JPH07296291A - Traveling lane detector for vehicle - Google Patents

Abstract

PURPOSE:To detect the traveling lane one's own vehicle by using a vehicle traveling ahead. CONSTITUTION:An object detecting means 110 detecting the relative distance, relative speed, azimuth between the object in front of ones' own vehicle (A) and the vehicle (A) and a yaw angle detecting sensor 111 detecting the speed signal of the vehicle (A) and the change of the yaw angle are provided on the traveling lane detecting device for vehicle which detects the traveling lane of the vehicle (A). A traveling locus calculation means 112 calculates the traveling locus being the change with time of the position of the vehicle (A). A traveling locus calculation means 113 of an object to be detected adds the relative distance of the object to be detected and the position of the object to be detected calculated from the azimuth angle of the object to be detected to the position of the vehicle (A) and calculates the traveling locus being the change with time of the position of the object to be detected. A traveling lane decision means 114 decides the traveling locus of the object to be detected as the traveling lane of the vehicle (A) when the traveling locus of the vehicle (A) is overlapped with the traveling locus of the object to be detected. Thus, the traveling lane of the vehicle (A) can be decided when it is parallel to the traveling locus of the object to be detected.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a vehicle automatic steering system and a vehicle obstacle detection system. In particular, the present invention utilizes a front vehicle when the vehicle is following the vehicle ahead. The present invention relates to a traveling road detection device that detects a traveling road of a vehicle.

[0002]

2. Description of the Related Art In recent years, research and development in the field of autonomous driving have been actively conducted for the purpose of reducing the driving burden on drivers, improving safety, and increasing road traffic efficiency. Against this background, development of an automatic driving system has been undertaken, assuming automatic driving on a dedicated lane of a highway, which is considered to be the closest to practical use as automatic driving. In this development, when the vehicle is automatically steered, it is necessary to detect the traveling path of the host vehicle in order to control the steering angle of the vehicle. As a conventional technique for this purpose, a method of detecting a white line indicating the road edge of the road by image processing and controlling the steering angle so as to follow the white line is considered.

Various image processing techniques and lasers have been considered to detect obstacles in the traveling direction of the vehicle.
As such a device, there is known a device that picks up a stereo image in image processing and measures the distance distribution of an object in front by stereoscopic vision using the principle of triangulation. In this case, when it is determined that the obstacle is an obstacle from the obtained distance distribution of the object, first, two white lines indicating the road edge are detected from the captured image and the inside of the vehicle is traveled. And
A detected object in this traveling path is detected as an obstacle candidate, and when it becomes a certain distance or less of the detected object, it is judged as an obstacle and an alarm is issued.

[0004]

However, when such a method is used, it is necessary that a white line indicating the road edge is picked up in the picked-up image. In a real environment, a white line is drawn by the road. There is a problem that there are not places. Further, in rainy weather, there is a problem that the white line cannot be seen due to the water splash of the preceding vehicle, or it becomes difficult to detect the white line, which makes it very difficult to detect the white line. Further, since the white line is thinly connected in the image at a long distance, there is a problem that the white line at a long distance may not be accurately detected when the road is curved and greatly changes.

Therefore, in view of the above problems, the present invention provides a vehicle running road detecting device capable of stably detecting running roads and obstacles without being restricted by road conditions and weather. To aim.

[0006]

In order to solve the above-mentioned problems, the present invention provides a vehicle traveling road detecting device having the following configuration. That is, the vehicle running path detection device for detecting the running path of the own vehicle includes an object detecting unit for detecting a relative distance, a relative speed, and an azimuth angle between the object in front of the own vehicle and the own vehicle, and the own vehicle. A vehicle speed and yaw angle detection sensor for detecting a change in the speed signal and the yaw angle of the vehicle. The running locus calculation means of the own vehicle, which inputs the change amount of the own vehicle speed and the yaw angle, calculates the running locus which is a temporal change of the position of the own vehicle. The traveling locus calculation means of the detected object is the time change of the position of the detected object by adding the position of the detected object calculated from the relative distance and the azimuth of the detected object to the position of the host vehicle. Calculate the travel locus. The traveling path determining means determines the traveling path of the detected object as the traveling path of the own vehicle when the traveling path of the host vehicle overlaps the traveling path of the detected object.

