JP5282590B2 - Vehicle driving support device and vehicle driving support method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To properly assist an obstacle avoidance operation performed by a driver. <P>SOLUTION: An obstacle detecting means detects a position of an obstacle around own vehicle. In addition, a means for estimating a distribution of existence probability estimates a distribution of presence probability of an obstacle around the own vehicle in response to a detected position and a characteristic of detected error included in the detected position under a characteristic of the obstacle detecting means. Then, an avoidance assisting means assists an obstacle avoidance operation performed by a driver in response to the estimated distribution of presence probability. Accordingly, in the case that a detected error included in the result of detection, i.e. a detected error of a position of the obstacle is high in response to a characteristic of the obstacle detecting means, for example, it is possible to reduce a presence probability of the obstacle. Thus, it is possible to reduce an amount of assistance of the obstacle avoidance operation. As a result, it is possible to restrict interference of the assistance of the avoidance operation against the obstacle avoidance operation performed by the driver. Accordingly, it becomes possible more properly to perform the obstacle avoidance operation performed by the driver. <P>COPYRIGHT: (C)2010,JPO&amp;INPIT

Description

  The present invention relates to a vehicle driving support device and a vehicle driving support method that support an obstacle avoidance operation by a driver.

Conventionally, as this type of technology, for example, there is a technology described in Patent Document 1.
In the technique described in Patent Document 1, first, the motion state of the host vehicle and the motion state of another vehicle are detected. Next, the predicted arrival position of the own vehicle and the predicted arrival position of the other vehicle after the set time are calculated based on the movement state of the own vehicle and the movement state of the other vehicle. Next, a quadrangle corresponding to the vehicle size of the host vehicle is placed on the map at the predicted arrival position of the host vehicle. Similarly, a quadrangle corresponding to the vehicle size of the other vehicle is arranged at the predicted arrival position of the other vehicle. Then, information such as an alarm is provided to the driver according to the size of the area where each quadrangle overlaps.

JP 2008-65483 A

By the way, in the technique described in Patent Document 1, a quadrangle corresponding to the vehicle size of the other vehicle arranged on the map is enlarged according to the detection error of the traveling state of the other vehicle.
Therefore, for example, when the detection error of the running state of the other vehicle is large, the quadrangle corresponding to the vehicle size of the other vehicle becomes large. Therefore, each quadrangle is likely to overlap, and the alarm generation timing is accelerated. As a result, the driver may feel uncomfortable.
Therefore, it has been difficult to appropriately support the obstacle avoidance operation by the driver.
This invention pays attention to the above points, and makes it a subject to enable it to support obstacle avoidance operation by a driver more appropriately.

  In order to solve the above-mentioned problem, the present invention detects the position of an obstacle around the host vehicle, and based on the detected position and the characteristics of the obstacle detection means, based on the nature of the detection error included in the position, the obstacle Estimate the existence probability distribution of an object. Based on the estimated existence probability distribution, the driver assists the obstacle avoidance operation.

According to the present invention, for example, when the detection error included in the detection result of the obstacle detection unit, that is, the detection error of the position of the obstacle is large, the probability of existence of the obstacle can be reduced. Therefore, the support amount for the obstacle avoidance operation can be reduced. As a result, it is possible to suppress the assistance of the avoidance operation from interfering with the obstacle avoidance operation by the driver.
Therefore, the driver can more appropriately support the obstacle avoidance operation.

