown in FIG. 5 is possible when the influence of vehicle vibration can be ignored. Actually, the position and angle of the camera attached to the vehicle change due to the vibration, which causes a positional shift in the image. That is, an image different from the image when the vehicle is stationary is captured. Step 2 above
The position (m, n) where the point p obtained in 03 is visible in the image 23
24 is the position in the screen when the vehicle is stationary. When the camera mounting position and the angle greatly change due to vibration, the position (m, n) 24 is displaced in the screen, which is a problem. Therefore, FIG. 8 shows a processing flow of a method for eliminating the influence of vibration.

Step 201 in FIG. 8 has the same contents as step 201 in FIG. In step 301, the rail image 30 shown in FIG. 9 in which the left and right rails are drawn using the route data stored in the route data memory 16 is generated on the image memory 13. The rail image 30 is, for example, a binary image in which a rail is drawn with a bit “1” on the background of a bit “0”.

An example of a method of generating the rail image 30 is shown below. In step 202 of FIG. 5, not only a point in front of (L + ΔL) m but also a point in front of 200 to 1500 m is calculated every 100 m, and in step 203, coordinates on the screen are obtained. Since these are the coordinates at the center of the rail, the coordinates of the left and right rails are obtained by adding the number of inter-rail pixels according to the distance. The coordinates are connected by a straight line to generate left and right rail images 200 to 1500 m ahead. Needless to say, it is not always necessary to calculate 200 to 1500 m every 100 m, and it is possible to change it flexibly. Further, as described above, the above step 3
It is also conceivable that the coordinates of the left and right rails in 100 m ahead of 200 to 1500 m calculated in 01 are stored in association with the distance in kilometers and used as route data.
In addition, there is no problem in storing any route data as long as the rail image 30 corresponding to about a kilometer can be generated.

In step 302, the shift amounts ρm and ρn are obtained by correlating the binary image 31 extracted from the rail portion shown in FIG. 10 generated in step 103 with the rail image 30. The binary image 31 is shown in FIG.
As shown in, for example, the rail 34a part is a bit "1".
It is a binary image as represented by. The amount of deviation ρm3
As shown in FIG. 11, 5 m is the number of pixels displaced in the horizontal direction (m-axis direction) between the actual rail 34 and the drawn rail 33, and the deviation amount ρn35n is displaced in the vertical direction (n-axis direction). The number of pixels.

As shown in FIG. 9, a window 32 is set in the rail image 30 so as to surround the drawn rail 33. The correlation value in the window 32 is obtained by shifting one image vertically and horizontally from the state in which the two images completely overlap. Specifically, a logical product image of the rail image 30 and the binary image 31 is obtained, and the sum total of bits “1” in the window 32 is set as the correlation value. The shift amount in the horizontal direction when the correlation value is maximum is regarded as the shift amount ρm, and the shift amount in the vertical direction is regarded as the shift amount ρn. The method of setting the window for obtaining the correlation does not necessarily have to be the above method, and the deviation amounts ρm, ρn
Any window may be used as long as it can be accurately obtained.

Next, at step 303, the above deviation amount ρm35.
The obstacle search area 25 is set by correcting m and ρn35n. As shown in FIG. 12, a point p21 at the center of the rail in front of (L + ΔL) m is visible in the image 23 at a position (m,
n) 24 is corrected by the deviation amounts ρm35m and ρn35n, and a point (m + ρm, n + ρn) 36 is centered (L + Δ
The obstacle search area 25 having a size corresponding to L) is set. That is, an accurate obstacle search area 25 can be set by obtaining the amount of deviation on the screen and correcting the amount of deviation.

