JPH0765294A - Preventive safety device for vehicle - Google Patents

Abstract

PURPOSE:To provide the preventive safety device which can evaluate, judge, and process the degree of danger in consideration of the size of an obstacle as to plural dangerous objects at the periphery of the vehicle CONSTITUTION:Obstacle detecting means 10 and 20 detect the distance and direction to an obstacle and the size of the obstacle, a data extracting means 40 extracts data corresponding to the obstacle according to the size of the obstacle, and a travel environment output means 50 generates a figure corresponding to the size of the extracted data on actual space coordinates corresponding to a travel road surface in front of the vehicle. The travel state of this vehicle, on the other hand, is detected by a travel state detecting means 60, a danger degree coefficient calculating means 70 sets a distribution of danger degree coefficients M on the actual space coordinates according to the travel state, and a danger degree output means 80 finds the degree C of danger from the figure corresponding to the size of the extracted data and the danger degree coefficients and outputs the degree C of danger with a two-dimensional distribution corresponding to the size.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a preventive safety device for a vehicle, in particular, for monitoring an area around the vehicle to detect an obstacle, and to determine the degree of danger of the obstacle based on the speed and the traveling direction of the vehicle. The present invention relates to a vehicle preventive safety device capable of informing a driver of an accident risk such as contact and collision.

[0002]

2. Description of the Related Art As a conventional device of this type, for example, there is a vehicle running control device disclosed in Japanese Patent Laid-Open No. 3-92436. FIG. 62 shows a block diagram of this traveling control device. In the figure, reference numeral 1 denotes a scanning laser radar device mounted on the own vehicle, which is an inter-vehicle distance to a preceding vehicle, a relative speed,
Also, the relative position indicating the deviation between the vehicle traveling ahead and the vehicle traveling is detected. Reference numeral 2 is a vehicle speed sensor for detecting the traveling speed of the own vehicle, 3 is a steering angle sensor for detecting the steering angle of the own vehicle, 4 is a risk degree calculation means, 5 is an appropriate inter-vehicle distance calculation means, and 6 is an appropriate vehicle speed operation amount control means. , 7 is a throttle actuator, 8
Is a brake actuator.

Next, the operation will be described. The inter-vehicle distance, the relative speed, the relative position, the own vehicle speed, and the steering angle detected by the laser radar device 1, the vehicle speed sensor 2, and the steering angle sensor 3 are sent to the risk degree calculating means 4, and the risk degree calculating means 4 detects each of them. By using fuzzy inference with the signal as a parameter, the risk level that matches the feeling of the driving vehicle is calculated as an index. The appropriate inter-vehicle distance calculating means 5 calculates an appropriate inter-vehicle distance between the preceding vehicle and the own vehicle by using the risk index, the relative speed, and the own vehicle speed.
The proper vehicle speed operation amount control means 6 calculates a correction amount by which a command should be sent to the throttle actuator 7 and the brake actuator 8 by fuzzy reasoning in order to make the proper vehicle speed based on the proper inter-vehicle distance.

[0004]

The conventional vehicle drive control device is constructed as described above, and considers only one vehicle traveling ahead as a dangerous object, and uses fuzzy reasoning for this vehicle. Calculated the degree of danger. Therefore, when a plurality of vehicles are driving in the vicinity at the same time, no consideration is given, and the risk cannot be evaluated in multiple directions at the same time. Further, in the above conventional device, the evaluation of the risk is
Although it is performed based on the distance and direction to the obstacle, and the relative speed to the own vehicle and the own vehicle speed, the degree of danger is not determined only by the distance and direction between the obstacle and the own vehicle, but the size and size of the obstacle, Depending on the moving direction of the obstacle, or whether the obstacle is a car, a two-wheeled vehicle, or a pedestrian, the degree of danger differs even if the distance is the same and the relative speed is the same. Further, although the degree of danger varies depending on the driving state of the driver, these have not been taken into consideration in the past.

The present invention has been made in order to solve the above-mentioned problems, and the size, size and type of obstacles and their future behavior with respect to a plurality of dangerous objects around the vehicle. Furthermore, it is an object of the present invention to provide a preventive safety device capable of evaluating, judging, and treating the degree of danger including the driving state of the driver.

[0006]

SUMMARY OF THE INVENTION A vehicle preventive safety device according to the present invention includes a relative distance and direction between an obstacle and an own vehicle in a peripheral traveling area mainly in front of the vehicle, and the obstacle. Obstacle detecting means for detecting the size of the object, data extracting means for extracting the extracted data corresponding to the obstacle degree of the obstacle from the output signal of the obstacle detecting means, the distance and the azimuth, and the front of the extracted data. Traveling environment output means for generating a graphic corresponding to the size of the extracted data on the real space coordinates corresponding to the traveling road surface, traveling state detecting means for detecting the traveling state of the own vehicle, and the actual traveling state according to the traveling state. Risk factor calculating means for setting the distribution of risk factors on spatial coordinates, and risk factor output that calculates the risk factor from the extracted data and the risk factor and outputs the risk factor in a two-dimensional distribution according to its size. means It includes those were.

The risk output means enlarges the graphic corresponding to the size of the extracted data according to the risk coefficient,
It may be configured such that the figure that is reduced or deformed and generated is output on the real space coordinates.

Alternatively, when the risk is calculated by the risk output means, the traveling environment output means generates a solid figure function corresponding to the size of the extracted data on the real space coordinates corresponding to the road surface ahead. The risk output means may cut the three-dimensional figure function according to the risk coefficient and output the two-dimensional distribution of the cut surface as the risk.

The running environment output means adds new prediction data to the extraction data corresponding to the size of the obstacle by taking into consideration the movement of the obstacle before the extraction data,
A graphic corresponding to the size of this new extracted data may be generated.

The data extracting means may output the extracted data corresponding to the obstacle degree of the obstacle according to the ratio of the height to the width of the obstacle.

Further, for each of the above vehicle preventive safety devices, a driving condition detecting means for detecting the driving condition of the driver is provided, and the risk factor calculating means determines the actual driving condition according to the driving condition of the own vehicle and the driving condition. It may be configured to set the distribution of the risk factors on the spatial coordinates.

Further, for each of the above vehicle preventive safety devices,
On the real space coordinates corresponding to the road surface, the warning judgment area is determined by the distance from the own vehicle, and when the warning judgment area and the graphic corresponding to the degree of danger at least partially overlap, a warning signal is given. Alarm output means for outputting may be provided.

Further, for each of the above vehicle preventive safety devices,
Equipped with a driving state detection means for detecting the driving state of the driver,
On the real space coordinates corresponding to the traveling road surface, the warning judgment area is determined by the distance from the own vehicle and the driving state,
An alarm output means may be provided for outputting an alarm signal when the alarm determination area and the graphic corresponding to the degree of risk at least partially overlap.

Further, in the two-dimensional distribution of the risk levels output from the risk level output means, the total amount of the risk levels with respect to the azimuth from the own vehicle is calculated, and an alarm output means for outputting the distribution of the risk levels is provided. Good.

[0015]

In the vehicle preventive safety device of the present invention, the traveling environment output means displays the degree of obstacle corresponding to the size of the obstacle as the extracted data on the real space coordinates corresponding to the road surface ahead. Furthermore, the distribution of the risk factors on the real space coordinates is set according to the running state of the own vehicle, and the two-dimensional distribution of the risk factors can be obtained from the extracted data and the risk factors. Even if you are doing it, you can evaluate the risk in multiple directions at the same time. In addition, since the size of obstacles is also taken into consideration in the risk assessment,
It will be safer.

When calculating the risk level, the graphic corresponding to the size of the extracted data is enlarged, reduced, or transformed according to the risk coefficient, and the generated graphic is output on the real space coordinates. By doing so, the risk calculation process becomes easy.

Further, when determining the degree of risk, the traveling environment output means generates a three-dimensional figure function corresponding to the size of the extracted data on the real space coordinates corresponding to the road surface ahead, and the three-dimensional figure is generated. Even if the function is cut according to the risk factor and the two-dimensional distribution of the risk is output depending on the size of the cut surface, the risk calculation process becomes easy.

It is also possible to add prediction data in consideration of the movement of the obstacle before that to the extracted data corresponding to the size of the obstacle, obtain new extracted data, and similarly determine the degree of danger. For example, it is possible to evaluate, judge, and process the degree of danger, including future behavior of obstacles, and obtain a safer preventive safety device.

