JP5261045B2 - Vehicle driving support device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To attain a natural driving support by setting a recognition ratio appropriately in accordance with a traveling environment. <P>SOLUTION: A control unit 3 sets a current risk as a risk function with respect to a white line, guard rail, sidewall and a three-dimensional object existing ahead a vehicle, and sets the degree that a target vehicle recognizes its own vehicle as a recognition ratio according to the vehicle speed of the target vehicle and the direction of the its own vehicle with respect to the target vehicle and the direction of the line of sight of the driver of the target vehicle as for the risk function of the target vehicle, and variably sets a recognition rate by comparing the current recognition rate with the previous recognition rate, and corrects the risk function of the vehicle to the recognition rate, and predicts the final avoidance route from the set risk function, and outputs a control signal to an automatic steering control device 14 based on the turning controlled variables of the final avoidance route to execute steering control, and outputs a signal to an automatic controller 15 for brake control. <P>COPYRIGHT: (C)2010,JPO&amp;INPIT

Description

  The present invention relates to a driving support apparatus for a vehicle that sets a degree of recognition of a target vehicle with respect to the host vehicle as a recognition rate, sets a risk based on the recognition rate, and supports driving of the vehicle based on the risk.

  In recent years, vehicles have been driven so that they can recognize the front environment and drive safely based on information obtained from ITS (Intelligent Transport Systems), inter-vehicle communication systems, in-vehicle image processing systems, radar devices, etc. Various driving assistance devices to assist have been proposed and put into practical use.

For example, in Japanese Patent Application Laid-Open No. 2007-241729, the position of the own vehicle side vehicle and the other vehicle side vehicle at the intersection is predicted, and the minimum distance when the own vehicle side vehicle and the other vehicle side vehicle are closest to each other at the intersection is calculated. Based on the calculated minimum distance, the risk of collision with another vehicle side vehicle at the intersection is calculated. Then, it is determined whether or not the driver of the other vehicle side vehicle recognizes the vehicle, and when it is estimated that there is no vehicle in the direction of the line of sight and the vehicle is not recognized, it is determined in advance. A technique for changing a crossing vehicle collision risk threshold value so as to be lowered by a predetermined value is disclosed.
JP 2007-241729

  However, when considering the recognition as disclosed in the above-mentioned Patent Document 1, there is a problem that it is not possible to determine the recognition with high accuracy only by the direction of the driver's line of sight. For example, even if the vehicle is facing the same direction, it may be recognized when the vehicle speed is low, but may not be recognized when the vehicle speed is high. Even if it is, it can be regarded as recognized if the driver has already memorized enough. If an alarm system is constructed without taking such changes into account, an unnecessary alarm may be performed or a system in which an alarm is difficult to be performed may result in inconvenience for the driver.

  The present invention has been made in view of the above circumstances, and provides a driving support device for a vehicle that can set a recognition rate appropriately according to the driving environment and can provide driving support to the driver with a natural feeling. It is aimed.

The present invention recognizes a traveling environment recognizing means for recognizing a traveling environment and acquiring at least information of a three-dimensional object outside the vehicle and communicating with another vehicle to acquire information from the other vehicle, and the traveling environment recognizing means. The target vehicle to be controlled is extracted from the information of the three-dimensional object, the risk setting means for setting a risk for the target vehicle, the vehicle speed of the target vehicle from the traveling environment recognition means, and the vehicle from the target vehicle. The direction of the vehicle and the direction of the driver of the target vehicle are input, and the perceived rate setting means for setting the degree of recognition of the target vehicle with respect to the host vehicle as the perceived rate according to the input information, and the perceived rate And a risk correction means for correcting the risk of each of the target vehicles according to the above.The perceived rate setting means compares the currently set perceived rate with the previously set perceived rate, and compares the currently set perceived rate. If knowledge rate is smaller than the recognition rate is set above the previous outputs the recognition rate was allowed Please low by lowering the object recognition rate set the last time at a rate set in advance as an object recognition rate previously set, On the other hand, when the perceived rate set this time is equal to or higher than the previously set perceived rate, the perceived rate set this time is output as it is as the previously set perceived rate .