Further, the traveling path determining means determines the traveling path of the own vehicle from the traveling locus in front of the detected object when the traveling locus of the own vehicle is parallel to the traveling locus of the detected object. You may do it. The traveling locus calculation means of the own vehicle may change the origin of the traveling locus according to the setting conditions.

Further, an obstacle candidate judging means for judging an object to be detected on the road ahead of the traveling road of the own vehicle determined by the vehicle traveling road detecting device as an obstacle candidate, and judging as the obstacle candidate. When the relative distance to the detected object becomes a certain distance or less or approaches at a certain relative speed or more, an obstacle determining means for determining the detected object as an obstacle is added. It may be provided.

[0009]

According to the vehicle road detection device of the present invention, the relative distance, relative speed, and azimuth between the object in front of the host vehicle and the host vehicle are detected, and the speed signal and yaw of the host vehicle are detected. The amount of change in the angle is detected, and a running locus that is a temporal change in the position of the own vehicle is calculated from the amount of change in the vehicle speed and the yaw angle, and the relative position of the detected object is calculated at the position of the own vehicle. The position of the detected object calculated from the distance and the azimuth is added to calculate a running locus, which is a temporal change in the position of the detected object, and the running locus of the own vehicle overlaps the running locus of the detected object. Since the traveling locus of the detected object is determined to be the traveling path of the own vehicle while the vehicle is in motion, the traveling path of the own vehicle can be obtained without using the white line indicating the end of the traveling road.

Further, when the running locus of the vehicle is parallel to the running locus of the detected object, the running path of the own vehicle is determined from the running locus in front of the detected object to determine the running path. Increase the variety of. Further, by changing the origin of the traveling locus according to the set conditions, it is possible to prevent the errors of the traveling loci from accumulating with the passage of time, and the detection accuracy of the traveling road from being deteriorated.

Further, the traveling route of the host vehicle determined by the vehicle traveling route detecting device can be used to determine an object in front of which the traveling object is detected as an obstacle candidate. When the relative distance to the detected object determined as a candidate becomes equal to or less than a certain distance or approaches at the relative speed of a certain amount or more, the detected object can be determined as an obstacle.

[0012]

Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is a diagram showing the overall configuration of a vehicle travel detection device according to an embodiment of the present invention. The vehicle traveling road detection device shown in the figure is for detecting the traveling road of the own vehicle and for detecting obstacles.First, the relative position of an object in front of the own vehicle to the own vehicle is detected. An object detection unit 110 that calculates position data of a detected object is provided. The object detecting means 110 is composed of two cameras 2 consisting of cameras A and B.
10 and 211, and a detection unit 212 that detects the position of the front object relative to the own vehicle by stereoscopic vision using the principle of triangulation using the stereo images of the front of the vehicle captured by the cameras 210 and 211 as data. To do.

Further, the vehicle traveling road detecting device includes:
A yaw angle detection sensor 213 for detecting the yaw angle in order to obtain a change amount Δθ of the yaw angle of the own vehicle, and a speed V of the own vehicle.
A vehicle speed and yaw angle detection unit 111 having a speed detection sensor 214 for detecting Instead of the signal from the yaw angle detection sensor 213, the signals from the steering angle detection sensor of the steering wheel and the angular acceleration detection sensor may be used.

The vehicle speed and yaw angle detection sensor 1
11, a traveling locus calculation means 11 of the own vehicle, which is provided in the latter stage of 11.
2 inputs the data of the velocity V of the host vehicle and the change component Δθ of the yaw angle, and calculates the traveling locus data of the host vehicle based on these data. FIG. 2 is a diagram showing a traveling locus map obtained by the traveling locus calculation means 112 of the own vehicle in FIG. The travel locus shown in this figure is expressed by the following equation (1),
It is obtained by using (2).

[0015] Here, ZXL (t1) is the position of the own vehicle in the X direction on the traveling locus map at time t1, and ZYL (t1).
Is the position of the host vehicle in the Y direction in the travel locus map at time t1, Δθ (t) is the change in the yaw angle of the host vehicle at time t, and θs (t) is the host vehicle at time t. In the direction of Where V (t) is the speed of the host vehicle at time t, and t
0 is the time when the apparatus starts operating.

