JP7089954B2 - Obstacle detector for construction vehicles - Google Patents

Description

The present invention relates to an obstacle detection device for a construction vehicle.

In the rolling roller, for example, when rolling close to the curb, the driver operates while paying close attention to the rolling surface around the curb, so that attention to the traveling direction tends to be neglected. Therefore, an accident that comes into contact with surrounding workers is likely to occur, especially when the vehicle is moving backward.

To solve this problem, an alarm device that issues an alarm when a person or an object is detected at a certain distance using radio waves or ultrasonic waves or an automatic stop device that automatically stops the vehicle is known (for example, a patent). Refer to Documents 1 to 3). Patent Document 1 describes a magnetic field generator mounted on a vehicle, an IC tag attached to an operator, a detection device for detecting radio waves transmitted from the IC tag, and a vehicle when the detection device detects radio waves. An emergency stop device including an engine stop device for stopping the engine is described. Patent Document 2 describes a trigger signal output means mounted on a vehicle, an ID tag attached to an operator, a receiving unit for receiving an ID number output by the ID tag, and when the receiving unit receives an ID number. Describes a stop system comprising a stop means for stopping the vehicle. Patent Document 3 describes an ultrasonic type obstacle detection device.

Japanese Unexamined Patent Publication No. 2016-153558 Japanese Unexamined Patent Publication No. 2017-10483 Japanese Unexamined Patent Publication No. 2006-17496

The techniques of Patent Documents 1 and 2 have a problem that the cost tends to increase when there are a large number of workers because the tag needs to be attached to the worker, and the worker may forget to attach the tag. It can happen.

In addition, when detecting obstacles with ultrasonic waves, unevenness of the ground, slopes, etc. may be recognized as obstacles. The problem of stopping arises.

In addition, if the construction site is foggy or rainy, or if water vapor or dust is floating, these may also be recognized as obstacles, and even though there is no risk of obstacles, alarms will sound frequently and vehicles will sound. Occurs the problem of stopping.

The present invention has been created to solve such a problem, and an object of the present invention is to provide an obstacle detection device for a construction vehicle having excellent obstacle detection accuracy.

In order to solve the above problems, the present invention is an obstacle detection device mounted on a construction vehicle, which is a TOF type distance image sensor that measures a distance from a time difference between projected light and reflected light, and the distance image sensor. It is equipped with a control device that determines the presence or absence of obstacles based on the measurement data of the above, and regarding the measurement data, the vehicle traveling direction component from the vehicle end is x data, the vehicle width direction component is y data, and the height from the ground. When the vertical component is z data, the control device performs a height selection step of selecting only measurement data whose z data is larger than the threshold value T1 to determine the presence or absence of an obstacle, and the height selection step . The first determination step of calculating the inclination of the distribution from the x data and the z data of the measurement data selected in step 1 and determining the presence or absence of an obstacle based on this inclination, and the control device selecting in the height selection step. The determination coefficient ^R2 is calculated by the following equation (2) based on the x data and z data of the measured data, and when it is determined in the first determination step that there is no obstacle, the determination coefficient is the threshold T3. It is characterized by having a second determination step in which it is determined that there is an obstacle when the data is smaller than the above, and no obstacle is determined when the determination coefficient is larger than the threshold value T3 .
According to the present invention, the accuracy of detecting an obstacle can be improved by using the TOF type distance image sensor. There is no need for workers to wear tags. Then, by performing the height selection step, the unevenness of the ground can be ignored and unnecessary obstacle detection can be reduced.
Further, according to the present invention, it is possible to reduce the possibility of determining an uphill as an obstacle.
Further, according to the present invention, the accuracy of detecting obstacles is further improved.

Further, the present invention is an obstacle detection device mounted on a construction vehicle, and is based on a TOF type distance image sensor that measures a distance from a time difference between a projected light and a reflected light, and measurement data of the distance image sensor. With respect to the measurement data, the vehicle traveling direction component from the vehicle end is x data, the vehicle width direction component is y data, and the height direction component from the ground is z. When used as data, the control device performs a height selection step of selecting only measurement data whose z data is larger than the threshold value T1 to determine the presence or absence of an obstacle, and the measurement data selected in the height selection step. The inclination of the distribution is calculated from the x data and the z data, and the first determination step of determining the presence or absence of an obstacle is performed based on this inclination. In the first determination step, the horizontal axis is the z. The data, the vertical axis is the inclination a calculated by the minimum square method in the scatter diagram of the x data, and the failure occurs when the inclination 1 / a obtained from the inclination a is larger than the threshold T4 or smaller than 0. It is characterized by determining that there is an object.
According to the present invention, the accuracy of detecting an obstacle can be improved by using the TOF type distance image sensor. There is no need for workers to wear tags. Then, by performing the height selection step, the unevenness of the ground can be ignored and unnecessary obstacle detection can be reduced.
Further, according to the present invention, the possibility of determining an uphill as an obstacle can be further reduced.

Further, the present invention is an obstacle detection device mounted on a construction vehicle, and is based on a TOF type distance image sensor that measures a distance from a time difference between a projected light and a reflected light, and measurement data of the distance image sensor. With respect to the measurement data, the vehicle traveling direction component from the vehicle end is x data, the vehicle width direction component is y data, and the height direction component from the ground is z. When used as data, the control device performs a height selection step of selecting only measurement data whose z data is larger than the threshold T1 to determine the presence or absence of an obstacle, and the measurement data selected in the height selection step. The inclination of the distribution is calculated from the x data and the z data, and the first determination step of determining the presence or absence of an obstacle is performed based on this inclination. In the first determination step, the horizontal axis is the z. The data, the vertical axis is the inclination a calculated by the minimum square method in the scatter diagram of the x data, and it is characterized in that it is determined that there is an obstacle when the inclination a is smaller than the threshold value T5.
According to the present invention, the accuracy of detecting an obstacle can be improved by using the TOF type distance image sensor. There is no need for workers to wear tags. Then, by performing the height selection step, the unevenness of the ground can be ignored and unnecessary obstacle detection can be reduced.
Further, according to the present invention, the possibility of determining an uphill as an obstacle can be further reduced.

Further, the control device has a coefficient of determination R according to the following equation (2) based on the x data and z data of the measurement data selected in the height selection step. ² When it is determined that there is no obstacle in the first determination step, it is determined that there is an obstacle when the coefficient of determination is smaller than the threshold value T3, and an obstacle is determined when the coefficient of determination is larger than the threshold value T3. It is preferable to have a second determination step that does not determine an object.
According to the present invention, the accuracy of detecting obstacles is further improved.