It is a conceptual diagram of the apparatus structure of the own vehicle SW equipped with the driving assistance apparatus for vehicles of 1st Embodiment. It is a block diagram which shows the driving assistance device for vehicles of 1st Embodiment. 4 is a flowchart showing processing of the avoidance support controller 4. It is a top view which shows an example of the scene assumed. It is a graph which shows the presence probability distribution of the obstruction SM. It is a graph which shows the presence probability distribution of the obstruction SM. It is a flowchart which shows the process which calculates steering assistance torque. It is a top view for demonstrating the state quantity of the own vehicle SW. It is a conceptual diagram of the apparatus structure of the own vehicle SW equipped with the driving assistance apparatus for vehicles of 2nd Embodiment. It is a block diagram which shows the driving assistance device for vehicles of 2nd Embodiment. 4 is a flowchart showing processing of the avoidance support controller 4. It is a top view which shows an example of the scene assumed. It is a graph which shows the presence probability distribution of the obstruction SM. It is a figure which shows the characteristic of a steering assistance torque. It is a figure which shows an application example. It is a flowchart which shows the calculation method of presence probability distribution of the obstruction SM. It is a figure which shows the extraction method of the index of a control map. It is a graph showing a change state of the lateral velocity v _BY obstacle SM. It is a graph which shows the presence probability distribution of the obstruction SM. It is a graph which shows the presence probability distribution of the obstruction SM. It is a flowchart which shows the process which calculates a right avoidance steering angle pattern. FIG. 6 is a diagram showing a steering angle pattern δ _f (t). It is a block diagram which shows the driving assistance device for vehicles of 3rd Embodiment. 4 is a flowchart showing processing of the avoidance support controller 4. It is a top view which shows an example of the scene assumed. It is a figure which shows the characteristic of a steering assistance torque. It is a flowchart which shows the process which calculates a right avoidance steering angle pattern. It is a conceptual diagram of the apparatus structure of the own vehicle SW equipped with the driving assistance device for vehicles of 4th Embodiment. It is a block diagram which shows the driving assistance device for vehicles of 4th Embodiment. 4 is a flowchart showing processing of the avoidance support controller 4. It is a figure which shows the right avoidance steering angle pattern (delta) _Ri . It is a figure which shows the characteristic of a steering assistance torque. It is a flowchart which shows the process which determines the presence or absence of the possibility that the own vehicle SW and the obstacle SM interfere. It is a flowchart which shows the process which calculates right avoidance steering angle pattern (delta) _Ri .

Next, an embodiment according to the present invention will be described with reference to the drawings.
(First embodiment)
FIG. 1 is a conceptual diagram of a device configuration of a host vehicle SW equipped with the vehicle driving support device of the present embodiment.
(Constitution)
As shown in FIG. 1, the host vehicle SW is a front-wheel steering and front-wheel drive vehicle.
The own vehicle SW is equipped with various sensors as follows. The sensors are a plurality of cameras 1, a vehicle speed sensor 2, and a steering angle sensor 3. These sensors output detection signals to the avoidance support controller 4.

  The camera 1 detects information ahead of the host vehicle SW. For example, a CCD camera or the like attached in front of the vehicle interior can be used. And the presence or absence of the obstacle SM and the position of the obstacle SM are detected by photographing the road situation ahead of the host vehicle SW. The obstacle SM is an object that becomes an obstacle to the traveling of the host vehicle SW. For example, a pedestrian, a vehicle, etc. are mentioned. Further, the distance between the camera 1 and the obstacle SM is also detected by configuring the camera 1 in a stereo format.

The vehicle speed sensor 2 detects the rotational speed of the wheel. For example, a rotary encoder attached to the rear wheels 5RL and 5RR can be used. Then, the rotational speed of the rear wheels 5RL and 5RR is detected by detecting a pulse signal generated in proportion to the rotation of the rear wheels 5RL and 5RR.
The steering angle sensor 3 detects the rotation angle of steering by the driver. For example, a rotary encoder or the like attached to a pinion in a front wheel steering mechanism configured by a rack and pinion system can be used. The rotation angle of the steering wheel 6 is detected by detecting a pulse signal generated in proportion to the rotation of the pinion.

The host vehicle SW includes a steering actuator 7. In response to a signal from the avoidance support controller 4, the steering actuator 7 applies a steering support torque that supports an obstacle avoidance operation by the driver to the steering column 8. For example, hydraulic power steering or electric power steering can be used.
The avoidance support controller 4 includes a microprocessor. The microprocessor includes an A / D conversion avoidance, a D / A conversion avoidance, a central processing unit, an accumulation avoidance composed of a memory and the like. Then, the signals detected by the various sensors are processed according to the program stored in the memory, and the processing results are output to the steering actuator 7.

FIG. 2 is a block diagram summarizing the above-described device configuration by function.
The own vehicle state detection means is constituted by the vehicle speed sensor 2. And the function which acquires the information regarding the motion state of the own vehicle SW by processing the signal detected by the vehicle speed sensor 2 in an integrated manner is realized.
The operation state detection means is constituted by the steering angle sensor 3. And the function which processes the signal detected with the steering angle sensor 3, and acquires the information regarding a driver | operator's operation state is implement | achieved.

The obstacle detection means is configured by the camera 1. And the function which extracts the information regarding the position of the obstruction SM by image-processing the image imaged with the camera 1 is implement | achieved.
The existence probability distribution estimation means is configured by the avoidance support controller 4. And the function which processes the information obtained by the obstacle detection means and estimates the existence probability of the obstacle SM is realized.
The avoidance support means is constituted by the steering actuator 7. Then, based on the information obtained by the own vehicle state detection unit, the operation state detection unit, and the existence probability distribution detection unit, the steering actuator 7 realizes a function that supports the obstacle avoidance operation by the driver.