  According to the vehicle driving assistance device of the present invention, it becomes possible to set the recognition rate appropriately according to the driving environment and to provide driving assistance to the driver with a natural feeling.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.
1 to 9 show an embodiment of the present invention, FIG. 1 is a schematic configuration diagram of a driving support device mounted on a vehicle, FIG. 2 is a flowchart of a driving support control program, and FIG. 3 is a flowchart continuing from FIG. FIG. 4 is an explanatory diagram showing an example of a risk rate correction gain calculation routine, FIG. 5 is an explanatory diagram showing an example of a risk function set in front, FIG. 6 is an explanatory diagram of a gaze direction angle with respect to the vehicle direction, and FIG. FIG. 8 is an explanatory diagram of the characteristics of the recognition rate, FIG. 9 is an explanatory diagram of the recognition rate set by predicting a change due to memory, and FIG. 10 shows an example of the generated avoidance route and turning control amount It is explanatory drawing.

  In FIG. 1, reference numeral 1 denotes a vehicle such as an automobile (own vehicle), and a driving support device 2 is mounted on the vehicle 1. The driving support device 2 includes a control unit 3 that includes a stereo image recognition device 4 that recognizes a forward environment based on image data, a gaze direction detection device 5 that detects a gaze direction (angle) of a driver, and other vehicles and vehicles. A communication device 6 that performs inter-vehicle communication to acquire information on other vehicles, a positioning device 7, a vehicle speed sensor 8 that detects the host vehicle speed V, and a yaw rate sensor 9 that detects the yaw rate (dψ / dt) are connected. It is configured.

  The stereo image recognition device 4 is mounted at a certain interval in front of the ceiling in the vehicle interior, and processes image data from a set of (left and right) CCD cameras 10 that captures an object outside the vehicle in stereo from different viewpoints. .

  Processing of an image from the CCD camera 10 in the stereo image recognition device 4 is performed as follows, for example. First, distance information is obtained from a set of stereo image pairs taken in the traveling direction of the host vehicle 1 captured by the CCD camera 10 from the corresponding positional shift amount, and a distance image is generated. Then, based on this data, a well-known grouping process is performed and compared with frames (windows) such as three-dimensional road shape data, side wall data, and three-dimensional object data stored in advance. Side wall data such as guardrails and curbs that exist along the road are extracted, and three-dimensional objects are classified and extracted into other three-dimensional objects such as two-wheeled vehicles, ordinary vehicles, large vehicles, pedestrians, and utility poles.

  Each of the recognized data is calculated by calculating the position in the coordinate system in which the own vehicle 1 is the origin, the front-rear direction of the own vehicle 1 is the X axis, and the width direction is the Y axis. In the vehicle data of a large vehicle, the length in the front-rear direction is preliminarily estimated as 3 m, 4.5 m, 10 m, etc., and the width direction uses the center position of the detected width. The currently existing center position is calculated with the coordinates of (xobstacle, yobstacle). In addition, when the longitudinal length of the vehicle can be obtained with high accuracy by inter-vehicle communication or the like, the above-described center position may be calculated using the length data.

  Further, in the three-dimensional object data, the relative speed with respect to the own vehicle 1 is calculated from the change in the X-axis direction and the change in the Y-axis direction of the distance from the own vehicle 1, and the vector V is taken into consideration for the relative speed and the vector V Thus, the X-axis direction speed and the Y-axis direction speed (vxobstacle, vyobstacle) of each three-dimensional object are calculated.

  Each information obtained in this way, that is, white line data, guard rails existing along the road, side walls such as curbs, and solid object data (type, distance from own vehicle 1, center position (xobstacle, yobstacle), speed) Each data of (vxobstacle, vyobstacle), the direction (angle) θvo of the three-dimensional object with respect to the vehicle position) is input to the control unit 3. Thus, in this Embodiment, the stereo image recognition apparatus 4 is provided as a driving | running | working environment recognition means.

  The line-of-sight detection device 5 receives image information of the driver's eyes from a field-of-view camera 11 arranged in the direction of the driver's face. This gaze direction detection device 5 detects the gaze direction θe of the driver by a so-called pupil / corneal reflection method, and the virtual image by the infrared lamp 12 on the cornea is caused by the difference between the rotation centers of the cornea and the eyeball. The eye movement is detected by detecting the movement of the eyeball in parallel with the visual field camera 11 while simultaneously detecting the center of the pupil by the visual field camera 11. Information on the line-of-sight direction θe is input to the control unit 3 and transmitted to other vehicles by inter-vehicle communication by the communication device 6. Note that the detection of the line-of-sight direction θe is not limited to this detection method, and other detection methods (EOG (Electro-Oculography) method, scleral reflection method, corneal reflection method, search coil method, etc.) if possible. It may be detected by.