Further, if the extracted data corresponding to the obstacle degree of the obstacle is output according to the ratio of the height to the width of the obstacle, not only a vehicle as an obstacle but also a two-wheeled vehicle or a pedestrian can run. Appropriate evaluation and judgment can be performed as the above dangerous goods, and a safer preventive safety device can be obtained.

Further, a safer preventive safety device can be obtained by detecting the driving condition of the driver and setting the distribution of the risk factor including the driving condition for each of the above vehicle preventive safety devices.

Further, for each of the above vehicle preventive safety devices,
On the real space coordinates corresponding to the road surface, the warning judgment area is determined by the distance from the own vehicle, and when the warning judgment area and the graphic corresponding to the degree of danger at least partially overlap, a warning signal is given. If a warning output means for outputting is provided, the driver can be notified of the danger quickly.

Further, if the driving condition of the driver is detected and the warning judgment area is determined also including this driving condition,
The driver can be notified of the danger sooner, and a safer preventive safety device can be obtained.

Further, in the two-dimensional distribution of the risk levels output from the risk level output means, the total amount of the risk levels with respect to the azimuth from the own vehicle is obtained, and the distribution of the risk levels is output to the driver. It is possible to clearly indicate the high-frequency direction, and it becomes easy to avoid danger and guide the vehicle in a safe direction.

[0024]

【Example】

Example 1. FIG. 1 is a block diagram showing a vehicle preventive safety device according to an embodiment of the present invention. In FIG. 1, 1
Reference numeral 0 denotes a laser radar device as a first obstacle detecting means, which irradiates a pulsed laser beam forward at a predetermined time interval, and if there is a laser beam that reflects this laser beam forward, reflects the laser beam. The light is received, and the distance to the reflecting object (obstacle) is detected by measuring the reflection time. As shown in FIG. 2, four laser radars of this type are installed at the front of the vehicle (P _{1 to} P ₄ ), the front of the vehicle is divided into four areas, and the laser radar light existing in each area is divided into four areas. The distance to the obstacle detected as a reflecting object is output from each laser radar (for example, laser radars P ₁ to r _B).
However, the laser radar P ₃ outputs r _C ). Reference numeral 20 denotes an image sensor as a second obstacle detecting means, which is the entire front area of the vehicle (area including all four areas in FIG. 2).
A sensor for inputting as image information, such as a commercially available C
It is composed of a CD camera and the like. For example, in FIG. 2, it is installed at a position P ₅ immediately in front of the driver's seat of the vehicle. Reference numeral 30 denotes an image processing area limiting means, which is the first obstacle detecting means 1
Signal of direction θ output from 0 (In this case, since the laser radar is installed for each area, the output from the laser radar installed at P ₁ corresponds to θ ₁ and the output from the laser radar at P ₂ Corresponds to the signal of the azimuth θ representative of the above-mentioned divided area such as θ ₂ ) and is output from the second obstacle detection means 20 in the forward traveling area 2 of the vehicle.
The limited two-dimensional image data is sent to the image data extraction means 40 by limiting the image area including the direction in which the obstacle is detected on the three-dimensional image. The image data extraction means 40 extracts an image area estimated to have an obstacle from the limited two-dimensional image data, calculates a data size (hereinafter abbreviated as S) corresponding to the size, and calculates the data size. Output the data. This output data may be analog or digital. The image processing area limiting means 30 and the image data extracting means 40 are composed of electronic circuits for processing image data, in which analog and digital are mixed. Reference numeral 50 denotes a traveling environment diagram creating means as a first traveling environment output means. From the first obstacle detecting means 10, data of a distance r to the obstacle and a direction θ representative of an area where the obstacle exists. In response to the received data, the image data extracting means 40 extracts the extracted data S corresponding to the size of the obstacle and the direction θ in which the obstacle exists.
The data corresponding to the size of the extracted data S is generated at the position of the distance r and the azimuth θ on the real space coordinates corresponding to the front road surface. First traveling environment output means 50
Is composed of a microcomputer, an analog or digital input / output electronic circuit, an image data processing circuit, a memory circuit, an image display circuit and the like. Reference numeral 60 denotes a vehicle motion measuring means as a traveling state detecting means. For example, as shown in FIG.
The traveling direction θ _v is detected by the image sensor 20 and the image processing device 62 as the obstacle detection means, or the yaw rate ψ is detected by the yaw rate sensor 63. Other than this, the vehicle motion measuring means may measure lateral acceleration, longitudinal acceleration, steering reaction force, tire lateral force, or the like. Reference numeral 70 denotes, for example, the velocity v and the traveling direction θ _v of the own vehicle from the vehicle motion measuring means 60, and the risk factor M for the velocity and the traveling direction is on the real space coordinates (xy coordinates) corresponding to the road surface ahead. It is a risk factor calculation means for setting the distribution state in. Reference numeral 80 denotes a risk level output means, which converts a graphic corresponding to the size of the extracted data S generated by the first traveling environment output means 50 into a risk level coefficient M (polar coordinates) at the position coordinates of the extracted data S. ) Is enlarged, reduced, or deformed in accordance with), and the generated figure is output as a figure having a degree of risk C at a position of distance r and azimuth θ on the real space coordinates corresponding to the road surface ahead. .

The operation of the above configuration will be described below. FIG. 4 is a flowchart showing the operation of the vehicle preventive safety device according to one embodiment of the present invention. In FIG. 4, step S1 is a step of calculating the extracted data S, and
In step 2, a driving environment diagram is generated. In step S3, the distribution of risk factors is calculated, and in step S4, the distribution of risk factors is calculated. The operation of step S1 will be described with reference to FIG. FIG. 2 is a view of a vehicle A equipped with the vehicle preventive safety device having the configuration of FIG. 1 and a front traveling area thereof as seen from above. The laser radar device in the front part P _{1 of the} vehicle A is a region I in the diagonally left front, the laser radar device in P ₂ is a region II in the front left side, and the laser radar device in P ₃ is the region in the front right side. The laser radar device at the portion III and P ₄ covers the portion IV in the diagonally right front. These four laser radar devices constitute the first obstacle detection means 10 of FIG. Now, assuming that the vehicle B has entered the area I, the laser radar device at P ₁ detects this, measures the relative distance from the vehicle A to the vehicle _B, and outputs distance data r _B. This is 1
Data of the distance data r _B and the representative azimuth data θ ₁ of the area I from the obstacle detection means 10 and the representative azimuth data θ ₁ of the image processing area limiting means 3 to the first traveling environment output means 50.
Sent to 0. On the other hand, an example of a two-dimensional image of the traveling area in front of the vehicle A input by the second obstacle detection means 20 is shown in FIG. Image processing area limiting means 30
Then, with respect to the image data of FIG. 5, the representative azimuth data θ ₁ , θ ₂ , θ ₃ , θ ₄ of the area where the obstacle exists in the output signal of the first obstacle detection means 10 is received, As shown in 6, the two-dimensional image data is limited only to the portion in which the enclosing frames R ₁ and R ₂ are inserted, and the data is output to the image data extracting means 40. At this time, in order not to lose the correspondence between the image data of the limited area and the azimuths θ _{1 to} θ ₄ ,
It is sent to the first traveling environment output means 50 together with the signal S output from the image data extraction means 40. The image data extraction means 40 estimates the obstacle as shown in FIG. 7 by the known image processing (basic image processing such as edge extraction and line combination) on the two-dimensional image of only the surrounding frame of FIG. Extract a two-dimensional image of an object. From this image, the size index S is calculated, for example, by the following formula.

[0026]

[Equation 1]

Next, the operation of step S2 will be described. That is, the signals (extracted data) S ₁ , S ₂ , and S ₃ indicating the size of the obstacle are output from the image data extraction unit 40 and the relative distances to the respective obstacles from the first obstacle detection unit 10. Signal indicating azimuth (r ₁ , θ ₁ ) (r ₂ , θ ₂ ) (r ₃ ,
When θ ₃ ) is input to the first traveling environment output means 50, a traveling environment diagram as shown in FIGS. 8A and 8B is created according to these signals. FIG. 8A shows a vehicle A.
In a running environment diagram in which a circle having a radius proportional to the calculation amount S calculated by the image data extracting means 40 is made to correspond to an obstacle on the real space coordinates indicating the traveling road surface in front of is there. FIG. 8B shows the image data extracting means 4 on the real space coordinates indicating the road surface ahead of the vehicle A.
3 is a traveling environment diagram in which a three-dimensional figure corresponding to the calculation amount S calculated by 0 is associated with an obstacle and its position is also displayed. The traveling environment diagram thus obtained represents the degree of obstacle on the real space coordinates corresponding to the road surface ahead.