FIG. 13 shows an example of the obstacle presence / absence determination processing in step 107 of FIG. This apparatus uses an infrared camera 11 as an image pickup means. Infrared camera 11
When shooting obstacles such as automobiles, the high temperature part such as the engine is brightly imaged. Using this feature, if there is a high-luminance portion in the obstacle search area 25, it is determined that there is an obstacle. In step 401, the image data in the obstacle search area 25 is extracted from the digital image 23 stored in the image memory 13. In step 402, for example, the image data is binarized with an appropriate threshold value, and a high-luminance portion (a portion having a higher luminance than the background)
To extract. In this case, for example, the high-luminance portion is bit "1" and the other portions are "0".

In step 403, the above step 402 is performed.
It is determined whether the high-intensity part is extracted in the binary image generated by. As an example of the determination method, if there is a bit "1" portion, it is determined that there is an obstacle, and the process proceeds to step 404Y. If there is no obstacle, step 404
Proceed to N. This method determines the presence or absence of an obstacle based on the presence or absence of a high-intensity part in the obstacle search area 25, and the binarization method in step 402 is any method that can extract the high-intensity part. Can be a method,
Further, there is no reason that the method of determining the presence / absence of the high-luminance portion in step 403 is not necessarily the above method.

The obstacle presence / absence determination result 404Y or 404N obtained by the above processing is output to step 108 of FIG. 4, and if there is an obstacle, the process proceeds to step 109 to measure the distance to the obstacle. Here, a method of performing distance measurement using a laser beam will be described. There is no reason that the laser beam must be used as the distance measuring means, and any means can be used as long as the distance to the obstacle can be measured.

An example of the laser beam direction setting process of step 109 in FIG. 4 is shown below. FIG. 14 is an image in which an obstacle exists. The screen size is 2M × 2N (pixels), and the camera view angle is 2θh (°) in the horizontal direction and 2θv (°) in the vertical direction. The coordinates 41 of the obstacle on the screen are (m0, n0), and the screen center 40 is (M, N). At this time, the direction angle of the obstacle viewed from the optical axis of the camera (the horizontal direction is Θh and the vertical direction is Θv) is given by the following equation. However, in the horizontal direction, the right side from the center is positive,
In the vertical direction, the upper side of the center is positive.

Θh = (m0-M) θh / M Θv =-(n0-N) θv / N FIG. 15 is a front view of the vehicle. Infrared camera 1
1. The laser distance measuring sensor 19 is installed coaxially and on its optical axis, and its optical axis is in the same direction.
Therefore, the azimuth angles Θh ′ and Θv ′ of the obstacle viewed from the laser distance measuring sensor 19 are obtained by correcting the difference Δh42 in the mounting position height between the laser distance measuring sensor 19 and the infrared camera 11. This calculation is given by a mathematical coordinate transformation. On the other hand, the vehicle is moving while capturing the image from the infrared camera 11 and performing the above processing. Therefore, the obstruction direction angle at the time of performing the laser distance measurement is different from the obstruction direction angle at the time of capturing the image. In order to avoid the above situation, as shown in FIG.
Is mounted near the infrared camera 11. The gyro sensor 18 determines the yawing angle α of the vehicle at the camera mounting position.
And the pitching angle β. The yawing angle and the pitching angle are β0 and β0, respectively. The yawing angle just before the laser distance measurement is α1, and the pitching angle is β1. Obstacle direction angles Θh ′ and Θv ′ viewed from the laser distance measuring sensor 19 are finally given by the following equations.

[Theta] h '= [Theta] h' + ([alpha] 1- [alpha] 0) [Theta] v '= [Theta] v' + ([beta] 1- [beta] 0) Next, an example of the laser distance measurement in step 110 in FIG. 4 will be described below. The above-mentioned Θh ′ and Θv ′ are passed from the laser beam direction setting means 14d of FIG. 1 to the laser beam direction control drive means 19a to perform laser distance measurement.