Next, the traveling locus calculation means 113 of the detected object, which is provided at the subsequent stage of the object detection means 110 and the traveling locus calculation means 112 of the own vehicle, inputs the traveling locus data of the own vehicle and the position data of the detected object. Then, the traveling locus data of the detection object A is calculated based on these data. The traveling locus data of the detected object is the traveling locus calculation means 1 of the detected object A.
In 13, the position of the detected object at each time is added to the traveling locus of the host vehicle, and is calculated by the following equations (3) and (4).

[0017] Here, KXL (t1) is the position of the detected object in the X direction on the traveling locus map at time t1, and KYL (t
1) is the position of the detected object in the Y-direction in the travel locus map at time t1, d (t1) is the relative distance between the host vehicle and the detected object at time t1, and Δθk (t
1) is the orientation of the detected object with respect to the host vehicle at time t1.

The calculation of d (t1) and Δθk (t1) will be described later. FIG. 3 is a diagram showing an example of obtaining each traveling locus with respect to the movement of the detected object A using the position of the own vehicle. As shown in the figure, since the moving amount and the moving direction of the own vehicle at each time are known, the moving amount can be integrated by the equations (1) and (2) to calculate the traveling locus of the own vehicle. The circle marks in the figure are the calculated travel loci, and so on. Further, the object detection means 110 gives the relative distance and the azimuth angle between the own vehicle and the detected object A, and the movement amount of the detected object A with respect to the own vehicle at each time is known, and the moving direction of the detected object with respect to the own vehicle is known. Therefore, the traveling locus of the detected object A can be calculated by adding the movement amount to the traveling locus of the own vehicle by the equations (3) and (4).

Further, the running locus calculation means 112 of the own vehicle.
Further, the traveling path determination means 114 provided in the latter stage of the traveling locus calculation means 113 of the detected object inputs the traveling locus data of the own vehicle and the traveling locus data of the detected object, and based on these data, the traveling locus data of the own vehicle. Is along the traveling locus data of the detected object, the traveling locus of the detected object in front is determined as the traveling path of the host vehicle as follows.

FIG. 4 is an example before the traveling path determination means 114 of FIG. 1 overlaps the traveling loci of the vehicle and the detected object A, and shows an example in which the traveling path is not yet determined. As shown in the figure, the traveling locus of the detected object A is calculated, but the detected object A at a certain time t1 is at the position of P1, and Δt1 seconds after that, the vehicle and the detected object are still at the position of P1. The traveling loci of A may not overlap. The traveling path determination unit 114 detects a state where the traveling loci do not overlap each other from the positions of the own vehicle and the detected object A in this way. As a result of this detection, the travel route determination means 114 has not yet determined the travel route of the host vehicle.

FIG. 5 is a diagram showing an example of a process in the case where the running path determining means 114 of FIG. 1 connects the running loci of the vehicle and the detected object A, and shows an example in which the running path is not yet determined. Is. As shown in the figure, the traveling locus of the host vehicle may reach the traveling locus of the detected object A at time t2, and the mutually traveling loci may be connected. The traveling path determination means 114
Similarly to the above, the state in which the traveling loci are connected is detected from the positions of the vehicle and the detected object A. Driving route determination means 11
As a result of this detection, No. 4 has not yet determined the travel route of the host vehicle.

FIG. 6 is a diagram showing an example in which the traveling paths of the vehicle and the detection object A overlap each other for a certain period of time in the traveling path determining means 114 of FIG. 1, and the traveling path is determined. As shown in the figure, there may be a case where they overlap each other for a certain period of time after the time t2. In the same manner as described above, the traveling path determination unit 114 detects a state in which the traveling loci overlap by Δt2 seconds, for example, from the positions of the own vehicle and the detected object A. As a result of this detection, the travel route determination unit 114 determines that the travel route of the host vehicle is the travel locus of the detected object A. The determined traveling path is displayed and output by the traveling path output means 115.