  The communication device 6 is a device compatible with, for example, ITS (Intelligent Transport Systems), receives light and radio wave beacons from road incidental facilities, and receives traffic congestion information, weather information, traffic regulation information in a specific area, etc. In addition, it has a function of acquiring vehicle information, performing vehicle-to-vehicle communication with other vehicles traveling around the host vehicle 1, and exchanging vehicle information. In vehicle-to-vehicle communication in this embodiment, communication with a vehicle existing in a communicable area is performed using a carrier signal in a predetermined frequency band, vehicle type, vehicle position, vehicle direction, driver's line-of-sight direction, vehicle speed, Information such as an acceleration / deceleration state, a brake operation state, a blinker state, etc. are exchanged, and the acquired information is input to the control unit 3. Accordingly, the communication device 6 is provided as a traveling environment recognition unit.

  The positioning device 7 is composed of, for example, a navigation device, measures the position of the host vehicle 1, calculates and synthesizes the position of the host vehicle and the map information, changes the scale of the map, and displays the detailed name of the place name. In response to an operation input such as display switching of area information, the current position of the host vehicle 1 and a map around it are displayed on the display 13, and various road / traffic information received via the communication device 6 are displayed. Display information. The position of the host vehicle 1 is determined by dead reckoning navigation based on the position of the host vehicle 1 based on radio waves from positioning satellites such as GPS (Global Positioning System) and signals from the geomagnetic sensor and the wheel speed sensor. Positioning is performed based on the position of 1 and information acquired via the communication device 6. This positioning information is input to the control unit 3 and further transmitted to other vehicles by inter-vehicle communication via the communication device 6.

  In accordance with the driving support control program described later, the control unit 3 determines the current risk level for each of the white line, guardrail, side wall, and three-dimensional object existing in front of the risk function Rline, Set as Robstacle. At this time, for the risk function of the target vehicle, the degree of recognition of the target vehicle is recognized according to the vehicle speed of the target vehicle, the direction of the host vehicle with respect to the target vehicle, and the line-of-sight direction of the driver of the target vehicle. It is set as a rate, and the perceived rate set this time is compared with the previously set perceived rate to variably set the perceived rate, and the risk function of the vehicle is corrected by this perceived rate. The current total risk function R is set from the risk functions Rline and Robstacle set in this way, the temporal change of the position of each target for which the total risk function R is set is predicted, and the temporal change of the total risk function R is predicted, Based on this, a final avoidance route R (t) f is predicted, and a control signal is output to the automatic steering control device 14 based on the turning control amount u (t) of the final avoidance route R (t) f. Then, the steering control is executed, and a signal is output to the automatic brake control device 15 based on the value of the final avoidance route R (t) f to execute the brake control. When signals are output to the automatic brake control device 15 and the automatic steering control device 14, the signals are visually displayed on the display 13 to notify the driver. That is, the control unit 3 is configured to have functions as a risk setting unit, a recognition rate setting unit, and a risk correction unit.

Next, the driving support control program executed by the driving support device 2 will be described with reference to the flowcharts of FIGS.
First, in step (hereinafter abbreviated as “S”) 101, necessary parameters are read, and the process proceeds to S102, where the current risk function Rline for white lines (guardrails and side walls are treated as equivalent to white lines) is The calculation is performed according to the equation (1).
Rline = Kline · (y−ylinec) ² (1)
Here, Kline is a preset gain, and ylinec is a white line center coordinate. That is, as shown in FIG. 5, the current risk function Rline for the white line is 2 centered on the center of the road recognized by the left and right white lines (guardrails and side walls are also treated as white lines). Is given by In the present embodiment, the risk function Rline is a quadratic function. However, the risk function Rline may be a function that leads to a larger risk value as it approaches the white line from the center of the road, for example, 4 It can also be a second or sixth order function. In the present embodiment, the guard rail and the side wall are also treated in the same way as the white line so as to give a risk function Rline of a quadratic function. However, in the case of the guardrail and the side wall, the function is different from the risk function Rline for the white line. It may be changed so that a larger risk value is derived than in the case of the white line. For example, when the risk function Rline for the left and right white lines is given by a quadratic function, the card rail and the side wall are changed to a quartic or sixth order function. Further, even for the same quadratic function, the value of the gain Kline may be changed to a large value. Furthermore, the risk function Rline for the white line is not limited to the example in which the center of the travel path is the central axis, but the central axis may be offset so that the risk values differ between the left and right white lines.