FIG. 16 shows a laser beam direction control driving means 1
An example of the principle of 9a is shown. This is called a wedge prism rotation method. Wedge-shaped prism 4
The laser beam is swung in an arbitrary direction by rotating 4, 45. FIG. 17 is a view of the prisms 44 and 45 as seen from the lateral direction. Assuming that the refraction angle 48 of the laser beam at the wedge is θ as shown in the figure, the scannable range 46 of the beam is a conical range of 4θ as shown in FIG. One of the two prisms 44 and 45 can change the position in the circumferential direction, and the other prism can change the position in the radial direction. That is, the prism 4
The laser beam can be swung in a predetermined direction by controlling the rotation angles of 4, 45.

FIG. 18 shows a laser beam direction control driving means 1
An example of 9a is shown. Direction angle of laser beam Θh ′, Θ
v'is passed from the laser beam direction setting means 14d to the servo driver 50 in the laser beam direction control driving means 19a. The servo driver 50 rotates the motor 51a by an amount corresponding to the angle Θh 'to drive the wedge prism 4
Rotate 4. An encoder 52a is attached to the motor 51a, and its rotation angle is fed back to the servo driver 50 to be controlled. Similarly, by rotating the motor 51b by an amount corresponding to Θv ', the wedge prism 4
Rotate 5. An encoder 52b is attached to the motor 51b, and its rotation angle is fed back to the servo driver 50 to be controlled. On the other hand, the laser beam 47 output from the laser distance measuring means 19b goes toward the obstacle by passing through the wedge prisms 44 and 45. Thereby, the distance to the obstacle is measured by, for example, the pulse time difference counting method, and the value is passed to the arithmetic unit 14. As a result, in step 111 of FIG.
Thus, the driver is notified of the existence of the obstacle and the distance to the obstacle via the output device 17.

In the above embodiment, the case where a vehicle stopped at a railroad crossing is detected as an obstacle is shown. However, when the distance from the preceding train is short, the same as in the above embodiment. Then, the preceding train can be dealt with as an obstacle. That is, the presence or absence of a preceding train is determined by the same processing as in the above-mentioned example, and when there is a preceding train, the distance to the preceding train is measured, the speed limit of the vehicle is calculated by the calculation processing according to the distance, and the aptitude Limit vehicle speed to speed. This allows the distance from the preceding train to be kept at a safe distance.

(Second Embodiment) Next, a second embodiment of the present invention will be described. The second embodiment shows another example in which the preceding train is detected as an obstacle.

FIG. 19 is a block diagram showing the structure of an obstacle detection device for a railway vehicle according to the second embodiment of the present invention. In the figure, 11a is a CCD camera for photographing the front of the vehicle, and the video signal photographed by this CCD camera 11a is converted into digital data by the A / D converter 12, and based on a command from the arithmetic unit 14. Image memory 13
Written in.

In the arithmetic unit 14, the vehicle speed signal from the vehicle speed sensor 1 for detecting the speed of the vehicle, the kilometer distance signal from the kilometer distance sensor 15 for detecting the current position of the vehicle, and the route data memory 16 are stored in advance. The route data is being supplied.

The arithmetic unit 14 has a binarizing means 14A,
The preceding train search area setting means 14B, the preceding train presence / absence determining means 14C, and the distance measuring means 14D to the preceding train. The binarizing means 14A, after obtaining the image data from the image memory 13, binarizes it by various image processing algorithms and records the binary image in the image memory 13. The preceding train search area setting means 14B uses the vehicle speed signal from the vehicle speed sensor 1, the kilometer signal from the kilometer sensor 15, the route data from the route data memory 16 and the binary image from the image memory 13 to determine whether or not there is a preceding train. Set the area (preceding train search area) to be inspected on the screen.
The preceding train presence / absence determining unit 14C determines the presence / absence of a preceding train by performing various image processing in the above-mentioned area (leading train search area). Distance measuring means 14D to the preceding train
Calculates the distance to the preceding train based on the position of the preceding train on the screen, the kilometer signal from the kilometer sensor 15 and the route data from the route data memory 16 and outputs it to the speed limit calculation control device 2.