Further, the present invention is an obstacle detection device mounted on a construction vehicle, and is a TOF type distance image sensor that measures a distance from a time difference between a projected light and a reflected light, and measurement data of the distance image sensor. A control device for determining the presence or absence of an obstacle based on the reflection intensity of the reflected light is provided, and the reflection intensity of the reflected light is measured by the TOF type distance image sensor, and the control device is the reflection. The slope of the distribution is selected from the x data and z data of the measurement data selected in the reflection intensity selection step of selecting only the measurement data whose light reflection intensity is larger than the threshold T6 to determine the presence or absence of obstacles and the reflection intensity selection step. The following formula is used based on the first determination step of determining the presence or absence of an obstacle based on this inclination and the x data and z data of the measurement data selected by the control device in the reflection intensity selection step. Decision coefficient R in (2) ² When it is determined that there is no obstacle in the first determination step, it is determined that there is an obstacle when the coefficient of determination is smaller than the threshold value T3, and an obstacle is determined when the coefficient of determination is larger than the threshold value T3. It is characterized by having a second determination step of not determining an object.
According to the present invention, the accuracy of detecting an obstacle can be improved by using the TOF type distance image sensor. There is no need for workers to wear tags. Further, the reflection intensity of the reflected light from water vapor or dust is lower than the reflection intensity of the reflected light from a solid substance. Therefore, by providing a control device that determines the presence or absence of an obstacle based on the measurement data of the distance image sensor and the reflection intensity of the reflected light, fine particles such as water vapor and dust floating in the air are erroneously determined as an obstacle. Can be reduced. Then, the unevenness of the ground can be ignored, and unnecessary obstacle detection can be reduced.
Further, according to the present invention, it is possible to reduce the possibility of determining an uphill as an obstacle.
Further, according to the present invention, the accuracy of detecting obstacles is further improved.

Further, it is preferable that the slope is the slope m calculated by the least squares method in the scatter plot of the x-data on the horizontal axis and the z-data on the vertical axis.
Further, it is preferable to determine that there is an obstacle when the slope m is larger than the threshold value T2, smaller than 0, or divided by zero.
Further, according to the present invention, the possibility of determining an uphill as an obstacle can be further reduced.

Further, the present invention is an obstacle detection device mounted on a construction vehicle, and is a TOF type distance image sensor that measures a distance from a time difference between a projected light and a reflected light, and measurement data of the distance image sensor. A control device for determining the presence or absence of an obstacle based on the reflection intensity of the reflected light is provided, and the reflection intensity of the reflected light is measured by the TOF type distance image sensor, and the control device is the reflection. The slope of the distribution is selected from the x data and z data of the measurement data selected in the reflection intensity selection step of selecting only the measurement data whose light reflection intensity is larger than the threshold T6 to determine the presence or absence of obstacles and the reflection intensity selection step. Is calculated, and a first determination step of determining the presence or absence of an obstacle is performed based on this inclination. In the first determination step, the horizontal axis is the z data and the vertical axis is the x data. It is a slope a calculated by the minimum square method in the scatter diagram, and is characterized in that it is determined that there is an obstacle when the slope 1 / a obtained from the slope a is larger than the threshold T4 or smaller than 0. And.
According to the present invention, the accuracy of detecting an obstacle can be improved by using the TOF type distance image sensor. There is no need for workers to wear tags. Further, the reflection intensity of the reflected light from water vapor or dust is lower than the reflection intensity of the reflected light from a solid substance. Therefore, by providing a control device that determines the presence or absence of an obstacle based on the measurement data of the distance image sensor and the reflection intensity of the reflected light, fine particles such as water vapor and dust floating in the air are erroneously determined as an obstacle. Can be reduced. Then, the unevenness of the ground can be ignored, and unnecessary obstacle detection can be reduced.
Further, according to the present invention, it is possible to reduce the possibility of determining an uphill as an obstacle.

Further, the present invention is an obstacle detection device mounted on a construction vehicle, and is a TOF type distance image sensor that measures a distance from a time difference between a projected light and a reflected light, and measurement data of the distance image sensor. A control device for determining the presence or absence of an obstacle based on the reflection intensity of the reflected light is provided, and the reflection intensity of the reflected light is measured by the TOF type distance image sensor, and the control device is the reflection. The slope of the distribution is selected from the x data and z data of the measurement data selected in the reflection intensity selection step of selecting only the measurement data whose light reflection intensity is larger than the threshold T6 to determine the presence or absence of obstacles and the reflection intensity selection step. Is calculated, and a first determination step of determining the presence or absence of an obstacle is performed based on this inclination. In the first determination step, the horizontal axis is the z data and the vertical axis is the x data. It is a slope a calculated by the minimum square method in the scatter diagram, and is characterized in that it is determined that there is an obstacle when the slope a is smaller than the threshold value T5.
According to the present invention, the accuracy of detecting an obstacle can be improved by using the TOF type distance image sensor. There is no need for workers to wear tags. Further, the reflection intensity of the reflected light from water vapor or dust is lower than the reflection intensity of the reflected light from a solid substance. Therefore, by providing a control device that determines the presence or absence of an obstacle based on the measurement data of the distance image sensor and the reflection intensity of the reflected light, fine particles such as water vapor and dust floating in the air are erroneously determined as an obstacle. Can be reduced. Then, the unevenness of the ground can be ignored, and unnecessary obstacle detection can be reduced.
Further, according to the present invention, it is possible to reduce the possibility of determining an uphill as an obstacle.

Further, in the present invention, the control device has a coefficient of determination R according to the following equation (2) based on the x data and z data of the measurement data selected in the reflection intensity selection step. ² When it is determined that there is no obstacle in the first determination step, it is determined that there is an obstacle when the coefficient of determination is smaller than the threshold value T3, and an obstacle is determined when the coefficient of determination is larger than the threshold value T3. It is preferable to have a second determination step that does not determine an object.
According to the present invention, the accuracy of detecting obstacles is further improved.

Further, the threshold value T1 is preferably in the range of 5 to 30 cm.
Further, the threshold value T2 is preferably in the range of 0.6 to 0.7.
Further, the threshold value T4 is preferably in the range of 0.6 to 0.7.
Further, the threshold value T3 is preferably in the range of 0.6 to 1.0.
Further, the threshold value T5 is preferably in the range of 1.4 to 1.7.

According to the present invention, it is an obstacle detection device having excellent obstacle detection accuracy.