  Next, proceeding to S103, the recognition rate correction gain Gr is calculated. This recognition rate correction gain Gr is calculated when the target is a vehicle, and specifically, is calculated according to the flowchart of FIG.

  First, in S201, the line-of-sight direction θd with respect to the vehicle direction is detected by subtracting the vehicle direction θv0 from the target vehicle from the line-of-sight direction θe acquired by inter-vehicle communication for the driver of the target vehicle. For example, in the traveling environment shown in FIG. 6, the line-of-sight direction θd1 with respect to the direction of the vehicle in the vehicle 1 is calculated as θe1−θv01, where θe1 is the angle of the line-of-sight direction and θv01 is the angle of the vehicle direction. Further, the line-of-sight direction θd2 with respect to the vehicle direction in the vehicle 2 is calculated as θe2−θv02, where θe2 is the angle of the line-of-sight direction and θv02 is the angle of the vehicle direction. The driver's line-of-sight direction θe and the vehicle direction θv are detected with the left side as a negative angle and the right side as a positive angle with respect to the front direction of the target vehicle (angle zero).

  Next, the process proceeds to S202, and a recognition rate r is set with reference to a map (FIG. 7) set in advance based on the vehicle speed of the target vehicle and the line-of-sight direction θd with respect to the direction of the host vehicle. As shown in FIG. 7, the map of the recognition rate r is set in advance with the vehicle speed of the target vehicle and the line-of-sight direction θd with respect to the vehicle direction as variables.

In the present embodiment, the map of the recognition rate r shown in FIG. 7 is formed as follows.
As shown in FIG. 8A, first, the highest recognition rate at the point where the line-of-sight direction θd with respect to the direction of the subject vehicle becomes 0 °, that is, the point at which the subject vehicle becomes the central view of the driver of the subject vehicle. The recognition rate at this time is set to 1.0.

  Further, the recognition rate is set to be gradually lower than 1.0 in the region of 0 ° <θd ≦ 10 ° and −10 ° ≦ θd <0 where the visual acuity is considered to gradually decrease with respect to the visual acuity at the gazing point. To do.

  Then, gradually, the area to be recognized by both eyes (−35 ° ≦ θd ≦ 35 °), the area that can be recognized by both eyes (−60 ° ≦ θd ≦ 60 °), and the visual field limit (θd = ± 100 °) are gradually recognized. The rate is set to decrease, and the recognition rate is set to 0 in the region outside the field of view (100 ° <θd, θd <−100 °).

  Further, as shown in FIG. 8B, it is known that the moving object visual acuity decreases as the vehicle speed increases. Accordingly, the relationship between the moving object visual acuity and the vehicle speed is reflected in the vertical axis direction of the relationship between the recognition rate and the vehicle direction in FIG. The recognition rate is set to decrease as the vehicle speed of the target vehicle increases.

  Furthermore, as shown in FIG. 8C, it is known that the viewing angle becomes narrower as the vehicle speed increases. Accordingly, the relationship between the viewing angle and the vehicle speed is reflected in the horizontal axis direction of the relationship between the recognition rate and the viewing direction with respect to the own vehicle direction in FIG. The recognition rate is set to decrease as the vehicle speed of the target vehicle increases.

  After the recognizable rate r is set in S202 described above, the process proceeds to S203 and the subsequent steps, and the processes in S203 to S206 are processes for setting a recognized rate by predicting a change due to storage. That is, in S203, the recognition rate r_flt (z) set in the previous process is replaced with the previous value r_flt (z-1) of the current process (r_flt (z-1) = r_flt (z)).