Next, in step S3, the distribution of risk factors is calculated. First, the vehicle motion measuring means 60 measures the speed v and the traveling direction θ _v of the host vehicle. To measure the traveling direction θ _v , as shown in FIGS. 9 and 10, the output signal from the second obstacle detection means 20 is input in step S11, and this output signal is input to the image processing device 82 in step S12. By processing, the road boundary line is first extracted. Next in step S13
Then, the boundary lines are extrapolated from these road boundary lines to obtain intersection points of the boundary lines, which are set as traveling direction reference points. In step S14, θ _{v based on} the installation position of the second obstacle detection means 20
The distance from the reference line of = 0 to the traveling direction reference point is obtained, and the traveling direction θ _v is calculated by this distance in step S15. According to θ _v and v obtained in this way,
The risk factor calculating means 70 sets the distribution state of the risk factor M on the real space coordinates (xy coordinates) corresponding to the road surface ahead. FIG. 11 is a flow chart for explaining a method of calculating the distribution of the risk factor M, and FIG. 12 shows an example of the distribution of the risk factor M in the real space coordinates (xy coordinates) corresponding to the road surface ahead. FIG. In FIG. 11, in step S21, a vehicle speed v representing the running state of the vehicle,
The traveling direction θ _v is input, and risk coefficient parameters ψ _x and ψ _y are calculated in step S22. In step S23, the distribution of the risk factor M on the xy coordinates is determined. [Psi _x in step S22, the [psi _y, for example, _{ψ x = K Mx θ v,} ψ y
= K _My / v, x of the risk factor M in step S23
The distribution on the y coordinate is M (x, y) = 1−ψ _y · y + ψ _x · x .. (2), the spatial distribution is as shown in FIG. Figure 1
In FIG. 2 (a), the hatched plane shows the spatial distribution of the risk factor M, and FIG. 12 (b) shows the spatial distribution with contour lines. The risk factor M is M = 1 for all directions on the xy coordinates when the vehicle is moving straight at an extremely high speed, that is, when v is infinite and θ _v is 0. That is, the spatial distribution of the risk factor M is a plane parallel to the xy plane and M = 1. Then, as the speed becomes slower, this surface inclines in the y direction (inclination angle ψ _{y in the} y direction). Further, as the traveling direction θ _v deviates from the straight traveling direction, the surface inclines in the x direction (inclination angle ψ _{x in the} x direction). Therefore, when the velocity is v and the traveling direction is θ _v , the plane showing the spatial distribution of the risk factor M is ψ _{y in} the y direction and ψ _{y in} the x direction.
It becomes a plane inclined by _x as shown by the diagonal lines in FIG.

Next, the risk C in step S4 of FIG.
A method of calculating the distribution of will be described. FIG. 13 is an explanatory diagram showing an algorithm for calculating the risk level. In step S31, the position coordinates (r _n , of the obstacle level S _n (each obstacle is shown by a serial number n)).
Enter θ _n ). In step S32, the risk factor M (r _n , θ _n ) corresponding to the position of the fault level S _n is calculated.
Here, the xy coordinates are converted into polar coordinates, and M (r _n ,
θ _n ) is calculated. In step S33, the obstacle degree S _n is transformed according to the value of the risk degree coefficient M to determine the risk degree C _n , and in step S34, the graphic corresponding to the risk degree C _n corresponds to the actual road surface ahead. It is output at the position of distance r and azimuth θ on the space coordinates. For example, the risk level C _n is determined by multiplying the failure level S _n by M (r _n , θ _n ), as shown in FIG. 14, and is similar to the figure representing the failure level S _n on the real space coordinates. It is reduced or expanded and is represented by a circle whose area is proportional to M (r _n , θ _n ). Alternatively, as shown in FIG. 15, it is represented by a solid whose volume is proportional to M (r _n , θ _n ), which is reduced or enlarged in a shape similar to the solid figure showing the obstacle degree S. Alternatively, as shown in FIG. 16, when the height of the three-dimensional figure representing the obstacle degree S is h, this three-dimensional figure is represented by a three-dimensional figure restricted by the height h · M (r _n , θ _n ). Alternatively, as shown in FIG. 17, the height of the three-dimensional figure representing the obstacle degree S is set to h, and the height of this three-dimensional figure is M (r _n ,
It is represented by a solid figure restricted by h · M according to the distribution of θ _n ).

Example 2. FIG. 18 shows another example of the distribution of the risk factor M in the above embodiment. The curved surface of the gable roof style indicated by the diagonal lines in FIG. 18A is the spatial distribution of the risk factor M, and the risk factor M on the real space coordinates (xy coordinates) is shown.
Is represented by M (x, y) = 1−ψ _x | (y−x / θ _v ) | (3). In addition, ψ _x is a coefficient that is constant or inversely proportional to v, and indicates the inclination of the roof. The ridgeline direction of the gable roof is the traveling direction θ _v . Furthermore, the risk factor M is always 1 at the ridgeline of the gable roof. FIG.
(B) represents the spatial distribution using contour lines.

Example 3. In the second embodiment, a gable roof-like curved surface is assumed as the spatial distribution of the risk factor M, but even a semicircular arc shape as shown in FIG. 19 (a) is as shown in FIG. 19 (b). It may have a Gaussian curved shape. In these things, the direction of the ridge line is the traveling direction and the height is always 1 as in the second embodiment.

Example 4. FIG. 20 shows a spatial distribution in which the ridge line is further tilted in the y direction with respect to that of the second embodiment (tilt angle ψ _{y in the} y direction). The curved surface of the gable roof style shown by the diagonal lines in FIG. 20A has a spatial distribution of the risk factor M, and the risk factor M on the real space coordinates (xy coordinates) is M (x, y) = 1. −ψ _x | (y − x / θ _v ) | − ψ _y · y (4) In addition, ψ _x is a coefficient that is constant or inversely proportional to v, and indicates the inclination of the roof. Further, ψ _y is a coefficient inversely proportional to v, and the ridgeline of the gable roof is inclined by ψ _{y in the} y direction. Further, the ridge direction of the gable roof is the traveling direction θ _v . FIG. 20B shows the above spatial distribution using contour lines.

Example 5. FIG. 21 shows still another example of the distribution of the risk factor M in the real space coordinates (xy coordinates) corresponding to the road surface ahead. The gabled roof curved surface shown by the diagonal lines in FIG. 21A has a spatial distribution of the risk factor M, and the risk factor M on the real space coordinates (xy coordinates) is expressed by the following equation.

[0034]

[Equation 2]

Ψ _x is a coefficient that is constant or inversely proportional to v, and indicates the inclination of the roof. Further, the ridgeline of the gabled roof coincides with the predicted route L of the vehicle. Furthermore, the risk factor M is always 1 at the ridgeline of the gable roof. Figure 21
(B) represents the spatial distribution using contour lines. Further, FIG. 22 shows a method for obtaining the predicted course of the vehicle, in which the vehicle motion measuring means 60 shown in FIG. 1 measures the vehicle speed v, the traveling direction θ _v , and the yaw rate ψ. It is calculated using these values. That is, in FIG. 22, the predicted path y _{p at} the coordinate x is represented by the following equation,

[0036]

[Equation 3]

[0037] Using this formula, the formula of the above-described risk factor M is obtained. That is, step S4 in FIG.
In 1, the vehicle speed v representing the traveling state of the vehicle and the traveling direction θ _v yaw ψ are input, and in step S42, the predicted course y _p
Is calculated from the equation (6). In step 43, the risk factor parameters ψ _x and ψ _y are calculated or referred to, and step S4
In 4, the distribution of the risk factor M on the xy coordinates is determined by the equation (5).

Example 6. FIG. 24 shows a spatial distribution in which the ridgeline is further tilted in the y direction with respect to that of Example 5 (tilt angle ψ _{y in the} y direction). Real space coordinates (xy coordinates)
The risk factor M above is

[0039]

[Equation 4]

It is represented by In addition, ψ _x is a coefficient that is constant or inversely proportional to v, and indicates the inclination of the roof. Further, ψ _y is a coefficient inversely proportional to v, and the ridgeline of the gable roof is inclined by ψ _{y in the} y direction. Further, the ridge direction of the gabled roof coincides with the predicted course of the vehicle. Note that FIG.
The above spatial distribution is shown in (b) using contour lines.