It is explanatory drawing which shows the detection range of the obstacle detection device attached to the tire roller, and (a) and (b) are plan view and side view, respectively. (A) is a side view of the detection range when the detection target is a worker, and (b) is a graph of the calculated x data and z data. (A) is a side view of the detection range when the detection target is a box with a height, and (b) is a graph of the calculated x data and z data. (A) is a side view of the detection range when the detection target is a low-height box, and (b) is a graph of the calculated x data and z data. (A) is a side view of the detection range when the detection target is a slope with a relatively slope, and (b) is a graph of the calculated x data and z data. (A) is a side view of the detection range when the detection target is a slope with a relatively gentle slope, and (b) is a graph of the calculated x data and z data. It is a flow figure which shows an example of the determination flow of the obstacle which concerns on this invention. It is a graph which shows the brake start distance. The scatter diagram of FIG. 2B is a scatter diagram in which the horizontal axis is z data and the vertical axis is x data. The scatter diagram of FIG. 3B is a scatter diagram in which the horizontal axis is z data and the vertical axis is x data. The scatter diagram of FIG. 4B is a scatter diagram in which the horizontal axis is z data and the vertical axis is x data. The scatter diagram of FIG. 5B is a scatter diagram in which the horizontal axis is z data and the vertical axis is x data. The scatter diagram of FIG. 6B is a scatter diagram in which the horizontal axis is z data and the vertical axis is x data. It is a flow chart which shows an example of the determination flow of the obstacle which concerns on 2nd Embodiment. It is a flow chart which shows an example of the determination flow of the obstacle which concerns on 3rd Embodiment. (A) is a side view showing a detection range when there is no obstacle, and (b) is a graph regarding xz and a graph regarding x-reflection intensity. (A) is a side view showing a detection range when an obstacle is present, and (b) is a graph regarding xz and a graph regarding x-reflection intensity. (A) is a side view showing a detection range in the presence of fine particles, and (b) is a graph relating to xz and a graph relating to x-reflection intensity. It is a perspective view which shows the experimental example of the obstacle detection apparatus. It is a graph about the x-reflection intensity measured in the experimental example of FIG. 5, (a) is the data before the correction of the reflection intensity, and (b) is about the data after the correction of the reflection intensity. It is a graph about xz measured in the experimental example of FIG. 5, (a) is the data before the correction of the reflection intensity, and (b) is about the data after the correction of the reflection intensity. It is a graph about yz measured in the experimental example of FIG. 5, (a) is the data before the correction of the reflection intensity, and (b) is about the data after the correction of the reflection intensity. It is a graph about yx measured in the experimental example of FIG. 5, (a) is the data before the correction of the reflection intensity, and (b) is about the data after the correction of the reflection intensity. It is a flow chart which shows an example of the determination flow of the obstacle which concerns on 4th Embodiment.

In FIG. 1, the obstacle detection device 1 of the present invention is mounted on a construction vehicle such as a rolling roller that performs work while traveling at a low speed. FIG. 1 shows a case where an obstacle detection device 1 is mounted on a tire roller 10 that rolls an asphalt road surface or the like with a tire 11. The obstacle detection device 1 is an obstacle based on the TOF (Time Of Flight) type distance image sensor (3D distance sensor) 2 that measures the distance from the time difference between the projected light and the reflected light, and the measurement data of the distance image sensor 2. A control device 3 for determining the presence or absence of an object G is provided.

"Distance image sensor 2"
The distance image sensor 2 includes a light emitting unit that emits projected light such as infrared rays, and a light receiving unit that receives reflected light when the projected light hits an object. The distance to the target is measured by measuring the time from the transmission of infrared rays from the light emitting unit to the reception of the reflected light by the light receiving unit. The projection angle from the distance image sensor 2 is, for example, a horizontal angle of 95 ° and a vertical angle (reference numeral θ1 shown in FIG. 1B) of 32 °, and the projection cross section has a horizontally long rectangular shape. The image resolution is, for example, 64 pixels in the horizontal direction and 16 pixels in the vertical direction, for a total of 1024 pixels. The distance image sensor 2 is attached to the rear portion of the tire roller 10 at the center in the vehicle width direction so that the projected light is projected diagonally downward in the reverse direction.

Regarding the detection range of the obstacle G, if the projection range of the projected light is set to the detection range as it is, that is, if the dimension W in the vehicle width direction is set wider than the vehicle width dimension of the tire roller 10, there is no risk of collision. It is recognized that there is an obstacle G, and the vehicle stops unnecessarily. Therefore, it is preferable that the dimension W of the detection range (indicated by diagonal lines in FIG. 1) 4 in the vehicle width direction is set to be substantially the same as the vehicle width dimension of the tire roller 10. Since the distance image sensor 2 can measure the distance to the obstacle G, the obstacle G exists in the detection range 4 set in the vehicle width dimension from the measurement data for each pixel, specifically from the y data described later. The control device 3 can determine whether or not to do so. By using the distance image sensor 2 in this way, the dimension W of the detection range 4 can be secured to be constant in the front-rear direction of the vehicle. That is, the detection range 4 can be easily set to a substantially rectangular range having one side as the dimension W in a plan view as shown in FIG. 1 (a). The dimension L2 in the vehicle front-rear direction of the detection range 4 is appropriately set according to the commonly used traveling speed, and is set to, for example, about 3 meters in the present embodiment.

Further, since the projected light of the distance image sensor 2 is projected diagonally downward in the reverse direction, the lateral angle θ2 of the projected light when viewed in a plan view is in a range much larger than 95 °. Therefore, the distance L3 in the vehicle front-rear direction can be kept small with respect to the non-detection ranges 5 and 5 formed between the rear end ends of the tire roller 10 and the detection range 4. That is, the undetected blind spots formed on both sides of the rear part of the vehicle can be reduced.

"Control device 3"
In FIG. 1, the control device 3 is mounted, for example, in the vicinity of the distance image sensor 2 or around the driver's seat. From the measurement data of each pixel measured by the distance image sensor 2, the control device 3 has x data for the backward component from the rear end of the vehicle, y data for the vehicle width direction component from the center of the vehicle width, and y data from the ground. The z data is calculated for the height component of. Then, the control device 3 performs the following three steps in determining the obstacle G.

"Height sorting step"
The control device 3 performs a height selection step of selecting only the measurement data whose z data is larger than the threshold value T1 and determining the presence or absence of the obstacle G. This height selection step is mainly performed for the purpose of not recognizing a slight unevenness of the ground as an obstacle G. The threshold value T1 is set in the range of 5 to 30 cm, preferably in the range of 10 to 20 cm. If the threshold value T1 is set in the range of 5 to 30 cm, the unevenness of the ground that can be generally assumed can be ignored, and unnecessary obstacle detection can be reduced. Further, when the posture becomes low, such as when a worker crouches within the detection range 4, the height is unlikely to be 30 cm or less, and in this case, it can be considered that there is a risk of an obstacle G.

"First judgment step"
The control device 3 calculates the slope of the straight line of the distribution from the x data and the z data of the measurement data selected in the height selection step, and determines the presence or absence of the obstacle G based on this slope. In this embodiment, the slope m is calculated by the method of least squares. Then, the control device 3 determines that the obstacle G exists when the slope m is larger than the threshold value T2, smaller than 0, or divided by zero. The slope m by the method of least squares was obtained by Eq. (1).