FIG. 7 is a diagram showing an example in which the own vehicle travels in parallel with the traveling locus of the detected object B for a certain period of time in the traveling path determining means 114 of FIG. 1, and in which the traveling path is determined. . As shown in the figure, when the vehicle travels in parallel with the traveling locus of the detection object B for Δt3 seconds, it is considered that the detection object B is traveling in the adjacent lane. The fact that the traveling loci of the detected object B and the own vehicle are parallel is confirmed by obtaining the distance between the present position of the own vehicle and the traveling locus of the detected object and keeping the minimum value constant. Then, the traveling path determination unit 114 determines, as the traveling path of the host vehicle, a curve that is parallel to the trajectory of the detected object B in front of the host vehicle and starts from the front of the host vehicle.

Therefore, according to this embodiment, it is possible to obtain the traveling path of the host vehicle without using the white line indicating the end of the traveling road. By the way, when the traveling locus is calculated by the traveling locus calculation means 112 of the own vehicle, if the origin of the coordinate system of the map of the traveling locus is set to the position of the own vehicle at a certain time and the traveling locus is calculated, The error of the traveling locus accumulates with the passage of time, and the accuracy of detecting the traveling path deteriorates as follows.

FIG. 8 is a diagram showing an example of calculating the traveling loci of the own vehicle and the detected object A with the position of the own vehicle at time t0 as the coordinate origin of the map of the traveling locus. As shown in this figure,
Measured values for calculating the running trajectory over time,
That is, errors such as the speed of the hour vehicle, the amount of change in the yaw angle, and the position of the detected object with respect to the host vehicle are added together in the process of calculating the travel locus. Then, with respect to the correct traveling locus Ls1 of the own vehicle and the traveling locus Ls2 of the detected object A, the traveling locus Lg1 of the own vehicle and the traveling locus Lg2 of the detected object A, which have accumulated errors, have a large deviation from the time t0 + tk. Since it is calculated, it causes an erroneous detection in the road detection and the obstacle determination described later.

For this reason, the vehicle traveling path detection device is provided with a change origin calculation instruction means 116 for the travel locus, and the change origin calculation instruction means 116 for the travel trajectory sets a certain condition and meets this condition. At this time, the origin of the coordinate system of the map of the traveling locus is changed to cancel the accumulated error of the traveling locus as follows. FIG. 9 is a diagram for explaining an example in which the origin of the coordinate system of the map of the traveling locus is changed to the current time by the change instruction unit 116 of the calculation origin of the traveling locus at constant time intervals. Calculation of travel locus Calculation origin change instruction means 1
When the origin of the coordinate system of the map of the traveling locus is changed to the position of the own vehicle at the current time by 16 at a constant time interval tk, the locus after time t0 + tk in FIG. 8 becomes as shown in FIG.
The calculation of the traveling locus The transformation of the coordinate system by the origin changing instruction means 116 only calculates the traveling locus by changing the starting point of the integration range in equations (1) to (4) from t0 to t0 + tk. As a result, the error between the correct solid line travel locus in the coordinate system of FIG. 9 and the calculated travel locus is reduced as compared with the coordinate system of FIG. In this way, when the coincidence of the traveling loci in the coordinate system of FIG. 9 is determined, it is not possible to determine the area of the traveling locus Lb of the detected object in FIG. 8, so this improvement will be described below.

FIG. 10 is a view for explaining an example of converting the traveling locus of the detected object A in the past fixed time into the coordinate system of FIG.
As shown in the figure, the traveling locus of the detected object A corresponding to the traveling locus Lb of FIG. 8 in the past fixed time tm is converted into the coordinates of FIG. As a result, the traveling locus L of the detected object A in FIG.
It enables a decision on the region of b. Next, an obstacle candidate determination unit 117 is provided at a stage subsequent to the travel route determination unit 114, and the obstacle candidate determination unit 117 is included in the travel route determination unit 114.
In the case of obtaining the determined travel route according to, the detected object A whose traveling locus and the traveling locus of the own vehicle overlap is determined as an obstacle candidate. After that, while the traveling locus of the own vehicle overlaps with the traveling locus of the detected object A, the traveling locus of the detected object A in front is the traveling path of the own vehicle, and the detected object A is an obstacle candidate. To be judged. That is, the detected object A on the traveling path of the own vehicle
Is because there is a possibility of collision with the own vehicle.