Example 7. FIG. 25 is an explanatory diagram for explaining still another example of the distribution of the risk factor M in the real space coordinates (xy coordinates) corresponding to the road surface ahead. FIG. 25 (a) shows the road surface ahead of the vehicle, and the shaded area R shows the movable area of the vehicle. The center direction of the movable area faces the traveling direction θ _{v of the} vehicle, and the size of the area, that is, the spread angle, becomes narrower as the vehicle speed increases and becomes wider as the vehicle speed decreases. FIG. 25 (b) shows the distribution of the risk factor M according to the present embodiment, which has a gable roof-like curved surface similar to that of the second embodiment, but in the case of the present embodiment, the ridgeline of the roof can be moved as described above. It is a flat surface that matches the area. FIG. 25 (c) shows the above spatial distribution using contour lines.

Example 8. Next, a vehicle preventive safety device according to another embodiment of the present invention will be described. The configuration of the device is the same as that in FIG. 1, but in this embodiment, the spatial distribution of the risk factor W is the reverse distribution of the risk factor M shown in the above-mentioned embodiments, and the degree of failure is Is represented by a solid figure function, and the degree of risk is output by the cut surface of the solid figure. FIG. 26 shows the operation of the risk factor calculating means 70. In step S51, the vehicle speed v indicating the traveling state of the vehicle and the traveling direction θ
_v is input, and risk coefficient parameters ψ _x and ψ _y are calculated in step S52. In step S53, the distribution of the risk factor W on the xy coordinates is determined by W (x, y) = 1-M (x, y) (8). Ψ _x and ψ _y in step S52 are set to, for example, ψ _x = K _Mx θ _v and ψ _y = K _My / v, and step S5
The distribution of M at xy coordinates in 3 is M (x, y) = 1-ψ _y · y + ψ _x · x .. (2), the spatial distribution of the risk factor W is as shown in FIG. 27, a spatial distribution of planes risk factor W shown by oblique lines, W (x, y) = 1-M (x, y) = The _{ψ y · y-ψ x ·} x, {1- If M (x, y)} <0, then W (x, y) = 0 (9).

FIG. 28 is an explanatory diagram showing an algorithm for calculating the risk from the spatial distribution of the risk coefficient W thus obtained. In step S61, the obstacle S _n (each obstacle is assigned a serial number n). Input the position coordinates (r _n , θ _n ) of (shown). In the present embodiment, the degree of obstacle is represented by a solid figure function. In step S62, the risk factor W (r _n , θ _n ) corresponding to the position of the fault level S _n is calculated. In addition,
Here, the xy coordinates are converted to polar coordinates, and W (r _n , θ _n )
To calculate. In step S63, the three-dimensional figure function having the obstacle degree S _n is cut according to the value of the risk coefficient W (for example, when the height of the three-dimensional figure representing the obstacle degree S is h, the height of the three-dimensional figure is h. W (r _n , θ _n ) is cut), and the risk level C _n is determined by the cut surface. In step S64, the graphic corresponding to the degree of risk C _n , that is, the cut surface, is output at the position of the distance r and the azimuth θ on the real space coordinates corresponding to the road surface ahead.

Example 9. FIG. 29 shows another example of the distribution of the risk factor W in the above embodiment. The curved surface shown by the diagonal lines in FIG. 29 is the spatial distribution of the risk factor W, and M (x, y)
The same M as M in Example 2 (x, y) = 1 -ψ x | - | represented by ··· (3) (y x / θ v). Therefore W (x, y) is W (x, y) = ψ x | (y - x / θ v) | a (10).

FIG. 30 illustrates the process of calculating the risk from the spatial distribution of the risk coefficient W as described above. The risk coefficient W is calculated from the position coordinates of the fault S ₁ , and the risk coefficient W is calculated. The solid figure function having the obstacle degree S ₁ is cut according to the value of (the height of the solid figure showing the obstacle degree S is h), and the risk C ₁ is determined by the cut surface. A graphic corresponding to the risk C ₁ , that is, a cut surface, is output on the real space coordinates corresponding to the road surface ahead. In addition, as a method of determining the cutting plane, as shown by the hatched portion in FIG. 30 and the thick solid line in FIG. 31, the intersection of the center line of the obstacle degree S ₁ and the spatial distribution curved surface of the risk factor W is set in the xy plane. There are a method of cutting in parallel and a method of cutting the solid figure function having the obstacle degree S ₁ along the spatial distribution curved surface of the risk factor W as shown by the broken line in FIG.

FIG. 32 shows that, for a plurality of failure levels S ₁ and S ₂ ,
A display example when the degree of danger is calculated according to this embodiment will be described. An obstacle of the same size exists symmetrically in front of the traveling direction of the host vehicle, the obstacle indicated by the obstacle degree S ₁ is in the traveling direction of the own vehicle, and the obstacle indicated by the obstacle degree S ₂ is the own vehicle. 32A, the risk degree of the obstacle indicated by the obstacle degree S ₁ is C ₁ as shown in FIG. 32A, and the danger degree of the obstacle indicated by the obstacle degree S ₂ is C ₂ Therefore, the degree of danger is C ₁ > C _2, and the degree of danger is calculated with an emphasis on the traveling direction. FIG. 32 (b) shows an output example of the risk level output means, where S ₁ and S ₂ are obstacle levels, and shaded areas C ₁ and C ₂ are risk levels.

The method of calculating the risk factor W is described in the third embodiment.
Alternatively, it may be for the value of M shown in the seventh embodiment.

Further, the solid figure function representing the obstacle degree may be other than the conical shape function described in the above embodiment.

Example 10. FIG. 33 is a block diagram showing a vehicle preventive safety device according to still another embodiment of the present invention. In the present embodiment, as the degree of failure S, the degree of risk is evaluated, judged, and processed by obtaining the degree of failure in consideration of the future behavior of the obstacle. In the figure, 10 to 80 are the same as those shown in FIG. The first traveling environment output means 50 is
The data of the distance r from the first obstacle detection means 10 to the obstacle and the azimuth θ representative of the area where the obstacle exists are received,
A signal of the data S corresponding to the size of the obstacle is received from the image data extracting means 40, and the size of the extracted data S is set at the position of the distance r and the azimuth θ on the real space coordinates corresponding to the road surface ahead. A corresponding figure is generated and stored with its position data. Reference numeral 90 is a predicting means for calculating the prediction data S _D corresponding to the predicted motion amount from the previous position data stored for the same obstacle in consideration of the movement before the obstacle, and 100 is an extraction unit. The second traveling environment output means for adding the above-mentioned prediction data S _D to the data S to generate a graphic corresponding to the size of the new extraction data S _* on the real space coordinates corresponding to the road surface ahead. is there.

Next, the operation will be described. The operation until the first traveling environment output means 50 determines the extracted data S is the same as that shown in FIG. The first traveling environment output means 50 stores the extracted data S and the position data, and the predicting means 60 determines the predicted data S _D based on the position data. FIG. 34 is an explanatory diagram for explaining how to obtain the prediction data S _D corresponding to the predicted motion amount. FIG. 35 is a flowchart showing the operations of the first traveling environment output means 50 and the prediction means 90. FIG. 34 (a) shows the first
34 (b), the position of the obstacle and the extracted data S (t ₁ ) at time t = t ₁ obtained by the running environment output means 50 of FIG.
Similarly, indicates the position of the obstacle and the extracted data S (t ₂ ) at time t = t ₁ + Δt = t ₂ . In FIG. 35, in step S71, the position vector r (t ₁ ) of the obstacle at time t = t ₁ is stored. Here, the position vector r (t) with the vehicle front center as the origin is the coordinates r (t), θ
Specified by (t). Next, in step S72, the position vector r (t ₂ ) of the same obstacle at time t ₂ is obtained. In step S73, the difference vector between the position vector r (t ₂ ) and the position vector r (t ₁ ) is calculated. In step S74, the predicted motion vector d of the obstacle at time t ₂
(T ₂ ) is calculated by the following equation. Here, k is a constant.

[0051]

[Equation 5]

Next, in step S75, the prediction data S _D corresponding to the predicted motion amount is obtained from the following equation.

[0053]

[Equation 6]

Thereafter, the same operation is repeated for each time Δt, and the prediction data S _D corresponding to the prediction motion vector d and the prediction motion amount is obtained at each time.