Then, the speed of the vehicle is restricted based on the speed information obtained by the speed limit calculation control device 2.
Next, the operation of the above embodiment will be described.

FIG. 20 shows a flow of operations in the arithmetic unit 14. First, in step 101, a kilometer corresponding to the current position of the vehicle is set. In the next step 102, the output of the CCD camera 11a is converted into the A / D converter 1
It is digitized by 2 and stored in the image memory 13. In step 103, the image data stored in the image memory 13 is binarized by various known image processing algorithms to extract the rail portion as a binary image, and the binary image is recorded in the image memory 13.

In step 104, the kilometer corresponding to the current position of the vehicle is read from the kilometer sensor 15. In step 105, the route data corresponding to this kilometer is read from the route data memory 16. Then in step 120,
The vehicle speed signal detected by the vehicle speed sensor 1 is read. In step 121, the preceding train search area is set in the screen using the binary image and the route data as in the case of the first embodiment. In step 122, the presence / absence of a preceding train is determined by performing various kinds of image processing described later in the preceding train search area.

Then, in the next step 123, it is judged whether or not the preceding train is detected in the above step 122. If the preceding train is detected, the process proceeds to step 124, and the distance measuring means 14D measures the distance to the preceding train. Calculated by calculation. That is, the distance measuring unit 14D reads the route data from the route data memory 16 according to the distance detected by the distance sensor 15, and measures the distance to the preceding train by image processing using the route data. The speed limit of the vehicle is calculated by the calculation process according to the distance to the preceding train obtained by the measurement process, and the vehicle speed is limited to an appropriate speed (step 125). This allows the distance from the preceding train to be kept at a safe distance.

When the process of step 125 is completed, or when it is determined in step 123 that there is no preceding train, it is determined in step 112 whether the operation is completed. If it is completed, this process is completed and the operation is completed. If not, the process returns to step 102 and the above operation is repeated.

FIG. 21 shows an example of the preceding train presence / absence determination processing in step 122 of FIG. 20. First, in step 501, rail tracking processing is performed. An example of this rail tracking method is shown in FIG. Since the position of the rail is known in the preceding train search area setting process shown in step 121 of FIG. 20, the original image is horizontally scanned as shown by arrows a1, a2, ... In FIG. At this time, if there is no preceding train, there is a brightness difference between the position of the rail 34 and the rail.

However, when there is the preceding train 60, there is no difference in brightness between the position of the rail 34 and the rail, so that the rail is blocked. Therefore, in step 502 of FIG. 21, the presence or absence of the preceding train can be determined by checking the position of the rail 34 and the brightness difference between the rails. Step 50 if there is no preceding train
In step 3, it is determined whether or not the track position of the rail has reached the search area. If not, the process returns to step 501 to further track the rail. If the search area is reached in step 503, it is determined in step 504N that there is no preceding train. When the rail is blocked in step 502, it is determined in step 504Y that there is a preceding train.

If it is determined in step 504Y that there is a preceding train, the process proceeds to the distance calculation to the preceding train shown in step 124 of FIG. Since it is possible to calculate how many meters ahead the rail is closed from the route data stored in the route data memory 16, the distance to the preceding train can be calculated. The speed of the preceding train can also be calculated by repeating this operation. Then, in step 125, a speed limit is calculated from the distance to the preceding train and the speed of the preceding train to control the speed of the vehicle. According to the second embodiment, it is possible to calculate where the rail ahead by an arbitrary distance is seen on the screen, and it is possible to calculate the braking distance of the vehicle by calculating the speed of the preceding train. The area to be inspected can be accurately determined in a short time, the presence or absence of a preceding vehicle in the area beyond the braking distance can be reliably determined, and the vehicle speed can be controlled by calculating the speed limit.