FIG. 11 is a diagram showing an example in which the obstacle candidate judging section 117 in FIG. 1 judges that another detected object C is an obstacle candidate. As shown in this figure, the detected object C moves to the traveling path of the host vehicle previously detected, and as in FIG. 6, Δt2
When overlapping for a second, the obstacle candidate determination unit 117 determines that the detected object C is an obstacle candidate. Next, the obstacle determination unit 118 provided in the subsequent stage of the obstacle candidate determination unit 117 inputs the data of the relative distance and the relative speed from the object detection unit 110, and recognizes the own vehicle and these detected objects A and B. When the distance between the detection objects A and B is equal to or less than a certain value or the relative speed between the own vehicle and these detection objects is equal to or greater than a certain value and the possibility of a collision becomes extremely high, these detection objects A and B are obstacles. To determine.

The alarm output means 119 provided at the subsequent stage of the obstacle determining section 118 issues an alarm when it is determined that the vehicle is dangerous and alerts the driver. Next, the object detection means 11
Details of 0 will be described. FIG. 12 shows the object detecting means 11 of FIG.
It is a figure explaining the example which calculates the distance to a three-dimensional object based on the stereoscopic vision by the cameras 210 and 211 of 0. The cameras 210 and 211 shown in this figure (a) are provided with two lenses 12 and 13 in order to form two viewpoints facing an object to be detected. After the two lenses 12 and 13, image pickup devices 14 and 15 are provided whose optical axes 16 and 17 coincide with each other. The image pickup devices 14 and 15 are, for example, CCDs (Charge Coupled Devices). Here, “d” in the figure is the distance between the lenses 12 and 13 and the three-dimensional object, “f” is the focal length of the lenses 12 and 13, and “a” and “b” are the three-dimensional object 11 respectively. The same point is the distance from the optical axis of the position projected on the image pickup elements 14 and 15, and “m” is the distance between the optical axes (base line length). In this figure (b), the image pickup device on the right side of this figure (a) is moved by the base line length “m”, and is superimposed on the image pickup device on the left side. As shown in this figure (b), the triangle ABC and the triangle ADE are similar to each other. Therefore, the ratio of (a + b) and m becomes the same as the ratio of f and d. In the expression, (a + b): m = f: d, which is transformed into the following expression.

D = fm · (a + b) (5) If m and f are measured in advance and the distance of (a + b) can be measured, the distance d can be derived from the equation (5). This is called stereoscopy. Further, the relative distance d (t) can be obtained by the time change of this distance. In the method of obtaining (a + b) in FIG. 12, the brightness values of the three-dimensional objects in the left and right images are compared while being shifted little by little, and the most matching shift amount is obtained. This shift amount corresponds to (a + b), and this shift amount (a + b) is called parallax p. The detection unit 212 receiving the signals of the cameras 210 and 211 calculates this parallax p as follows.

Correlation calculation is used as a method for checking the degree of coincidence as a method for obtaining the above-mentioned most coincident shift amount. Regarding the correlation calculation method, the standard method introduced in books such as image recognition will be described below. Another method is an extension of this standard method. To simplify the description, a one-dimensional brightness value sequence will be described first. Figure 1
3 is a diagram showing an example of images formed on the image pickup devices 14 and 15 of FIG. This figure (a) is a left image by the image sensor 14 (referred to as "left eye image"), and this figure (b) is the image sensor 1
5 shows a right image (referred to as a “right eye image”) according to No. 5.

FIG. 14 is a graph showing an example in which the luminance values of one row indicated by reference numerals 22 and 23 in FIG. 13 are taken out. Reference numerals 34 and 35 shown in the figure are graphs of luminance values of the left-eye image and the right-eye image, respectively. The parallax is the shift amount by which the graph of the left-eye image is shifted to the graph of the right-eye image. FIG. 15 is a three-dimensional object 2 of FIGS. 14 to 13.
It is a graph which shows the example which took out the part of 1. The arrangement of the brightness values of the left-eye image of reference numeral 41 in the figure is treated as a sequence and is represented by bn, and the brightness value of the right-eye image of reference numeral 42 is also an.
Then, the correlation function of the following equation is calculated.