Next, in the second traveling environment output means 100,
Time t ₂ stored in the first traveling environment output means 50
The extraction data S (t ₂₎ in, added to prediction data S _D obtained by the predicting means 90, a new extraction at time t ₂ data S _* of (t ₂₎ a picture corresponding to the size, front It is generated at the position of the obstacle at the time t ₂ on the real space coordinates corresponding to the traveling road surface. FIG. 36 is an explanatory diagram showing an example of the prediction data S _D around the circle (for example, the diameter S or the area S) corresponding to the extracted data S obtained by the first traveling environment output means 50. An annular circle having a corresponding size (diameter S or area S) is formed, and a figure corresponding to the size of the new extracted data S _* (S _* = S + S _D ) is formed. By doing so, even obstacles of the same size are displayed larger as they move faster, and the degree of obstacle can be more easily determined.

37 and 38 show another example of the display on the second traveling environment output means 70. In FIG. 37, a circle having a size corresponding to the prediction data S _D is created in the moving direction of the obstacle, that is, at the position indicated by the prediction motion vector d, and these circles are connected to correspond to the new extraction data S _* . FIG. 38 shows that the solid figure function corresponding to the size of the new extracted data S _* is the distance r.
Is an example displayed in a bird's-eye view format at a position indicated by the direction &thetas;.

The new extracted data S thus obtained
A three-dimensional figure function corresponding to the size of _* , that is, a figure showing the degree of obstacle is enlarged, reduced, or modified according to the risk factor M as in the first to seventh embodiments, or the eighth or the eighth embodiment is carried out. If the risk C is obtained by cutting according to the risk factor W as in Example 9, the risk including the future behavior of the obstacle can be evaluated, judged, and processed, and a safer preventive safety device can be obtained.

Example 11. 39 illustrates another example of the image data extraction means 40 in FIG. 1. In this embodiment, an index (extraction) of the size of an obstacle is extracted from the two-dimensional image output from the image processing area limiting means 30. When calculating the data S, the extracted data is determined by different calculation formulas according to the ratio of the height to the width of the obstacle. The other operations are the same as those shown in FIG.

The operation of the image data extraction means 40 will be described with reference to FIG. As shown in the figure, the width of the obstacle 1 is x ₁ ,
When it is observed that the height is z ₁ , the width of the obstacle 2 is x ₂ , and the height is z ₂ , the extracted data S ₁ and S ₂ are conventionally used by using these x and z.
When the value is calculated, the value is uniformly determined by using the formula (1), but in the present embodiment, _first , the ratio ε (ε _{1 of} x ₁ and z ₁ and x ₂ and z ₂ is set.
= Z ₁ / x ₁ and ε ₂ = z ₂ / x ₂ ) respectively, and the extraction coefficient S is multiplied by each of the extraction data S obtained by the mathematical formula (1) according to the value of the ratio ε, and a new extraction is performed. Output as data S _* . For example, when ε is ε <1, S _* = S, that is, when the obstruction is horizontally long, the extracted data S _* uses the value S obtained by the above equation (1) as it is. When 1 <ε <ε _a , S _* = a × S That is, in the range of ε where ε _a is the upper limit value and the width-height ratio corresponds to a single vehicle, the extracted data S _* is expressed by the above formula (1). The value S obtained in step 3 is multiplied by the expansion coefficient a (for example, a = 2). When ε _a <ε <ε _b , S _* = b × S (where a <b) That is, ε _a is the lower limit value and ε _b is the upper limit value, and the ratio of width to height is ε corresponding to a person. In the range of, the extraction data S _* has the expansion coefficient b (for example, b =
Apply 4). Extracted data S _* obtained in this way
Using the extracted data S _{* for} the first traveling environment output means 50 _.
A figure corresponding to the size of is formed. As a result, even a small obstacle such as a motorcycle or a person, which has a high degree of danger, has a clearer degree of obstacle, and the degree of danger is also clearly displayed, so that a safer preventive safety device can be obtained.

Example 12 In each of the above-described embodiments, the first obstacle detection is performed in which the traveling area is divided into a plurality of areas, and the relative distance r between the obstacle and the vehicle and the azimuth θ representing the divided area are detected for each area. Means and a second obstacle detecting means for detecting the front traveling area as a two-dimensional image, the obstacle is detected, and 2 based on the output signal of the first obstacle detecting means.
Although the image area on the three-dimensional image is limited and the extracted data S corresponding to the size of the obstacle is output from the limited image data, the image processing area limiting means 3 as described above is used.
There may be no 0, and either the first obstacle detecting means or the second obstacle detecting means may be used, and each obstacle detecting means is not limited to the above. Instead, it may be any detection means that can detect the position data and the data corresponding to the size of the obstacle.

Example 13. FIG. 40 is a block diagram showing a vehicle preventive safety device according to still another embodiment of the present invention. In this embodiment, the driving state of the driver is detected, and the distribution of the risk factor is set including the driving situation. Figure 4
At 0, 110 is a driver monitoring means as a driving condition detecting means, which is configured as shown in FIG. 41, for example, and detects the driver's line-of-sight direction θ _w and the driver's consciousness state D _K. Others are the same as those in FIG.

Next, the method of calculating the distribution of the risk factor M in this embodiment will be described with reference to the flowchart of FIG. In FIG. 41, in step S81, the vehicle speed v and the traveling direction θ _v that represent the traveling state of the vehicle are input, and step S82 is entered.
Then, from the driver monitoring means 110, the driver's line-of-sight direction θ _w
And the driver's consciousness state D _K. Step S8
In step 3, risk factor parameters ψ _x and ψ _y are calculated, and in step S84, the distribution of the risk factor M on the xy coordinates is determined. The step S82 will be described in more detail. FIG. 42
1 is a configuration diagram showing the configuration of the driver monitoring device 110,
Reference numeral 11 is a driver, 112 is a driver photographing means, 113 is a line-of-sight direction detection circuit, and 114 is a driver state determination circuit. Further, FIG. 43 is a flowchart for explaining the operation of the driver monitoring device 110. In the figure, in step S91, the image of the driver is input by the driver photographing means 112.
From the image obtained in step S92, for example, facial feature points such as the inner canthus and both ends of the lips of both eyes and the eyeball region are extracted,
In step S93, the face direction is determined from the shape of the feature point, and the direction of the eyeball is determined using reflected light from the eyeball. In step S94, the line-of-sight direction θ _{w is} calculated from the face direction and the eyeball direction.
Determines, watching time change in step S95 in viewing direction, the viewing direction and the average value, and calculates the eye movement velocity, step S96 in the above eye movement velocity of the driver's state of consciousness D _K of FIG. 44 for example Determine using a table. Figure 44
When the gaze movement speed is particularly low, the driver's consciousness level D _K is reduced, and when the gaze movement speed is particularly high, the driver's consciousness level is confused. Take a large D _K. In step S97, the determined driver's gaze direction θ _w and the driver's consciousness state D _K are output.

Using the driver's line-of-sight direction θ _w , the driver's consciousness D _K , the vehicle speed v, and the traveling direction θ _v thus obtained, at step S73, the risk factor parameter ψ _x , Calculate ψ _y . FIG. 46 shows an example of calculating the risk factor parameters ψ _x and ψ _y . Note that this parameter is a parameter for determining the spatial distribution of the risk factor as shown in FIG. The distribution of the risk factor M on the xy coordinates is represented by the same distribution as that of the fourth embodiment as shown in FIG. 45, for example. FIG. 46 (a) shows a risk factor parameter ψ _y . For example, ψ _y is set so as to increase the risk degree to a distant place, considering that the amount of inattentiveness increases as the line-of-sight direction deviates from the traveling direction. It is expressed as a function of θ _w −θ _v |. Further, FIG. 46B shows the risk factor parameter ψ _x, and the lower the consciousness level is (D
_K small), when the danger level is high in a wide range and the consciousness level is confused (D _{K is} large), the risk level is increased only in the traveling direction, and ψ _x is set to avoid the confusion caused by surrounding information. The function is proportional to D _K. Then step S
In 74, the parameters ψ _x and ψ calculated in this way are
_{Based on y} , the spatial distribution of the risk factor is determined by the following equation, for example. M (x, y) = 1 -ψ x | (y - x / θ v) | - ψ y · y ··· (4)

Example 14 Although the distribution of the risk factor M is the same as that in the fourth embodiment in the above embodiment, the same distribution as in the other embodiments may be used. FIG. 47 shows Example 8.
And using Example 9, W (x, y) = 1-M
It is a flowchart when the risk factor W of (x, y) is used. As in FIG. 41, in step S91, the vehicle speed v and the traveling direction θ _v representing the traveling state of the vehicle are input,
In step S92, the driver's gaze direction θ _w and the driver's consciousness state D _K are input from the driver monitoring means 110. In step S93, the risk factor parameters ψ _x , ψ
_y is calculated, and in step S94, the distribution of the risk factor W on the xy coordinates is determined.