  Then, the process proceeds to S204, where the perceived rate r set in S202 is compared with the previous value r_flt (z-1). If the perceived rate r is lower than the previous value r_flt (z-1), the process proceeds to S205. The current value r_flt (z) of the recognition rate is set to D · r_flt (z−1) (where D is a value set in advance with 0 <D <1), and is set lower than the previous value by a predetermined amount.

  Conversely, if the perceived rate r is equal to or greater than the previous value r_flt (z-1), the process proceeds to S206, and the current value r_flt (z) of the perceived rate is set as the perceived rate r.

Through the processing of S203 to S206, the current value r_flt (z) of the recognition rate is set as shown in the time chart of FIG. 9, for example.
That is, if the recognition rate r_flt (z) is set based only on the vehicle speed of the target vehicle and the line-of-sight direction θd with respect to the host vehicle direction, information on the host vehicle is obtained while the driver of the target vehicle is checking safety. The recognition rate r_flt (z) is lowered despite the memory of the driver of the target vehicle, and an unnecessary alarm or unnecessary control may be executed.

  Therefore, even if the recognition rate r based on the vehicle speed of the target vehicle and the line-of-sight direction θd with respect to the direction of the host vehicle is reduced, this is not reflected immediately, and human memory is ambiguous over time. In consideration of the above, the recognition rate r_flt (z) is basically set to gradually decrease with the passage of time.

  At this time, it is considered that there is a difference in time that can be stored in memory when the vehicle is sufficiently recognized and when the vehicle is not sufficiently recognized (such as when captured at one corner of the field of view). Accordingly, the time until the recognition rate r_flt (z) becomes 0 is changed in proportion to the magnitude of the value. That is, since r_flt (z) = D · r_flt (z−1) is set, the larger the initial recognition rate value, the longer the time until it becomes zero.

  Further, when the user's own vehicle is recognized again after diverting his / her eyes from the own vehicle, the process proceeds from S204 to S206 described above, and a large recognition rate is set again.

After setting the current value r_flt (z) of the recognition rate in S205 or S206 described above, the process proceeds to S207. For example, the recognition rate correction gain Gr is calculated by the following equation (2), and the routine is executed. Exit.
Gr = 1 / (1 + r_flt (z)) (2)
Note that the recognition rate correction gain Gr is applied only to the risk function of the vehicle as the current value r_flt (z) of the recognition rate increases, and the recognition rate correction gain Gr decreases. For three-dimensional objects other than, Gr = 1.

Returning to the flowchart of FIG. 2, after calculating the recognition rate correction gain Gr in S103, the process proceeds to S104, and a three-dimensional object (two-dimensional vehicle, ordinary vehicle, large vehicle, pedestrian, other three-dimensional object such as a power pole) is targeted. The current risk function Robstacle is calculated by the following equation (3).
Robstacle = Gr · Kobstacle · exp (− ((xobstacle−x) ²
/ (2 · σxobstacle ² ))-((yobstacle-y) ²
/ (2 · σyobstacle ² )))… (3)
Here, Kobstacle is a preset gain. Also, σxobstacle indicates the dispersion in the X-axis direction of the preset object, σyobstacle indicates the dispersion in the Y-axis direction of the preset object, and these dispersions σxobstacle and σyobstacle are, for example, CCD cameras 10 may be set larger as the recognition accuracy by 10 is lower. Also, the variances σxobstacle and σyobstacle should be set larger when the target type is a pedestrian or two-wheeled vehicle, and smaller when it is a three-dimensional object. Anyway. Furthermore, you may make it set according to the lap | wrap rate of the width direction of the own vehicle 1 and the target solid object. In FIG. 5, the three-dimensional object A <b> 1 and the three-dimensional object A <b> 2 are examples of the current risk function Robtacle for the three-dimensional object calculated by the above-described equation (3).

Next, it progresses to S105 and calculates the present total risk function R by the following (4) Formula.
R = Rline + Robstacle (4)

Next, the process proceeds to S106, and the position of the three-dimensional object (xobstacle (t), yobstacle (t)) after t seconds is estimated by the following equation (5).
(Xobstacle (t), yobstacle (t))
= (Xobstacle + vxobstacle.t, yobstacle + vyobstacle.t) (5)

  Next, the process proceeds to S107, and the position of the three-dimensional object (xobstacle (t), yobstacle (t)) estimated in t106 described above is substituted into x and y of the total risk function R calculated in S105. Then, the total risk function R (xobstacle (t), yobstacle (t)) after t seconds is set.