In a construction vehicle, when working on a slope such as a slope or loading on a trailer or the like, when approaching an uphill from a flat surface, the control device 3 may mistakenly determine the surface of the slope as an obstacle G. The first determination step is performed for the purpose of not recognizing the uphill as an obstacle G. Generally, the limit climbing angle of rolling rollers and other construction vehicles is about 30 °, and the special one is about 35 °. The slope m of 30 ° by the least squares method is about 0.6, and the slope m of 35 ° is about 0.7. The value of the slope m by the method of least squares increases as the angle of the slope increases. Therefore, if the threshold value T2 is set in the range of 0.6 to 0.7, it is possible to avoid unnecessary obstacle detection for a generally conceivable uphill slope. Since the inclination m of a person or an object standing upright in the vertical direction is generally larger than 0.6 to 0.7, these can be recognized as obstacles G. The threshold value T2 can be changed as appropriate.

Regarding the inclination m of the person standing up in the vertical direction, for example, when the upper body side is close to the vehicle or the work equipment or the like held in the hand is close to the vehicle, the inclination m is negative, that is, smaller than 0. As for the object, when the upper part is closer to the vehicle than the lower part, the inclination m is smaller than 0. Therefore, even when the inclination m is smaller than 0, it is recognized that there is an obstacle G. When the measurement result becomes completely vertical, in the equation (1), the slope m = 0/0, that is, division by zero, so that the obstacle G is recognized in this case as well.

2 to 6 show the test results conducted by the present inventor, respectively, (a) is a side view showing a detection target, and (b) is a graph of calculated x data and z data. In each case, the threshold value T1 of the height selection step described above is set to 10 cm, and the measurement points (indicated by black circles) of z data lower than 10 cm are not taken into consideration for the determination of the obstacle G.

FIG. 2 shows a case where the detection target is an upright worker. FIG. 2A shows a case where 7 pixels out of 16 pixels in the vertical direction detect a worker at measurement points p1 to p7 10 cm or more from the ground. The slope m of the measurement points p1 to p7 by the method of least squares was −7.8485 as shown in FIG. 2 (b). That is, since the inclination m is smaller than 0, the control device 3 determines that the worker is an obstacle G by the first determination step.

FIG. 3 shows a case where the detection target is a square box with a height. In this case, the slope m of the measurement points p1 to p7 by the least squares method was 6.9676 as shown in FIG. 3 (b). That is, since the inclination m is larger than the threshold value T2 in the range of 0.6 to 0.7, the control device 3 determines that the box is an obstacle G by the first determination step.

FIG. 5 shows a case where the detection target is a slope with a gradient of 18.6 °. The slope m of the measurement points p1 to p16 by all 16 pixels by the least squares method was 0.3362 as shown in FIG. 5 (b). That is, since the slope m is smaller than the threshold value T2 in the range of 0.6 to 0.7 and larger than 0, the control device 3 uses the slope with a slope of 18.6 ° as an obstacle in the first determination step. Do not judge.

FIG. 6 shows a case where the detection target is a slope with a gradient of 4.2 °. The slope m of the measurement points p1 to p16 by all 16 pixels by the least squares method was 0.0735 as shown in FIG. 6 (b). That is, since the slope m is smaller than the threshold value T2 in the range of 0.6 to 0.7 and larger than 0, the control device 3 regards the slope with a slope of 4.2 ° as an obstacle by the first determination step. Do not judge.

FIG. 4 shows a case where the detection target is a low-height rectangular box. FIG. 4A shows a case where 6 pixels detect the upper surface of the box at the measurement points p1 to p6 and 3 pixels detect the side surface of the box at the measurement points p7 to p9. The slope m of these measurement points p1 to p9 by the least squares method was 0.2706 as shown in FIG. 4 (b). That is, since the slope m is smaller than the threshold value T2 in the range of 0.6 to 0.7 and larger than 0, the control device 3 does not determine the box as an obstacle G by the first determination step. Will end up. This is because, in addition to the measurement points p7 to p9 on the side surface of the box, the measurement points p1 to p6 on the horizontal upper surface of the box are also calculated, so that the inclination m is leveled low as a whole. To solve this problem, the control device 3 performs a second determination step.

"Second judgment step"
The control device 3 calculates the coefficient of determination ^R2 from the x data and z data of the measurement data selected in the height selection step, and when it is determined in the first determination step that there is no obstacle G, the coefficient of determination ^R2 Is smaller than the threshold value T3, it is determined that there is an obstacle G. The coefficient of determination R ² is an index showing the accuracy of regression analysis, and the higher the value, the higher the correlation. The coefficient of determination R ² can be obtained by the equation (2).

Usually, slopes continue on almost the same slope, so the correlation between measurement points is high. On the slope of FIG. 5, the coefficient of determination ^R2 of the measurement points p1 to p16 was 0.9677, and on the slope of FIG. 6, the coefficient of determination ^R2 of the measurement points p1 to p16 was 0.8312. As a result of the test, the coefficient of determination ^R2 on each slope was generally in the range of 0.6 to 1.0. Therefore, if the threshold value T3 is set in the range of 0.6 to 1.0, preferably 0.7 to 1.0, the control device 3 sets the detection target of the coefficient of determination ^R2 larger than the threshold value T3 as a slope. Recognize and do not judge as obstacle G.

On the other hand, in the case of the box shown in FIG. 4, the coefficient of determination ^R2 of the measurement points p1 to p9 was 0.4159, and it was found that the correlation between the measurement points p1 to p9 was low. Therefore, when the threshold value T3 is set in the range of 0.6 to 1.0, the control device 3 determines that the box having the coefficient of determination ^R2 smaller than the threshold value T3 is the obstacle G.

FIG. 7 shows an example of the obstacle determination flow. The control device 3 calculates x data, y data, and z data from the measurement data of each pixel measured by the distance image sensor 2 (step S1), and the y data is generated with respect to the dimension W of the detection range 4. -It is determined whether or not it is in the range of W / 2 to W / 2 (step S2). In the case of Yes, the process proceeds to step S3 assuming that the obstacle G may exist within the detection range 4, and in the case of No, the process returns to step S1.

In step S3, the control device 3 determines whether or not the z data is larger than the threshold value T1 as a height selection step. If Yes, the process proceeds to Step S4, and if No, the process returns to Step S1.