[0033] Here, n is the pixel number of the image, i is the shift amount of the pixel number, and the shift amount is an integer from 0 to S, and the shift amount is increased by 1 to calculate V (i). S depends on the parallax regarding the shortest distance set in advance as a detection range in the measuring device. w is a width for performing the correlation calculation, and a value is set depending on the purpose. For example, as shown in FIG. 15, in the reference numeral 42, the three-dimensional object 21 corresponds to the pixel numbers 1 to 12, so if w is 10, the best parallax can be obtained. However, when this method is actually applied, the position and size of the three-dimensional object on the image are often unknown.
Therefore, in general, the value of w that is empirically considered to be the best is adopted.

FIG. 16 is a diagram showing an example of the result of V (i). As shown in the figure, the reference numeral 51 has the smallest correlation value, and i at this time is the parallax. Further, when higher accuracy is required, a point calculated by interpolation calculation like reference numeral 52 is often used as the parallax (there are many interpolation methods). The above description is for the case of using the one-dimensional imaging device or the case of performing the correlation calculation for each row, but in the case of using the two-dimensional imaging device, it is generally not performed for each row. , There is a method of performing the correlation calculation in the area where a plurality of lines are collected.

FIG. 17 is a diagram for explaining the correlation calculation method of the two-dimensional image pickup device. As shown in this figure (a), the first of the methods is a method (projection method) in which the sum is calculated for each vertical column (projection), and the information corresponding to one row is obtained and the above calculation is performed.
Secondly, as shown in this figure (b), there is a method (area method) in which the above equation (2) is directly performed in two dimensions. For the projection method, the one-dimensional calculation described above may be used as it is, but for the area method, V (i) is calculated for each row in the horizontal direction, and the sum at the same i is obtained and evaluated. It Further, since the projection method compresses information in the horizontal direction, it is resistant to vertical displacement of the left and right screens, and the calculation time is short.

FIG. 18 is a diagram for explaining the calculation of the azimuth angle θk of the detected object. At the angle of view θ of the camera shown in FIG.
The resulting image is Wx dots in the horizontal direction.
It is expressed as shown in. When the object detection means 110 detects the object, the horizontal position P in the object distance and the image width.
x is obtained as shown in this figure (b), and thereby the azimuth angle θk of the detected object is obtained as Px × θ / Wx.
In addition, the azimuth angle θk is changed by the time change of the azimuth angle.
(T) can be obtained.

The stereoscopic method is an example, and the object detecting means 110 may be composed of a laser radar.

[0038]

As described above, according to the present invention, the relative distance, relative speed, and azimuth between an object in front of the host vehicle and the host vehicle are detected, and the speed signal and yaw angle of the host vehicle are detected. The amount of change is detected, and the running locus that is the temporal change in the position of the own vehicle is calculated from the changes in the own vehicle speed and yaw angle, and is calculated from the relative distance and the azimuth angle of the detected object at the position of the own vehicle. The running locus, which is the change over time in the position of the detected object, is calculated by adding up the detected loci of the detected object, and when the running locus of the host vehicle overlaps the running locus of the detected object, the detected object is detected. Since the travel locus of is determined as the travel path of the own vehicle, without using the white line indicating the end of the travel path,
It is possible to obtain the travel route of the host vehicle. Even if it is parallel to the running locus of the detected object, it can be determined as the running path of the own vehicle, and by changing the origin of the running locus according to the setting conditions, accumulated errors of the running locus over time can be suppressed and determined. It can be used to judge the running path of the own vehicle as an obstacle candidate of the detected object, and when the relative distance to the detected object judged as an obstacle candidate is less than a certain distance or a certain distance or more. When approaching at a speed, this detected object can be determined as an obstacle.

[Brief description of drawings]

FIG. 1 is a diagram showing an overall configuration of a vehicle travel path detection device according to an embodiment of the present invention.

FIG. 2 is a diagram showing a traveling locus map of the own vehicle obtained by a traveling locus calculation means 112 of the own vehicle of FIG.

FIG. 3 is a diagram showing an example of obtaining each traveling locus with respect to the movement of the detected object A by using the position of the own vehicle.