Next, in S108, the total risk function R (xobstacle (t), yobstacle (t)) after t seconds set in S107 is partially differentiated in the width direction (y direction), and the value is 0. From this point, the minimum point ymin (x, t) in the width direction (y direction) is calculated. That is,
∂R (xobstacle (t), yobstacle (t)) / ∂y = 0 (6)
The point that becomes is the minimum point.

Next, the process proceeds to S109, and the own vehicle position (X (t), Y (t)) after t seconds is estimated by the following equation (7).
(X (t), Y (t)) = (V · t, V · ∫sinψ (τ) dτ; integration range is 0 ≦ τ ≦ t)
... (7)
Here, ψ (t) is the yaw angle of the host vehicle 1 and is calculated by the following equation (8).
ψ (t) = (dψ / dt) · t
+ (1/2) · ((d ² ψ / dt ² ) + (u (t) / Iz)) · t ² (8)
Here, Iz is the yaw moment of inertia. U (t) is the turning control amount as described above, and is the additional yaw moment.

  Next, the process proceeds to S110, where the own vehicle position estimated in S110 is substituted for the y-direction minimum point ymin (x, t) calculated in S108, and the minimum point ymin ( Calculate X (t), t).

  Next, the process proceeds to S111, and each objective function J is created by the deviation of the lateral position Y (t) of the own vehicle and the minimum point ymin (X (t), t) and the turning control amount u (t) for each time. For each objective function J, a turn control amount u (t) for each time that minimizes the objective function J is obtained.

  For example, as shown in FIG. 10, a range in which the host vehicle 1 moves from time 0 (current) to Δt is considered as a control target region, and this region is divided by dt, 1dt, 2dt, 3dt,..., Mdt,. , (N−2) dt, (n−1) dt, and ndt (= Δt).

Between time 0 and 1 dt, for example, the objective function J0 to 1dt of the following equation (9) is set, and the turning control amount u (0) that minimizes the objective function J0 to 1dt is determined by a well-known optimization calculation. Ask.
J0 ~ 1dt = Wy · (ymin (X (1dt), 1dt) −Y (1dt)) ² + Wu · u (0) ² (9)
Here, Wy and Wu are preset weight values.

Further, for example, objective functions J1dt to 2dt of the following expression (10) are set between times 1dt and 2dt, and the turning control amount u (1dt) that minimizes the objective functions J1dt to 2dt is well known and optimized. Obtain by calculation.
J1dt ~ 2dt = Wy · (ymin (X (2dt), 2dt) −Y (2dt)) ² + Wu · u (1dt) ² (10)

Further, for example, the objective function J2dt to 3dt of the following equation (11) is set between the times 2dt to 3dt, and the turning control amount u (2dt) that minimizes the objective function J2dt to 3dt is known optimization. Obtain by calculation.
J2dt ~ 3dt = Wy · (ymin (X (3dt), 3dt) −Y (3dt)) ² + Wu · u (2dt) ² (11)
Since there are two minimum points at time 3dt, two values are also obtained for the turning control amount u (2dt).

Hereinafter, the same objective function is set after time 3dt, and the turning control amount is obtained. Between time (n-1) dt and ndt, for example, the objective function J (n-1) of the following equation (12) is used. dt ~ ndt is set, and the turning control amount u ((n-1) dt) that minimizes the objective function J (n-1) dt ~ ndt is obtained by a known optimization calculation.
J (n-1) dt ~ ndt = Wy. (Ymin (X (ndt), ndt) -Y (ndt)) ²
+ Wu · u ((n-1) dt) ² (12)

  Next, the process proceeds to S112, and the risk function R (t) for each route when the host vehicle 1 moves with the turn control amount u (t) for each time is set by the following equation (13).

R (t) = Rline + Robstacle (13)
Here, Rline and Robstacle are given by the values obtained when the host vehicle 1 moves with the turn control amount u (t) for each time in the above-described equations (1) and (3). And
Rline = Kline · (Y (t) −ylinec) ² (14)
Robstacle = Gr · Kobstacle · exp (− ((xobstacle (t) −X (t)) ²
/ (2 · σxobstacle ² ))-((yobstacle (t) -Y (t)) ²
/ (2 · σyobstacle ² ))) (15)

  Next, the process proceeds to S113, and a final avoidance route is selected as R (t) f from the risk function R (t) for each route set in S112.