[0058]

As described above in detail, according to the obstacle detecting device for a railroad vehicle of the present invention, by using the route data, the rail ahead of an arbitrary distance is displayed on the screen regardless of the track laying condition in front of the vehicle. Where it looks inside can be calculated, and the braking distance can be calculated from the speed of the vehicle detected by the vehicle speed sensor. Therefore, it is possible to accurately determine the obstacle search area in which the inspection for the presence or absence of the obstacle should be performed in a short time. Therefore, it is possible to accurately determine the presence / absence of an obstacle in a region beyond the braking distance and notify the driver, and high safety is guaranteed. Moreover, since the distance to the obstacle is measured and notified to the driver, the driver can deal with the obstacle according to the distance. Further, it is not necessary to install two cameras, and the problems of system complexity, extension of required calculation time, high cost, etc. do not occur.

Further, according to the present invention, it is possible to calculate where the rail ahead by an arbitrary distance is seen on the screen and also to calculate the braking distance of the vehicle by calculating the speed of the preceding train. The area to be inspected for the presence or absence of a train can be accurately determined in a short time, and safety is guaranteed by always inspecting the area beyond the braking distance. That is, it is possible to reliably prevent the situation in which the distance to the preceding train is shorter than the braking distance when the preceding train is detected, and moreover, the distance to the preceding train is known. The speed can be controlled.

[Brief description of drawings]

FIG. 1 is a configuration diagram of a railway vehicle obstacle detection device according to a first embodiment of the present invention.

FIG. 2 is an explanatory diagram of a vehicle equipped with the obstacle detection device.

FIG. 3 is a diagram showing an example of route data.

FIG. 4 is a flowchart illustrating a processing flow of the obstacle detection device.

5 is a flowchart illustrating an operation of obstacle search area setting processing in step 106 in FIG.

6 is an explanatory diagram of the operation in FIG.

7 is a diagram illustrating a method of setting an obstacle search area in FIG.

FIG. 8 is a flowchart illustrating an example of processing for eliminating the influence of vibration in setting an obstacle search area.

FIG. 9 is a diagram illustrating a method of setting a window on a rail image when obtaining a correlation value.

FIG. 10 is a diagram showing a binary image in which a rail portion is extracted.

FIG. 11 is a diagram illustrating deviation amounts ρm and ρn.

12 is a diagram illustrating a method of setting an obstacle search area in FIG.

FIG. 13 is a flowchart illustrating an obstacle presence / absence determination process in step 107 in FIG.

FIG. 14 is a diagram showing an example of an image when an obstacle is present.

FIG. 15 is a diagram showing an arrangement example of devices when the vehicle is viewed from the front.

16 is a diagram for explaining an example of the principle of the laser beam direction control driving means in FIG.

FIG. 17 is a diagram of the wedge prism in FIG. 16 viewed from the lateral direction.

FIG. 18 is a diagram showing a configuration example of a laser beam direction control driving means in FIG.

FIG. 19 is a configuration diagram of an obstacle detection device for a railway vehicle according to a second embodiment of the present invention.

FIG. 20 is a flowchart illustrating a preceding train detection and control operation in the embodiment.

21 is a flowchart illustrating the preceding train presence / absence determination process of step 122 in FIG.