The control device 3 calculates the slope m from the x data and the z data by the least squares method, calculates the determination coefficient ^R2 (step S4), and determines whether the slope m is larger than the threshold value T2 or greater than 0. It is determined whether or not it is small, or 0/0, that is, whether or not it is divided by zero (step S5). If Yes, it is determined that there is an obstacle G, and the process proceeds to step S7. If No, the process proceeds to step S6. In step S6, the control device 3 determines whether or not the coefficient of determination ^R2 is smaller than the threshold value T3. If Yes, it is determined that there is an obstacle G, and the process proceeds to Step S7. Return to S1.

In step S7, the control device 3 calculates the x data (Min) closest to the vehicle among the x data and the z data, and recognizes this as the distance from the vehicle to the obstacle G. In step S8, the control device 3 determines whether or not the x data (Min) is smaller than the predetermined threshold value T4, and in the case of Yes, the control device 3 transmits a brake signal to a braking means (not shown) to stop the vehicle. (Step S9), and if No, the process returns to step S7. In step S9, an alarm may be issued by sound or light instead of or in combination with the brake.

The timing at which the brake signal is output is preferably changed according to the traveling speed of the vehicle. In this case, as shown in FIG. 8, the control device 3 compares the brake start distance S (that is, the threshold value T4) preset according to the traveling speed with the x data (Min), and the x data (Min) is obtained. When it becomes smaller than a predetermined threshold value T4, a brake signal is output. The brake start distance S is set to a distance slightly larger than, for example, the limit braking distance T actually measured by the vehicle. In FIG. 8, the brake start distance S is set to about 0.5 m at 2 km / h, about 1 m at 4 km / h, about 1.6 m at 6 km / h, and about 2.4 m at 8 km / h. Examples of the vehicle speed sensor that detects the traveling speed of the vehicle include a proximity sensor such as a rotary encoder that detects the number of rotations of the tire.

As described above, if the control device 3 is configured to perform the height selection step of selecting only the measurement data whose z data is larger than the threshold value T1 and determining the presence or absence of the obstacle G, the unevenness of the ground can be ignored. , Useless obstacle detection can be reduced.

The control device 3 calculates the slope m of the distribution from the x data and z data of the measurement data selected in the height selection step, and performs the first determination step of determining the presence or absence of the obstacle G based on the slope m. With the configuration, it is possible to reduce the possibility of determining the uphill as an obstacle G. In particular, if the slope m is calculated by the least squares method and it is determined that there is an obstacle G when the slope m is larger than the threshold value T2, smaller than 0, or divided by zero, the obstacle G is determined. The detection accuracy of G is further improved.

The control device 3 calculates the coefficient of determination ^R2 from the x data and z data of the measurement data selected in the height selection step, and when it is determined in the first determination step that there is no obstacle G, the coefficient of determination ^R2 If the second determination step of determining that there is an obstacle G is performed when is smaller than the threshold value T3, the detection accuracy of the obstacle G is further improved.

The preferred embodiment of the present invention has been described above. In the embodiment described, the distance image sensor 2 is attached to the rear part of the vehicle, but the distance image sensor 2 may be attached to the front part of the vehicle to detect the forward direction of the vehicle.

"Second embodiment"
In the above-described embodiment, as a first determination step, as shown in FIG. 2B and the like, a scatter plot of x-data on the horizontal axis and z-data on the vertical axis is used, and the least squares method of the equation (1) is used. The slope m was calculated by On the other hand, in the second embodiment, as the first determination step, the horizontal axis is a scatter diagram of z data and the vertical axis is a scatter diagram of x data, and the slope a is calculated by the least squares method of the equation (3).

The control device 3 obtains a slope 1 / a having a slope a as a denominator. The slope 1 / a of the slope of 30 ° by the least squares method of the equation (3) is about 0.6, and the slope 1 / a of the slope of 35 ° is about 0.7. Therefore, if the threshold value T4 is set in the range of 0.6 to 0.7, it is possible to avoid unnecessary obstacle detection for a generally conceivable uphill slope. Since the inclination 1 / a with respect to a person or an object standing upright is generally larger than 0.6 to 0.7, these can be recognized as obstacles G. When the upper body side is close to the vehicle or the work equipment or the like held in the hand is close to the vehicle, the inclination 1 / a is negative, that is, smaller than 0. As for the object, when the upper part is closer to the vehicle than the lower part, the inclination 1 / a is smaller than 0. Therefore, even when the inclination 1 / a is smaller than 0, it is recognized that there is an obstacle G. That is, in the first determination step, the control device 3 determines that the obstacle G exists when the inclination 1 / a is larger than the threshold value T4 or smaller than 0.

9 to 13 are scatter plots in which the horizontal axis and the vertical axis of the scatter plots of FIGS. 2 (b) to 6 (b) are interchanged, and the horizontal axis is z data and the vertical axis is x data. There is. As described above, the measurement points (indicated by black circles) of z data lower than the threshold value T1 are not taken into consideration for the determination of the obstacle G.

FIG. 9 shows a case where the detection target is an upright worker. The slope a of the measurement points p1 to p7 by the method of least squares is −0.0286, and the slope 1 / a is −34.9. That is, since the inclination 1 / a is smaller than 0, the control device 3 determines that the worker is an obstacle G by the first determination step.

FIG. 10 shows a case where the detection target is a square box having a height. The slope a of the measurement points p1 to p7 by the method of least squares is 0.0831, and the slope 1 / a is 12.0. That is, since the slope 1 / a is larger than the threshold value T4 in the range of 0.6 to 0.7, the control device 3 determines that the box is an obstacle G by the first determination step.

FIG. 12 shows a case where the detection target is a slope with a gradient of 18.6 °. The slope a of the measurement points p1 to p16 by the least squares method is 2.8784, and the slope 1 / a is 0.34. That is, since the slope 1 / a is smaller than the threshold value T4 in the range of 0.6 to 0.7 and larger than 0, the control device 3 obstructs the slope with a slope of 18.6 ° by the first determination step. Do not judge as a thing.

FIG. 13 shows a case where the detection target is a slope with a gradient of 4.2 °. The slope a of the measurement points p1 to p16 by the least squares method is 11.303, and the slope 1 / a is 0.088. That is, since the slope 1 / a is smaller than the threshold value T4 in the range of 0.6 to 0.7 and larger than 0, the control device 3 obstructs the slope with a slope of 4.2 ° by the first determination step. Do not judge as a thing.

FIG. 11 shows a case where the detection target is a low-height rectangular box. In FIG. 4B, the slope m of the measurement points p1 to p9 by the least squares method was 0.2706, which was smaller than the threshold value T2 in the range of 0.6 to 0.7 and larger than 0. .. Therefore, since the control device 3 does not determine the box as the obstacle G in the first determination step, it is necessary to make a comparison determination with the coefficient of determination ^R2 in the next second determination step.