FIG. 4 is a diagram showing an example before the traveling loci of the own vehicle and the detected object overlap each other in the traveling road determination means 114 in FIG. 1, and an example in which the traveling road is not yet determined.

FIG. 5 is a schematic diagram showing an example in which the traveling path determination means 114 in FIG. 1 connects the traveling loci of the vehicle and the detected object, and the traveling path is not yet determined.

FIG. 6 is a diagram showing an example in which the travel paths of the host vehicle and the detected object A overlap each other for a certain period of time in the travel path determination means 114 of FIG. 1, and a travel path is determined.

7 is a diagram showing an example in which the own vehicle travels in parallel with the traveling locus of the detected object B for a certain period of time in the traveling path determination means 114 of FIG. 1, and in which the traveling path is determined.

FIG. 8 is a diagram showing an example of calculating the traveling loci of the own vehicle and the detected object A by using the position of the own vehicle at time t0 as the coordinate origin of the map of the traveling road locus.

FIG. 9 is a diagram illustrating an example in which the origin of the coordinate system of the map of the travel locus is changed to the current time by the travel locus calculation origin change instruction means 116 at constant time intervals.

FIG. 10 shows a traveling locus of the detected object A in the past fixed time period.
It is a figure explaining the example converted into the coordinate system of.

FIG. 11 is a diagram showing an example in which another detected object C is determined as an obstacle candidate by an obstacle candidate determination unit 117 in FIG. 1.

FIG. 12 is a diagram illustrating an example of calculating a distance to a three-dimensional object based on a stereoscopic method by cameras 210 and 211 of the object detection unit 110 of FIG. 1.

13 is a diagram showing an example of an image formed on the miscellaneous image elements 14 and 15 of FIG.

FIG. 14 is a reference numeral 22 and 23 of FIG. 13, respectively.
It is a graph which shows the example which extracted the brightness value of a line.

FIG. 15 is a graph showing an example in which a portion of the detection object 21 of FIGS. 14 to 13 is extracted.

FIG. 16 is a diagram showing an example of a result of V (i).

FIG. 17 is a diagram illustrating a correlation calculation method of the two-dimensional imaging device.

FIG. 18 is a diagram illustrating calculation of an azimuth angle θk of a detected object.

[Explanation of symbols]

 110 ... Object detection means 111 ... Own vehicle speed and yaw angle detection sensor 112 ... Traveling trajectory calculation means of own vehicle 113 ... Traveling trajectory calculation means of detected object 114 ... 117 ... Obstacle candidate determination unit 118 ... Obstacle detection determination means

Claims (4)

[Claims] 1. A vehicle traveling path detection device for detecting a traveling path of an own vehicle, comprising: a relative distance between an object in front of the own vehicle and the own vehicle;
Relative speed, object detection means for detecting the azimuth angle, own vehicle speed and yaw angle detection sensor for detecting a change amount of the speed signal and yaw angle of the own vehicle, by the change amount of the own vehicle speed and the yaw angle A traveling locus calculation means of the own vehicle for calculating a traveling locus which is a temporal change of the position of the own vehicle; and a relative distance of the detected object and a detected object calculated from an azimuth angle at the position of the own vehicle. A traveling locus calculation unit for the detected object that calculates the traveling locus that is the time change of the position of the detected object by adding the positions; and the traveling locus of the host vehicle is along the traveling locus of the detected object. And a travel route determining means for determining a travel trajectory of the detected object as a travel route of the host vehicle. 2. The traveling path determining means determines the traveling path of the host vehicle from the traveling trajectory in front of the detected object when the traveling trajectory of the vehicle is parallel to the traveling trajectory of the detected object. The vehicle travel path detection device according to claim 1, wherein 3. The vehicle traveling road detecting device according to claim 1, wherein the traveling locus calculation means of the own vehicle changes the origin of the traveling locus according to setting conditions. 4. An obstacle candidate determination means for determining an object to be detected running ahead of the determined traveling path of the own vehicle as an obstacle candidate, and an object to be detected determined as the obstacle candidate. When the relative distance between and becomes a certain distance or less, or when approaching at the relative speed of a certain amount or more, the obstacle determining means for determining the detected object as an obstacle is provided. Item 1. The vehicle traveling path detection device according to Item 1.