Example 15. FIG. 48 is a block diagram showing a vehicle preventive safety device according to still another embodiment of the present invention. In FIG. 48, 120 is an alarm output means, which determines an alarm determination area based on the distance from the own vehicle with respect to the two-dimensional distribution of the risk output from the risk output means 80. An alarm signal is output when the figure corresponding to the magnitude of the degree at least partially overlaps. Others are the same as those in FIG.

Next, the operation of this embodiment will be described with reference to FIGS. 49 and 50. FIG. 49 is a flow chart of this embodiment, and in step S101, the distribution of the degree of risk is input using C _n , r _n , θ _n (n corresponds to each obstacle). In step S102, the vehicle speed v is input, and in step S103, an alarm determination boundary line as shown by a broken line in FIG. 50 is determined. The warning judgment boundary is the body A
Is a boundary line provided in parallel with the front surface of the vehicle body at a distance L _ar in front of the vehicle body, and an area from the vehicle body to this boundary line is an alarm determination area. The boundary line is obtained as follows. That is, the initial velocity and v, and the stopping distance of deceleration and α is v ^2/2
α, on the other hand, when the time from when the alarm is issued until the driver reacts and starts braking is t _act , the distance traveled during this time is v · t _act , so L _ar is

[0067]

[Equation 7]

It becomes In the above equation, t _act may be a fixed value or may be set independently by the driver. Also, α should be changed depending on the road surface condition. FIG. 51 shows changes in the stop distance and the distance L _ar to the alarm judgment boundary line with respect to the vehicle speed. Next in step S104
Then, the area outer circumference vector of the risk C _n is calculated. That is,
As shown in FIG. 50, for example, the area center of the risk C ₁ is (r ₁ , θ ₁ ), and the vector indicating the area center is the vector r ₁ . Also, considering a vector C ₁ having the radius at the center of the region as a starting point and having a radius of the region, the vector of each point on the region having the risk C ₁ , that is, the region outer circumference vector is represented by vector r ₁ + vector C ₁ . Be done. In step S105, the area determination is performed with respect to each area outer peripheral vector thus obtained with the alarm determination boundary line. That is, the cosine of vector r _n + vector C _{n in} the y-axis direction is defined as vector e _y ,
The following formula

[0069]

[Equation 8]

When the condition is satisfied, it is determined that the alarm determination area and the graphic corresponding to the degree of risk at least partially overlap, and an alarm is issued in step S106.

Example 16. The warning judgment boundary line is shown in Fig. 5.
As shown in FIG. 2, centering on the vehicle body A, in front of the vehicle, at a radius L
_It may be a boundary line provided on the semicircle of _ar , and the area from the vehicle body to this boundary line is the alarm determination area. Therefore, in step S105 of FIG.

[0072]

[Equation 9]

When the condition is satisfied, it is determined that the alarm determination area and the graphic corresponding to the degree of risk at least partially overlap, and an alarm is issued in step S106.

Example 17 FIG. 53 is a case in which the above-described fifteenth embodiment is applied to the case where the two-dimensional distribution of the risk degree is three-dimensionally expressed, and a plane perpendicular to the xy plane at the distance L _ar as shown by a broken line is used as the alarm determination boundary plane. If the three-dimensional figure C _n is in the area between the boundary plane and the vehicle, or if the three-dimensional figure passes through the boundary plane (a cutting plane is generated as indicated by diagonal lines), an alarm is output.

In the fifteenth to seventeenth embodiments, the alarm intensity may be changed according to the area or volume of the degree of danger existing in the alarm determination area. Further, for example, a first warning judgment area having a boundary defined by the distance expressed by the equation (13) and a second warning judgment area defined by a stop distance defined as a boundary are provided, and the first warning judgment area is weak. As for the warning, a stronger warning may be issued in the second warning judgment area. Further, the distance L _ar to the boundary line or the boundary plane may be constant regardless of v.

Example 18. 54 is a block diagram showing a vehicle preventive safety device according to still another embodiment of the present invention. In FIG. 54, 120 is an alarm output means, and similarly to FIG. 48, with respect to the two-dimensional distribution of the risk levels output from the risk level output means 80, the alarm determination area is determined by the distance from the own vehicle. When the warning judgment area and the figure corresponding to the degree of risk at least partially overlap,
Although an alarm signal is output, in the case of this embodiment,
When the warning judgment area is determined, the driving condition of the driver is further detected, and the warning judgment area is also determined including this driving situation. Others are the same as those in FIG.

The operation of this embodiment will be described below with reference to FIGS. 55 and 56. FIG. 55 is a flowchart of this embodiment, and steps S111 to S113 are
The operation is the same as that of the fifteenth embodiment, and an alarm judgment boundary line having the distance shown in Expression (13) is obtained. Step S11
In 4, the driving vehicle monitoring means 11 similar to that used in Example 13 is used.
Information of the line-of-sight direction θ _w is input by 0. Step S1
In No. 15, L _w is set to L _ar as the alarm judgment boundary line in the direction outside the line of sight.
To add. That is, as shown in FIG. 56, the viewing angle is changed by Δθ _w
And the direction of the line of sight

[0078]

[Equation 10]

In the area of (1), the distance of the alarm judgment boundary line is set to L _ar, and in the areas of other directions, the distance of the alarm judgment boundary line is set to L _ar + L _w . For L _w , Δt _act is the time from when the alarm is issued until the line of sight is moved in the direction of the object (C _{2 in} this case) to recognize it, and L _w = v · Δt _act . I.e.

[0080]

[Equation 11]

It becomes In the above equation, Δθ _w changes according to the speed in accordance with the visual field characteristics of a human, and v is large and Δθ _w is small. In step S116, the first embodiment
In the same manner as 5, the area outer circumference vector of the risk C _n is calculated. In step S117, for each area outer circumference vector,
The area determination is performed with the alarm determination boundary line according to θ.
That is, the vector c y of vector r _n + vector C _{n in} the y-axis direction is defined as vector e _y

[0082]

[Equation 12]

When the condition is satisfied, it is determined that the alarm determination area and the graphic corresponding to the degree of risk at least partially overlap, and an alarm is issued in step S118. As described above, in this embodiment, the warning determination is made in consideration of the direction of the visual field, so that the danger can be dealt with quickly in the inattentive state.

Example 19 As in the sixteenth embodiment, as shown in FIG. 57, the alarm determination boundary line is centered on the vehicle body A, is an arc of radius L _{ar in} the front of the vehicle, in the direction of view (viewing angle Δθ _w ), and in other directions. It may be a boundary line provided on a semicircle of L _ar + L _w , and the area from the vehicle body to this boundary line is the alarm determination area.

In the eighteenth and nineteenth embodiments, the warning judgment is made in consideration of the visual field direction of the driving vehicle. However, if Δt _act is made variable according to the consciousness state D _K of the driving vehicle, A more appropriate alarm becomes possible.

Example 20. FIG. 58 is a block diagram showing a vehicle preventive safety device according to still another embodiment of the present invention. In FIG. 58, 120 is an alarm output unit, and in the two-dimensional distribution of the risk levels output from the risk level output unit 80, for a plurality of risk levels, the total amount of the risk levels with respect to the direction from the vehicle (cumulative risk level). ) Is obtained and the distribution of the degree of risk according to the direction is output. Others are the same as those in FIG.