On the other hand, according to the second embodiment, in FIG. 11, the slope a of the measurement points p1 to p7 by the least squares method is 1.5372, and the slope 1 / a is 0.65. If the threshold value T4 is set to 0.6, which is the value of a slope of 30 °, the slope 1 / a becomes a value larger than the threshold value T4, and the control device 3 sets the box as an obstacle G by the first determination step. It will be judged. Therefore, in this case, the second determination step for comparison with the coefficient of determination R ² becomes unnecessary.

In addition to the example shown in FIG. 11, the present inventor conducted a large number of tests on a relatively small box, other flat objects, and the like. As a result, as in this second embodiment, the horizontal axis is more than the first determination step using the inclination m calculated by the least squares method in the scatter diagram with the horizontal axis as x-data and the vertical axis as z-data. The first judgment step using the gradient 1 / a calculated by the least squares method in the scatter diagram with z data and x data on the vertical axis improves the accuracy of distinguishing between the slope and the obstacle G. There was found.

FIG. 14 shows an example of the obstacle determination flow in the second embodiment. Since steps S1 to S3 and S7 to S9 are the same as those in FIG. 7, the description thereof will be omitted. The control device 3 calculates the slope a from the x data and the z data in step S11 by the least squares method, and in step S12 whether the slope 1 / a is larger than the threshold value T4 or smaller than 0. Make a judgment. If Yes, it is determined that there is an obstacle G, and the process proceeds to step S7. If No, the process returns to step S1.

In the second embodiment as well, a second determination step for comparing the coefficient of determination ^R2 and the threshold value T3 may be provided. In this case, in FIG. 14, when No is obtained in step S12, the same step S6 as in FIG. 7 may be executed.

"Third embodiment"
In the second embodiment, as the first determination step, the horizontal axis is a scatter diagram of z data and the vertical axis is a scatter diagram of x data, and the slope a is calculated by the least squares method of the equation (3), and this slope a is used as the denominator. The slope 1 / a and the threshold value T4 were compared. On the other hand, the third embodiment is the same as the second embodiment up to the point where the horizontal axis is a scatter diagram of z data and the vertical axis is a scatter diagram of x data, and the slope a is calculated by the least squares method of the equation (3). However, it is different from the second embodiment in that the slope a is directly compared with the threshold value T5 without being fractionated. In the third embodiment, it is determined that there is an obstacle G when the slope a is smaller than the threshold value T5.

When the slope of the slope is θ, the threshold value T5 is obtained by “tan (90-θ)”. When θ is 30 °, the threshold T5 is about 1.73, and when θ is 35 °, the threshold T5 is about 1.43. Therefore, by setting the threshold value T5 in the range of 1.4 to 1.7, the accuracy of distinguishing between the slope and the obstacle G can be improved. Hereinafter, the scatter plots of FIGS. 9 to 13 will be verified when the threshold value T5 is set to 1.7, which is a value of a slope of 30 °.

Since the slope a in FIG. 9 is −0.0286, it is smaller than the threshold value T5. As a result, the control device 3 determines that the worker is an obstacle G.
Since the slope a in FIG. 10 is 0.0831, it is smaller than the threshold value T5. As a result, the control device 3 determines that the box is an obstacle G.
Since the slope a in FIG. 12 is 2.8784, it is larger than the threshold value T5. As a result, the control device 3 does not determine a slope with a gradient of 18.6 ° as an obstacle.
Since the slope a in FIG. 13 is 11.303, it is larger than the threshold value T5. As a result, the control device 3 does not determine the slope with a gradient of 4.2 ° as an obstacle.
Since the slope a in FIG. 11 is 1.5372, it is smaller than the threshold value T5. As a result, the control device 3 determines that the box is an obstacle G. Therefore, as in the second embodiment, the second determination step for comparison with the coefficient of determination ^R2 becomes unnecessary. As a result of verification with other objects, it was found that the accuracy of distinguishing between the slope and the obstacle G is improved also in this third embodiment.

FIG. 15 shows an example of the obstacle determination flow in the third embodiment. Since steps S1 to S3, S11, and S7 to S9 are the same as those in FIG. 14, the description thereof will be omitted. The control device 3 determines in step S13 whether or not the inclination a is smaller than the threshold value T5. If Yes, it is determined that there is an obstacle G, and the process proceeds to step S7. If No, the process returns to step S1.

Also in the third embodiment, a second determination step for comparing the coefficient of determination ^R2 and the threshold value T3 may be provided. In this case, in FIG. 15, when No is obtained in step S13, the same step S6 as in FIG. 7 may be executed.

"Fourth Embodiment"
The control device 3 has performed a "height selection step" in each step S3 of FIGS. 7, 14, and 15 to select only the measurement data whose z data is larger than the threshold value T1. Instead of this "height selection step", the control device 3 of the fourth embodiment selects only the measurement data in which the reflection intensity of the reflected light is larger than the threshold value T6, and determines the presence or absence of an obstacle, "reflection intensity selection". Take "steps". That is, the control device 3 determines the presence / absence of the obstacle G based on the measurement data regarding the coordinates measured by the distance image sensor 2 and the reflection intensity of the reflected light.

Examples of the object that can be detected by the distance image sensor 2 include water vapor and dust, in addition to people and objects that hinder traveling. In the rolling of asphalt road surface with a rolling roller, in order to prevent the asphalt mixture from adhering to the rolling wheels such as tires, the rolling wheels are rolled while spraying an asphalt adhesion preventive agent or water. Often. These liquids may generate water vapor when they come into contact with hot asphalt road surfaces or tire surfaces. In addition, in the compaction work of soil-based ground, dust may fly up from the ground. It is not preferable to determine that even these water vapor and dust are obstacles from the viewpoint of work efficiency.

In response to this problem, the present inventor paid attention to the reflection intensity of the reflected light, and decided to improve the determination accuracy of the obstacle G by adding the reflection intensity correction function. The reflection intensity of the reflected light varies depending on the distance, shape, material, color tone, etc. of the object, and the reflection intensity of water vapor and dust is lower than that of a person or a solid object. The present inventor made the following analysis, paying particular attention to the reflected light from the ground.

As shown in FIG. 16A, when there is no obstacle in the measurement range of the distance image sensor 2, all the measured coordinate data are related to the ground. In this case, as shown in FIG. 16B, the graph S1 showing the relationship between x and z shows that the z data, that is, the height component is 0, and there are no obstacles over all the x data. .. On the other hand, the reflection intensity of the reflected light tends to decrease as the distance between the measurement point and the distance image sensor 2 increases. Therefore, in the graph P1 showing the relationship between x-reflection intensity, the larger the x data, that is, the farther away from the vehicle, the lower the reflection intensity of the reflected light from the ground.