Specifically, the maximum value Rmax is obtained for each route set in S112. That is,
Rmax = max (R (t)) (0 ≦ t ≦ Δt) (16)
Then, the route with the smallest maximum value Rmax is selected as the final avoidance route R (t) f.

  A cumulative risk value Rsum (= ∫R (t) dt; integration range is 0 ≦ t ≦ Δt) is obtained for each route, and a route having the smallest value is defined as a final avoidance route R (t) f. You may make it select.

  In S113 described above, if there is only one route set in S112, that route is set as the final avoidance route R (t) f.

  For example, in the example shown in FIG. 10, the route 1 indicated by the solid line and the route 2 indicated by the broken line are set by the process of S112, and the route having a smaller maximum value Rmax from these routes 1 and 2 by the process of S113, or A route having a small risk accumulation value Rsum is selected as a final avoidance route R (t) f. The turning control amounts u (t) for the routes 1 and 2 are as shown in FIG.

  Then, the process proceeds to S114, where it is determined whether or not there is an area that is equal to or greater than the predetermined maximum allowable risk value Rlim (R (t) f ≧ Rlim) in the final avoidance route R (t) f, and R ( t) When there is no region where f ≧ Rlim, the routine proceeds to S117, where the steering control command is issued to the automatic steering control device 14 based on the turning control amount u (t) of the final avoidance route R (t) f. To exit the program.

  As a result of the determination in S114, if it is determined that there is a region where R (t) f ≧ Rlim, the process proceeds to S115, and the braking start point Xbrake, based on the earliest time when R (t) f ≧ Rlim is satisfied. The braking start time Tbrake is calculated.

If the earliest time when R (t) f ≧ Rlim is Tm, the braking start point Xbrake is calculated by the following equation (17).
Xbrake = X (Tm) −Bx (17)
Here, Bx is a braking distance by the deceleration G set in advance, and is calculated by the following equation (18).
Bx = (V ² /(2.G))+Bx0 (18)
Here, Bx0 is a preset distance to the obstacle when the vehicle is stopped, and is a value of about 2 m, for example.

  The braking start time Tbrake is calculated by calculating backward from the above-described braking start point Xbrake.

  Next, in S116, a braking control command based on the braking start point Xbrake and the braking start time Tbrake is output to the automatic brake control device 15.

  In S117, the steering control command is output to the automatic steering control device 14 based on the turning control amount u (t) of the final avoidance route R (t) f, and the program is exited.

  As described above, according to the embodiment of the present invention, the current total risk function R is set for each of the white line, the guardrail, the side wall, and the three-dimensional object existing in front, and the time of the position of each target is determined. A change is predicted to predict a temporal change of the total risk function R, and based on the temporal change of the total risk function R, a local minimum point ymin (x, t ) Is calculated. Then, an objective function J for each time is created, and the turn control amount u (t) for each time that minimizes the objective function J is calculated as the turn control amount u (t) of the host vehicle 1. The risk function R (t) for each route when the vehicle 1 moves with the turning control amount u (t) for each time is set, and the final avoidance route R is determined from the risk function R (t) for each route. (t) f is selected, steering control is executed based on the turning control amount u (t) of the final avoidance route R (t) f, and the final avoidance route R (t) f is set to the value. Based on this, brake control is executed. For this reason, it is possible to realize the collision avoidance control in consideration of not only the immediate danger but also the risk of coming ahead.

  Further, in the risk function of the vehicle, according to the vehicle speed of the target vehicle, the direction of the host vehicle with respect to the target vehicle, and the line-of-sight direction of the driver of the target vehicle, the degree of recognition of the target vehicle with respect to the host vehicle is set as the recognition rate. Furthermore, the recognition rate set this time is compared with the previously set recognition rate, the recognition rate is variably set, and the risk function of the vehicle is corrected by this recognition rate. Accordingly, since the degree of recognition of the target vehicle with respect to the subject vehicle is accurately reflected in the risk function, it is possible to provide driving assistance to the driver with a natural feeling.

  In the present embodiment, an example in which brake control and steering control can be performed based on the final avoidance route R (t) f has been described, but either one may be performed. .