The operation of this embodiment will be described with reference to FIGS. 59 and 60. FIG. 59 is a flow chart of the alarm output means in the present embodiment. In step S121, the danger levels expressed in the real space coordinates corresponding to the road surface ahead of the vehicle are represented by C _n , r _n , θ _n (n is each obstacle). Corresponding to the object). In step S122, the position vector r
_As shown in FIG. 60 (a), for the risk degree C _{n of n} ,
Draw a line segment of angle η from the origin of real space coordinates. Step S
At 123, a vector on the circumference of the risk degree C _n is obtained. That is, as shown in FIG. 61, _assuming that the radius of danger is | C _n |, the existing position (center) is (r _n , θ _n ), and the azimuth η and the verification vector of variable size are vectors C _L , Degree C _n
The vector obtained by subtracting the center (r _n , θ _n ) from the end of the test vector C _L is represented by the vector C _L −vector r _n ,

[0088]

[Equation 13]

It is possible to know whether the end of the test vector C _L is inside or outside the degree of danger C _n by the magnitude relationship between and | C _n |.
Therefore, the vector of the degree of danger C _{n on} the circumference is

[0090]

[Equation 14]

It is only necessary to obtain a test vector that satisfies the following equation. For the direction η, the first test vector at this time is vector C _Ll and the second test vector is vector C _Lu . In step S124, the line segment length C existing on the line segment of the angle η from the origin of the real space coordinates and existing inside the risk C _{n
Find Ln} (η). That is, C _Ln (η) is the first
And the difference of the second test vector,

[0092]

[Equation 15]

It is calculated by In step S125, the line segment lengths thus obtained are summed over all azimuths η to obtain the cumulative risk ΣC _Ln (η). In step S126, it is confirmed whether or not another risk C _n exists on the azimuth η, and if it exists, the same operation is repeated to obtain the sum. If there is no azimuth, it is confirmed in step S127 whether or not all azimuths η have been operated. If there is any azimuth that has not been moved, η is newly set and the above operation is repeated. In step S128, FIG.
As shown in 0 (b), the cumulative risk ΣC _Ln (η) is plotted on the vertical axis and the azimuth η is plotted on the horizontal axis, and the cumulative risk is displayed on the η-ΣC _Ln orthogonal plane. By displaying the azimuth distribution of the degree of danger in this way, it is possible to clearly warn the driver of the direction in which the degree of danger is high, and it becomes easy to avoid danger and guide the vehicle in a safe direction.

As a display example of the cumulative risk in step S127, the cumulative risk may be displayed in polar coordinate representation on the ΣC _Ln -η polar plane as shown in FIG. 60 (c). In this case, the comparison between the traveling direction and the degree of danger is easy to understand, and the alarm display becomes clearer.

[0095]

As described above, according to the present invention, the relative distance and azimuth between the obstacle and the vehicle in the peripheral traveling area mainly around the front of the vehicle, and the size of the obstacle are determined. Obstacle detecting means for detecting, data extracting means for extracting extracted data corresponding to the obstacle degree of the obstacle from the output signal of the obstacle detecting means, corresponding to the traveling road surface in front of the distance and azimuth and the extracted data Running environment output means for generating a graphic corresponding to the size of the extracted data on the real space coordinates, running state detection means for detecting the running state of the own vehicle,
A risk factor calculating means for setting the distribution of the risk factor on the real space coordinates according to the traveling state, and a risk factor is obtained from the extracted data and the risk factor, and the risk factor is determined according to its magnitude. Since the vehicle preventive safety device is constituted by the risk output means which outputs the data in a dimensional distribution, the risk can be evaluated in multiple directions at the same time even when a plurality of vehicles are traveling in the vicinity at the same time. Moreover, in the evaluation of the degree of risk, the size of the obstacle is also taken into consideration, so that it becomes safer.

According to the second aspect of the present invention, when the risk is calculated by the above apparatus, the graphic corresponding to the size of the extracted data is enlarged, reduced or deformed according to the risk coefficient, and the generated graphic is used. Since the data is output on the real space coordinates, the risk degree calculation process becomes easy.

According to the third aspect of the present invention, when the degree of danger is obtained by the above apparatus, the traveling environment output means corresponds to the size of the extracted data on the real space coordinates corresponding to the road surface ahead. Since such a solid figure function is generated, the solid figure function is cut according to the risk factor, and the two-dimensional distribution of the risk is output according to the size of the cut surface, the risk calculation process is performed. It will be easy.

According to the fourth aspect of the present invention, the predicted data in consideration of the movement of the obstacle before that is added to the extracted data corresponding to the size of the obstacle to obtain new extracted data, and the dangerous data is obtained. Since the degree is determined, the degree of danger including the future behavior of the obstacle can be evaluated, judged, and processed, and a safer preventive safety device can be obtained.

Further, according to the invention of claim 5, the extraction data corresponding to the obstacle degree of the obstacle is output according to the ratio of the height and the width of the obstacle. Nonetheless, motorcycles and pedestrians, etc.
Appropriate evaluations and judgments can be made, and safer preventive safety devices can be obtained.

Further, according to the invention of claim 6, the driving condition of the driver is detected and the distribution of the risk factor is set including the driving condition for each of the above vehicle safety devices. A safe preventive safety device is obtained.

Further, according to the invention of claim 7, for each of the above vehicle preventive safety devices, an alarm judgment area is determined based on the distance from the own vehicle on the real space coordinates corresponding to the traveling road surface, and this alarm is issued. Since the warning output means for outputting a warning signal is provided when the judgment area and the figure corresponding to the degree of danger overlap at least partially, it is possible to promptly notify the driver of the danger.

Further, according to the invention of claim 8, the driving condition of the driver is detected, and the warning judgment area is determined also including this driving condition. Therefore, the driver can be informed of the danger earlier. A safer preventive safety device can be obtained.

According to the ninth aspect of the present invention, for the plurality of risk levels output from the risk level output means, the total amount of the risk levels with respect to the azimuth from the own vehicle is calculated, and the distribution of the risk levels is output. Therefore, it is possible to notify the driver of a high-risk direction earlier, and it becomes easier to avoid the danger and guide the vehicle in a safe direction.

[Brief description of drawings]

FIG. 1 is a block diagram showing a vehicle preventive safety device according to a first embodiment of the present invention.

FIG. 2 is an explanatory diagram illustrating an operation of the obstacle detection means according to the first exemplary embodiment of the present invention.

FIG. 3 is a configuration diagram showing a traveling state detecting means according to the first embodiment of the present invention.

FIG. 4 is a flowchart showing the operation of the vehicle preventive safety device according to the first embodiment of the present invention.

FIG. 5 is an explanatory diagram illustrating an operation of the obstacle detection means according to the first exemplary embodiment of the present invention.

FIG. 6 is an explanatory diagram illustrating an operation of the obstacle detection means according to the first exemplary embodiment of the present invention.

FIG. 7 is an explanatory diagram illustrating an operation of the data extraction unit according to the first embodiment of the present invention.

FIG. 8 is an explanatory diagram illustrating an operation of the traveling environment output unit according to the first embodiment of the present invention.

FIG. 9 is an explanatory diagram illustrating the operation of the traveling state detecting means according to the first embodiment of the present invention.

FIG. 10 is a flowchart showing the operation of the traveling state detection means according to the first embodiment of the present invention.

FIG. 11 is a flowchart showing the operation of the risk factor calculation means according to the first exemplary embodiment of the present invention.

FIG. 12 is an explanatory diagram showing an example of a distribution of risk factors M according to the first embodiment of the present invention.

FIG. 13 is a flowchart showing the operation of the risk output means according to the first exemplary embodiment of the present invention.

FIG. 14 is an explanatory diagram illustrating an example of an output from the risk output unit according to the first exemplary embodiment of the present invention.

FIG. 15 is an explanatory diagram illustrating another example of the output from the risk output unit according to the first exemplary embodiment of the present invention.

FIG. 16 is an explanatory diagram illustrating another example of the output from the risk output unit according to the first exemplary embodiment of the present invention.

FIG. 17 is an explanatory diagram illustrating another example of the output from the risk output unit according to the first exemplary embodiment of the present invention.

FIG. 18 is an explanatory diagram showing an example of a distribution of risk factors M according to the second embodiment of the present invention.

FIG. 19 is an explanatory diagram showing an example of a distribution of risk factors M according to the third embodiment of the present invention.

FIG. 20 is an explanatory diagram showing an example of a distribution of risk factors M according to the fourth embodiment of the present invention.

FIG. 21 is an explanatory diagram showing an example of a distribution of risk factors M according to the fifth embodiment of the present invention.

FIG. 22 is an explanatory diagram illustrating an operation of the risk factor calculation means according to the fifth exemplary embodiment of the present invention.

FIG. 23 is a flowchart showing the operation of the risk factor calculation means according to the fifth exemplary embodiment of the present invention.