As shown in FIG. 17A, when the obstacle G is present within the measurement range of the distance image sensor 2, as shown in FIG. 17B, the graph S2 showing the relationship of xz is the obstacle. The z data changes according to the measurement point of G. From this, it can be seen that the obstacle G exists. The graph P2 showing the relationship between x-reflection intensity shows a value higher than the original reflection intensity from the ground with respect to the reflection intensity of the reflected light from the obstacle G, although it depends on the color tone of the obstacle G.

As shown in FIG. 18A, when fine particles F such as water vapor and dust are suspended in the measurement range of the distance image sensor 2, the relationship of xz is as shown in FIG. 18B. , Represented as plots S3 scattered approximately randomly. Depending on the distribution state of the plot S3, water vapor, dust, etc. may also be determined to be obstacles. On the other hand, regarding the relationship of x-reflection intensity, since the reflection intensity of the reflected light from water vapor or dust is low, the graph P3 is about the same as the graph P1 in FIG. 16B or has a lower reflection intensity than the graph P1. It turned out to be a trend. Therefore, when the reflection intensity of the graph P1 in FIG. 16B, that is, the reflection intensity of the reflected light from the ground is set to the threshold value T6, and the reflection intensity of the reflected light from the object is equal to or less than the threshold value T6, the control device 3 controls the object. If it is regarded as a floating fine particle F and is not determined as an obstacle G, the determination accuracy of the obstacle G can be improved.

As the threshold value T6, a fixed value pattern consisting of constant values obtained in advance by simulation or the like and a variation obtained based on the calculation result of calculating the reflection intensity of the reflected light from the ground obtained during actual construction operation in real time. The value pattern is mentioned. In the latter case of the fluctuation value pattern, the threshold value T6 is determined by fluctuating in real time based on the material and color tone of the road surface that is actually compacted, so that the accuracy of the threshold value T6 is improved and water vapor, dust, etc. are removed from the obstacle G. Since the accuracy of the cancel function to be excluded can be further improved, the detection accuracy of the obstacle G can be improved as a result. Further, the fixed value pattern and the variable value pattern may be configured to be manually switched by the operator, or the fixed value pattern and the variable value pattern may be automatically determined by the control device 3 to be switched.

As an experimental example, the present inventor measured the water vapor 6, the desk 7, the person 8, and the ground 9 ejected from the humidifier 21 with the distance image sensor 2. 20 to 23 show the measurement data. FIG. 20 is a graph relating to x-reflection intensity, and FIG. 20A shows a state in which no correction is made for the reflection intensity, and the portion surrounded by the dotted line shows the measurement data of the ground 9. FIG. 20B is a graph showing only the measurement data of the reflection intensity higher than the threshold value T6, where the measurement data of the ground 9 is set to the threshold value T6. The threshold value T6 is preferably a value obtained by adding the standard deviation σ to the value obtained by the least squares method of the measurement data, and the standard deviation σ is preferably set to 1.5σ to 3σ.

21, FIG. 22, and FIG. 23 are graphs relating to xz, yz, and yx, respectively. In these figures, each (a) is a graph when the reflection intensity is not corrected, and each (b) is a graph when the reflection intensity is corrected by the threshold value T6. In FIGS. 21 (a), 22 (a), and 23 (a) before the correction, the measurement data of the water vapor 6, the desk 7, the person 8, and the ground 9 are all displayed, whereas the threshold value T6 is used. In FIGS. 21 (b), 22 (b), and 23 (b) after the correction, only the measurement data of the desk 7 and the person 8 are displayed, and the measurement data of the water vapor 6 and the ground 9 are canceled. I understand. Of course, the obstacles to be detected in the construction vehicle are objects with a certain height such as desk 7 and person 8, so even if the measurement data of the ground 9 is canceled by the correction by the threshold value T6, there is no problem. do not have.

FIG. 24 shows an example of the obstacle determination flow of the fourth embodiment. Since steps S1 and S2 are the same as those in FIGS. 7, 14, and 15, the description thereof will be omitted. In step S31, the control device 3 determines whether or not the reflection intensity of the measurement data of each pixel is higher than the threshold value T6, and in the case of Yes, in step S4 of FIG. 7 or step S11 of FIGS. 14 and 15. If No, the process returns to step S1.

As described above, if the presence or absence of the obstacle G is determined based on the measurement data related to the coordinates measured by the distance image sensor 2 and the reflection intensity of the reflected light, the presence or absence of the obstacle G can be determined. The erroneous determination of the fine particles F as the obstacle G is reduced. This makes it possible to suppress unnecessary obstacle detection.

Further, if the control device 3 is configured not to determine the obstacle G when the reflection intensity is equal to or less than the threshold value T6, the fine particles F are erroneously determined to be the obstacle G by setting the threshold value T6 to an appropriate value. This can be reduced with a simple configuration.

Since the reflection intensity of the reflected light from the fine particles F is almost the same as or less than the reflection intensity of the reflected light from the ground, the threshold T6 is set to a value based on the reflection intensity of the reflected light from the ground to obtain fine particles. It is possible to further reduce the erroneous determination of the object F as the obstacle G.
Since it is possible to prevent erroneous determination of unevenness on the ground by correcting the reflection intensity, there is no need for a "height selection step".

1 Obstacle detection device 2 Distance image sensor 3 Control device 4 Detection range 5 Non-detection range 10 Tire roller (construction vehicle)

Claims (15)

An obstacle detection device mounted on a construction vehicle
A TOF distance image sensor that measures the distance from the time difference between the projected light and the reflected light,
A control device that determines the presence or absence of obstacles based on the measurement data of the distance image sensor, and
Equipped with
Regarding the measurement data, when the vehicle traveling direction component from the vehicle end is x data, the vehicle width direction component is y data, and the height direction component from the ground is z data.
The control device performs a height selection step of selecting only measurement data whose z data is larger than the threshold value T1 and determining the presence or absence of an obstacle.
The first determination step of calculating the slope of the distribution from the x data and z data of the measurement data selected in the height selection step and determining the presence or absence of an obstacle based on this slope,
The control device calculates the coefficient of determination ^R2 by the following equation (2) based on the x data and z data of the measurement data selected in the height selection step .