22 is a flowchart illustrating the rail tracking method of step 501 in FIG. 21. FIG.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 1 ... Vehicle speed sensor, 2 ... Speed limit calculation control device, 10 ... Vehicle, 11 ... Infrared camera, 11a ... CCD camera, 12
... A / D converter, 13 ... Image memory, 14 ... Arithmetic device,
14a ... Binarization means, 14b ... Obstacle search area setting means, 14c ... Obstacle presence / absence determination means, 14d ... Laser beam direction setting means, 14A ... Binarization means, 14B ... Preceding train search area setting means, 14C ... Leading train presence / absence determining means, 14D ... Distance measuring means to leading train, 15 ... Kilometer sensor, 16 ... Route data memory, 17 ... Output device,
18 ... Gyro sensor, 19 ... Laser distance measuring sensor, 19
a ... Laser beam direction control driving means, 19b ... Laser distance measuring means, 20 ... Three-dimensional coordinates, 21 ... (L + ΔL) m Front rail center point p, 22 ... Rail, 23 ... Digital image stored in image memory , 24 ... Position where point p is visible in digital image, 25 ... Obstacle search area, 30
... rail image, 31 ... binary image, 32 ... window for obtaining correlation value, 33 ... rail drawn using route data, 34 ... actual rail, 34a ... binarized actual rail, 35m ... deviation amount ρm, 35n ... Deviation amount ρn, 3
6 ... Point corrected by deviation amount ρm, ρn, 40 ... Center of screen (m, n), 41 ... Coordinates of obstacle, 42 ... Difference Δh between mounting positions of laser range finder and infrared camera, 43 ...
Laser light source, 44, 45 ... Wedge prism, 46 ... Laser beam scanning range, 47 ... Laser beam, 4
8 ... Angle θ of refraction of light at wedge, 50 ... Servo driver, 51a, 51b ... Motor, 52a, 52b ... Encoder, 60 ... Leading train.

 ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Hiroshi Yamashita, Hiroshi Yamashita, 4-6-22 Kannon Shinmachi, Nishi-ku, Hiroshima City, Hiroshima Prefecture Mitsubishi Heavy Industries, Ltd. Hiroshima Research Institute (72) Kuniaki Wakusawa, 4 Kannon Shinmachi, Nishi-ku, Hiroshima City, Hiroshima Prefecture 6-22 No. 6 Hiroshima Research Laboratory, Mitsubishi Heavy Industries, Ltd.

Claims (2)

[Claims] 1. An image input device installed so as to capture an image of the front of a traveling railway vehicle, an image memory for storing images captured by the image input device, and a current position of the traveling vehicle is detected. Position detection means, speed detection means for detecting the speed of the traveling vehicle, and how the rail looks in the image taken by the image input device corresponds to the vehicle position detected by the position detection means. The route data memory previously stored as the route data and the speed avoidance distance detected by the speed detecting means are used to determine the distance at which collision with the obstacle can be avoided, and the position detection signal detected by the position detecting means and the above An obstacle search area setting means for setting an obstacle search area in the screen obtained by the image input device from the route data stored in the route data memory; With respect to the image stored in the image memory, determination means for determining whether or not there is an obstacle in the obstacle search area, vibration angle detection means for detecting the vibration of the vehicle, and from the vehicle to the obstacle Distance measuring means for measuring the distance, and calculating means for calculating the direction of the obstacle from the position of the obstacle on the screen obtained by the image input device and the vibration angle obtained by the vibration angle detecting means, Distance measuring direction control means for controlling and driving the distance measuring direction of the distance measuring means based on the direction information obtained by the calculating means, presence / absence of obstacles judged by the judging means, and obstacles measured by the distance measuring means. An obstacle detection device for a railroad vehicle, comprising: an informing means for informing a driver of the distance to the vehicle. 2. An image input device installed so as to capture an image of the front of a traveling railway vehicle, an image memory for storing an image captured by the image input device, and a current position of the traveling vehicle is detected. Position detection means, speed detection means for detecting the speed of the traveling vehicle, and how the rail looks in the image taken by the image input device corresponds to the vehicle position detected by the position detection means. The route data memory previously stored as the route data and the speed signal detected by the speed detecting means determine the distance at which a collision with the preceding train can be avoided, and the position detection signal detected by the position detecting means and the above Leading train search area setting means for setting a leading train search area in the screen obtained by the image input device from the route data stored in the route data memory , Determination means for determining whether or not there is a preceding train in the preceding train search area for the image stored in the image memory, distance measuring means for measuring a distance from the vehicle to the preceding train, and this distance measurement An obstacle detection device for a railway vehicle, comprising: a means for calculating a speed limit based on the distance to the preceding train measured by the means, and limiting the vehicle speed.