FIG. 24 is an explanatory diagram showing an example of a distribution of risk factors M according to the sixth embodiment of the present invention.

FIG. 25 is an explanatory diagram showing an example of a distribution of risk factors M according to the seventh embodiment of the present invention.

FIG. 26 is a flow chart showing the operation of the risk factor calculation means according to the eighth exemplary embodiment of the present invention.

FIG. 27 is an explanatory diagram showing an example of the distribution of the risk factor W according to the eighth embodiment of the present invention.

FIG. 28 is a flow chart showing the operation of the risk output means according to the eighth embodiment of the present invention.

FIG. 29 is an explanatory diagram showing an example of the distribution of risk factors W according to the ninth embodiment of the present invention.

FIG. 30 is an explanatory diagram illustrating an operation of the risk level output means according to the ninth exemplary embodiment of the present invention.

FIG. 31 is an explanatory diagram illustrating an operation of the risk level output means according to the ninth exemplary embodiment of the present invention.

FIG. 32 is an explanatory diagram illustrating an example of an output from the risk output unit according to the ninth exemplary embodiment of the present invention.

FIG. 33 is a block diagram showing a vehicle preventive safety device according to a tenth embodiment of the present invention.

FIG. 34 is an explanatory diagram explaining an operation of the traveling environment output means according to the tenth embodiment of the present invention.

FIG. 35 is a flowchart showing operations of the first traveling environment output means and the prediction means according to the tenth embodiment of the present invention.

FIG. 36 is an explanatory diagram showing an example of the output of the second traveling environment output means according to the tenth embodiment of the present invention.

FIG. 37 is an explanatory diagram showing another example of the output of the second traveling environment output means according to the tenth embodiment of the present invention.

FIG. 38 is an explanatory diagram showing another example of the output of the second traveling environment output means according to the tenth embodiment of the present invention.

FIG. 39 is an explanatory diagram illustrating an operation of the image data extraction unit according to the eleventh embodiment of the present invention.

FIG. 40 is a block diagram showing a vehicle preventive safety device according to a thirteenth embodiment of the present invention.

FIG. 41 is a flowchart showing the operation of the risk factor calculating means according to the thirteenth embodiment of the present invention.

FIG. 42 is an explanatory diagram showing a driver monitoring apparatus according to Embodiment 13 of the present invention.

FIG. 43 is a flowchart showing the operation of the driver monitoring apparatus according to the thirteenth embodiment of the present invention.

FIG. 44 is an explanatory diagram illustrating an operation of the driver monitoring device according to the thirteenth embodiment of the present invention.

FIG. 45 is an explanatory diagram showing an example of a distribution of risk factors M according to the thirteenth embodiment of the present invention.

FIG. 46 is an explanatory diagram illustrating an operation of the risk factor calculating means according to the thirteenth embodiment of the present invention.

FIG. 47 is a flowchart showing the operation of the risk factor calculating means according to the fourteenth embodiment of the present invention.

FIG. 48 is a block diagram showing a vehicle preventive safety device according to a fifteenth embodiment of the present invention.

FIG. 49 is a flow chart showing the operation of the alarm output means according to the fifteenth embodiment of the present invention.

FIG. 50 is an explanatory diagram for explaining the operation of the alarm output means according to the fifteenth embodiment of the present invention.

FIG. 51 is an explanatory diagram for explaining the operation of the alarm output means according to the fifteenth embodiment of the present invention.

FIG. 52 is an explanatory diagram for explaining the operation of the alarm output means according to the sixteenth embodiment of the present invention.

FIG. 53 is an explanatory diagram illustrating an operation of the alarm output means according to the seventeenth embodiment of the present invention.

FIG. 54 is a block diagram showing a vehicle preventive safety device according to an eighteenth embodiment of the present invention.

FIG. 55 is a flowchart showing the operation of the alarm output means according to the eighteenth embodiment of the present invention.

FIG. 56 is an explanatory diagram for explaining the operation of the alarm output means according to the eighteenth embodiment of the present invention.

FIG. 57 is an explanatory diagram for explaining the operation of the alarm output means according to the nineteenth embodiment of the present invention.

FIG. 58 is a block diagram showing a vehicle preventive safety device according to a twentieth embodiment of the present invention.

FIG. 59 is a flowchart showing the operation of the alarm output means according to the twentieth embodiment of the present invention.

FIG. 60 is an explanatory diagram illustrating an operation of the alarm output means according to the twentieth embodiment of the present invention.

FIG. 61 is an explanatory diagram for explaining the operation of the alarm output means according to the twentieth embodiment of the present invention.

FIG. 62 is a block diagram showing a conventional travel control device.

[Explanation of symbols]

 10 First Obstacle Detecting Means 20 Second Obstacle Detecting Means 40 Image Data Extracting Means 50 First Traveling Environment Outputting Means 60 Vehicle Motion Measuring Means 70 Risk Coefficient Calculating Means 80 Risk Outputting Means 90 Predicting Means 100 2 driving environment output means 110 driver monitoring means 120 alarm output means

─────────────────────────────────────────────────── ─── Continuation of the front page (51) Int.Cl. ⁶ Identification code Internal reference number FI technical display location G01S 17/93

Claims (9)

[Claims] 1. An obstacle detecting means for detecting a relative distance and direction between an obstacle and a vehicle in a surrounding traveling area mainly in front of the vehicle and the size of the obstacle, and the obstacle. Data extraction means for extracting the extracted data corresponding to the obstacle degree of the obstacle from the output signal of the detection means, the distance and azimuth and the extracted data from the extracted data on the real space coordinates corresponding to the road surface ahead. Driving environment output means for generating a figure corresponding to the size, traveling state detection means for detecting the traveling state of the own vehicle, and risk degree for setting the distribution of the risk coefficient on the real space coordinates according to the traveling state A preventive safety device for a vehicle, comprising: a coefficient calculating means; and a risk output means for calculating a risk from the extracted data and the risk coefficient, and outputting the risk in a two-dimensional distribution according to its magnitude. 2. The risk level output means enlarges, reduces, or modifies a graphic corresponding to the size of the extracted data according to the risk coefficient, and places the generated graphic on real space coordinates. The preventive safety device for a vehicle according to claim 1, which is for outputting. 3. The traveling environment output means generates a three-dimensional figure function corresponding to the size of the extracted data on the real space coordinates corresponding to the road surface ahead, and the risk output means outputs the three-dimensional figure function. The preventive safety device for a vehicle according to claim 1, wherein the vehicle is cut according to a risk factor and a two-dimensional distribution of the risk is output depending on the cut surface. 4. The traveling environment output means stores the position data of the obstacle obtained by the obstacle detection means, and the predicted amount of motion is taken into consideration in consideration of the movement of the obstacle before that from the position data for the same obstacle. Equipped with a calculating means for calculating the prediction data corresponding to, by adding the above prediction data to the extracted data,
The vehicle preventive safety device according to any one of claims 1 to 3, which generates a graphic corresponding to a size of new extracted data corresponding to the degree of failure. 5. The data extracting means outputs, from the output signal of the obstacle detecting means, the extracted data corresponding to the obstacle degree of the obstacle according to the height-width ratio of the obstacle. The preventive safety device for vehicle according to any one of 1 to 3. 6. A driving condition detecting means for detecting a driving condition of a driver is provided, wherein the risk coefficient calculating means calculates a distribution of the risk coefficient on the real space coordinate according to the running condition of the own vehicle and the driving condition. The preventive safety device for a vehicle according to any one of claims 1 to 5, wherein the preventive safety device is set. 7. A warning judgment area is determined based on the distance from the own vehicle on the real space coordinates corresponding to the road surface, and the warning judgment area and the graphic corresponding to the degree of danger are at least partially overlapped. The preventive safety device for a vehicle according to any one of claims 1 to 6, further comprising alarm output means for outputting an alarm signal at the time. 8. A warning condition determination means for detecting a driving condition of a driver is provided, and a warning judgment area is determined based on a distance from the own vehicle and the driving condition on a real space coordinate corresponding to a traveling road surface. 7. The vehicular preventive safety device according to claim 1, further comprising an alarm output means for outputting an alarm signal when the judgment area and the graphic corresponding to the degree of danger overlap at least partially. 9. A two-dimensional distribution of the risk levels output from the risk level output unit, the alarm output unit for obtaining the total amount of the risk levels with respect to the direction from the vehicle and outputting the distribution of the risk levels. 7. The vehicle preventive safety device according to any one of 1 to 6.