When it is determined that there is no obstacle in the first determination step, it is determined that there is an obstacle when the coefficient of determination is smaller than the threshold value T3, and when the coefficient of determination is larger than the threshold value T3, it is regarded as an obstacle. An obstacle detection device for a construction vehicle, characterized in that it has a second determination step that does not determine . An obstacle detection device mounted on a construction vehicle
A TOF distance image sensor that measures the distance from the time difference between the projected light and the reflected light,
A control device that determines the presence or absence of obstacles based on the measurement data of the distance image sensor, and
Equipped with
Regarding the measurement data, when the vehicle traveling direction component from the vehicle end is x data, the vehicle width direction component is y data, and the height direction component from the ground is z data.
The control device performs a height selection step of selecting only measurement data whose z data is larger than the threshold value T1 and determining the presence or absence of an obstacle.
The slope of the distribution is calculated from the x data and z data of the measurement data selected in the height selection step, and the first determination step of determining the presence or absence of an obstacle is performed based on this inclination.
In the first determination step, the inclination is the inclination a calculated by the minimum square method in the scatter diagram of the z data on the horizontal axis and the x data on the vertical axis, and the inclination 1 obtained from the inclination a. An obstacle detection device for a construction vehicle, characterized in that it determines that there is an obstacle when / a is larger than the threshold value T4 or smaller than 0 . An obstacle detection device mounted on a construction vehicle
A TOF distance image sensor that measures the distance from the time difference between the projected light and the reflected light,
A control device that determines the presence or absence of obstacles based on the measurement data of the distance image sensor, and
Equipped with
Regarding the measurement data, when the vehicle traveling direction component from the vehicle end is x data, the vehicle width direction component is y data, and the height direction component from the ground is z data.
The control device performs a height selection step of selecting only measurement data whose z data is larger than the threshold value T1 and determining the presence or absence of an obstacle.
The slope of the distribution is calculated from the x data and z data of the measurement data selected in the height selection step, and the first determination step of determining the presence or absence of an obstacle is performed based on this inclination.
In the first determination step, the slope is the slope a calculated by the least squares method in the scatter plot of the z data on the horizontal axis and the x data on the vertical axis.
An obstacle detection device for a construction vehicle, characterized in that it is determined that there is an obstacle when the inclination a is smaller than the threshold value T5 . The control device calculates the coefficient of determination ^R2 by the following equation (2) based on the x data and z data of the measurement data selected in the height selection step .

When it is determined that there is no obstacle in the first determination step, it is determined that there is an obstacle when the coefficient of determination is smaller than the threshold value T3, and it is not determined as an obstacle when the coefficient of determination is larger than the threshold value T3. The obstacle detection device for a construction vehicle according to claim 2 or 3, further comprising a second determination step . An obstacle detection device mounted on a construction vehicle
A TOF distance image sensor that measures the distance from the time difference between the projected light and the reflected light,
A control device that determines the presence or absence of an obstacle based on the measurement data of the distance image sensor and the reflection intensity of the reflected light.
Equipped with
The reflection intensity of the reflected light is measured by the TOF type distance image sensor.
The control device has a reflection intensity selection step of selecting only measurement data in which the reflection intensity of the reflected light is larger than the threshold value T6 and determining the presence or absence of an obstacle.
The first determination step of calculating the slope of the distribution from the x data and z data of the measurement data selected in the reflection intensity selection step and determining the presence or absence of an obstacle based on this slope,
The control device calculates the coefficient of determination ^R2 by the following equation (2) based on the x data and z data of the measurement data selected in the reflection intensity selection step .

When it is determined that there is no obstacle in the first determination step, it is determined that there is an obstacle when the coefficient of determination is smaller than the threshold value T3, and it is not determined as an obstacle when the coefficient of determination is larger than the threshold value T3. An obstacle detection device for a construction vehicle, characterized by having a second determination step . The construction vehicle according to claim 1 or 5 , wherein the horizontal axis is the x-data and the vertical axis is the slope m calculated by the least squares method in the scatter plot of the z-data. Obstacle detection device. The obstacle detection device for a construction vehicle according to claim 6 , wherein it is determined that there is an obstacle when the inclination m is larger than the threshold value T2, smaller than the threshold value T2, or divided by zero. An obstacle detection device mounted on a construction vehicle
A TOF distance image sensor that measures the distance from the time difference between the projected light and the reflected light,
A control device that determines the presence or absence of an obstacle based on the measurement data of the distance image sensor and the reflection intensity of the reflected light.
Equipped with
The reflection intensity of the reflected light is measured by the TOF type distance image sensor.
The control device has a reflection intensity selection step of selecting only measurement data in which the reflection intensity of the reflected light is larger than the threshold value T6 and determining the presence or absence of an obstacle.
The slope of the distribution is calculated from the x data and z data of the measurement data selected in the reflection intensity selection step, and the first determination step of determining the presence or absence of an obstacle is performed based on this slope.
In the first determination step, the slope is the slope a calculated by the least squares method in the scatter plot of the z data on the horizontal axis and the x data on the vertical axis, and the slope 1 obtained from the slope a. An obstacle detection device for a construction vehicle, characterized in that it determines that there is an obstacle when / a is larger than the threshold T4 or smaller than 0 . An obstacle detection device mounted on a construction vehicle
A TOF distance image sensor that measures the distance from the time difference between the projected light and the reflected light,
A control device that determines the presence or absence of an obstacle based on the measurement data of the distance image sensor and the reflection intensity of the reflected light.
Equipped with
The reflection intensity of the reflected light is measured by the TOF type distance image sensor.
The control device has a reflection intensity selection step of selecting only measurement data in which the reflection intensity of the reflected light is larger than the threshold value T6 and determining the presence or absence of an obstacle.
The slope of the distribution is calculated from the x data and z data of the measurement data selected in the reflection intensity selection step, and the first determination step of determining the presence or absence of an obstacle is performed based on this inclination.
In the first determination step, the slope is the slope a calculated by the least squares method in the scatter plot of the z data on the horizontal axis and the x data on the vertical axis.
An obstacle detection device for a construction vehicle, characterized in that it is determined that there is an obstacle when the inclination a is smaller than the threshold value T5 . The control device calculates the coefficient of determination ^R2 by the following equation (2) based on the x data and z data of the measurement data selected in the reflection intensity selection step .

When it is determined that there is no obstacle in the first determination step, it is determined that there is an obstacle when the coefficient of determination is smaller than the threshold value T3, and it is not determined as an obstacle when the coefficient of determination is larger than the threshold value T3. The obstacle detection device for a construction vehicle according to claim 8 or 9, wherein the device has a second determination step . The obstacle detection device for a construction vehicle according to any one of claims 1 to 3, wherein the threshold value T1 is in the range of 5 to 30 cm. The obstacle detection device for a construction vehicle according to claim 7 , wherein the threshold value T2 is in the range of 0.6 to 0.7. The obstacle detection device for a construction vehicle according to claim 2 or 8 , wherein the threshold value T4 is in the range of 0.6 to 0.7. The obstacle detection device for a construction vehicle according to claim 1, claim 4, claim 5 or claim 10 , wherein the threshold value T3 is in the range of 0.6 to 1.0. The obstacle detection device for a construction vehicle according to claim 3 or 9 , wherein the threshold value T5 is in the range of 1.4 to 1.7.

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