  Further, the alarm control is performed without setting the brake control or the steering control, for example, by setting a time exceeding a preset threshold in the final avoidance route R (t) f as the alarm start time. Also good.

  Furthermore, the brake control described in the present embodiment is merely an example, and may be used in combination with other well-known brake control, for example, closing of the throttle opening or downshifting in an automatic transmission.

  In the present embodiment, the current total risk function R is set for a white line or a three-dimensional object in front of the host vehicle 1 and the temporal change thereof is predicted. However, the present invention is not limited to this. The setting of the total risk function R and its temporal change may be predicted for the three-dimensional object on the side or rear side of the vehicle 1.

  Furthermore, in the present embodiment, the configuration for generating the avoidance route when the host vehicle 1 moves forward is described. However, the present invention is not limited to this, and the avoidance route is determined when the host vehicle 1 moves backward by recognizing the rear environment of the host vehicle 1. You may make it produce | generate.

  Needless to say, the recognition rate described in the present embodiment can also be applied to a form of a system that obtains and controls other risks (for example, a form that obtains risks from a collision allowance time, an inter-vehicle time, etc.). .

  Further, in the present embodiment, the recognition rate is reflected by the recognition rate correction gain Gr shown in the equation (2), but may be reflected by a gain obtained by another arithmetic expression.

  The recognition rate of the present embodiment is such that the gaze direction of the target vehicle is detected and used by the gaze direction detection device 5, but the face direction is detected, and the face direction is assumed to be the gaze direction. May be calculated.

Schematic configuration diagram of a driving support device mounted on a vehicle Flow chart of driving support control program Flowchart continuing from FIG. Recognition rate correction gain calculation routine Explanatory drawing which shows an example of the risk function set ahead Explanatory drawing of gaze direction angle with respect to own vehicle direction Recognition rate setting map Explanatory diagram of characteristics of recognition rate Explanatory diagram of recognition rate set by predicting changes due to memory Explanatory drawing which shows an example of the avoidance route and turning control amount which are produced | generated

Explanation of symbols

1 self-vehicle 2 driving support device 3 control unit (risk setting means, recognition rate setting means, risk correction means)
4 Stereo image recognition device (traveling environment recognition means)
5 Gaze direction detection device 6 Communication device (traveling environment recognition means)
7 Positioning device 8 Vehicle speed sensor 9 Yaw rate sensor 10 CCD camera 11 Field of view camera 12 Infrared lamp 13 Display 14 Automatic steering control device 15 Automatic brake control device

Claims (5)

A travel environment recognition means for recognizing a travel environment and acquiring at least information of a three-dimensional object outside the vehicle and communicating with another vehicle to acquire information from the other vehicle;
A risk setting means for extracting a target vehicle to be controlled from information of the three-dimensional object recognized by the traveling environment recognition means, and setting a risk for the target vehicle;
The vehicle speed of the target vehicle, the direction of the host vehicle from the target vehicle, and the direction of the driver of the target vehicle are input from the travel environment recognition unit, and the degree of recognition of the target vehicle with respect to the host vehicle according to the input information A recognition rate setting means for setting as a recognition rate;
A risk correcting means for correcting the risk of each target vehicle according to the recognition rate,
The perceived rate setting means compares the currently set perceived rate with the previously set perceived rate, and when the currently set perceived rate is smaller than the previously set perceived rate, a preset rate Lower the previously set cognitive rate and output the reduced cognitive rate as the previously set cognitive rate , and conversely, the currently set cognitive rate is greater than the previously set cognitive rate In this case, the vehicle driving assistance device is characterized in that the recognition rate set this time is output as it is as the previously set recognition rate .   The vehicle driving support apparatus according to claim 1, wherein the recognition rate is set so as to include a relationship in which visual acuity decreases when the vehicle speed increases.   3. The vehicle driving support apparatus according to claim 1, wherein the recognition rate is set so as to include a relationship that a viewing angle becomes narrower as a vehicle speed increases.   4. The vehicle driving support apparatus according to claim 1, wherein the recognition rate is set by predicting a change due to memory. 5.   The vehicle driving support device according to any one of claims 1 to 4, wherein the risk correction unit corrects the risk to be smaller as the value of the recognition rate is larger.

Images