Next, processing of the avoidance support controller 4 will be described with reference to the drawings.
FIG. 3 is a flowchart showing processing of the avoidance support controller 4.
FIG. 4 is a plan view showing an example of an assumed scene.
Here, in order to make the processing easy to understand by giving concreteness, description will be made assuming the scene shown in FIG. In the scene shown in FIG. 4, it is assumed that an obstacle SM stationary in front of the host vehicle SW is detected while the host vehicle SW is traveling on a straight road. It is assumed that there is a space where the host vehicle SW can pass through on either side of the obstacle SM.

Note that the processing of FIG. 3 is repeatedly executed at a set sampling cycle.
As shown in FIG. 3, first, in step S101, signals detected by various sensors are read into the memory. Then, the movement state of the host vehicle SW, the operation state of the host vehicle SW, the position of the obstacle SM, and the distance between the obstacle SM are calculated as values on a unified coordinate system. The coordinate system can be set appropriately. In this embodiment, as shown in FIG. 4, the X axis is set in the front-rear direction of the vehicle body of the host vehicle SW, and the Y axis is set in the vehicle width direction perpendicular to the X axis. In the description of the present embodiment, the coordinate system sets the origin of the X coordinate system as the attachment position of the camera 1 and sets the origin of the Y coordinate near the center line of the road.

By setting the coordinate system, the position and speed of the host vehicle SW can be expressed in the form of (X _V , Y _V , V _VY , V _VY ) = (0, 0, v _VX , 0). It becomes like this.
Further, the position and speed of the obstacle SM can be expressed in the form of (X _B , Y _B , V _BX , V _BY ) = (x _B , y _B , 0, 0).
The vertical speed v _VX can be approximated by the wheel speed of the non-driven wheels if the horizontal speed v _VY can be considered sufficiently smaller than the vertical speed v _VX . Accordingly, the longitudinal speed v _VX can be obtained from the detection value of the vehicle speed sensor 2 attached to the non-driven wheel.

Further, the vertical position x _B and the horizontal position y _B of the obstacle SM can be acquired from the camera 1.
Further, it is considered that the yaw angle θ and the steering angle δ _f are important physical quantities as state quantities representing the motion state of the host vehicle SW. These physical quantities describing the motion state of the host vehicle SW can be obtained with specific values by processing the detection signals of the sensors as described below.
That is, among these physical quantities, regarding the yaw angle θ, if it is assumed that the road is a straight line, the angle between the road boundary and the direction in which the host vehicle SW faces is the angle of the host vehicle SW taken by the camera 1. It can be obtained by estimating the road condition ahead by image processing.

The steering angle δ can be acquired from the steering angle sensor 3.
Next, in step S102, the process branches depending on the possibility of interference between the host vehicle SW and the obstacle SM. As a method for determining whether or not there is a possibility of interfering with the obstacle SM, for example, the determination is made based on whether or not the obstacle SM enters the traveling area of the host vehicle SW between the present time and after the set time T _CJ. A method is available. The travel area of the host vehicle SW is an area on the road on which the host vehicle SW travels from the present to the time after the set time T _CJ .
Specifically, first, based on the longitudinal speed v _{VX of the host} vehicle SW and the longitudinal position x _B of the obstacle SM, the set time T _CJ is calculated according to the following equation (1).
T _CJ = x _B / v _VX (1)
Next, based on the calculated set time T _CJ , the lateral position y _B (T _CJ ) of the obstacle SM after the set time T _CJ is calculated.

Next, it is determined whether or not the absolute value | y _B (T _CJ ) | of the calculated lateral position y _B (T _CJ ) is less than the set value Y _TH . The set value Y _TH is a threshold value for detecting the intrusion of the obstacle SM into the traveling area of the host vehicle SW. For example, a value larger than half of the total value of the own vehicle SW width and the obstacle SM width can be used. If it is determined that the absolute value | y _B (T _CJ ) | of the lateral position is less than the set value Y _TH, it is determined that the obstacle SM has entered the travel area of the host vehicle SW (YES), The process proceeds to step S103. On the other hand, when it is determined that the absolute value | y _B (T _CJ ) | of the lateral position is equal to or _larger than the set value Y _TH , the obstacle SM does not enter the traveling area of the host vehicle SW (NO), that is, It is determined that it is not necessary to support the obstacle avoidance operation by the driver, and the process is terminated.

Next, in step S103, based on the detection error included in the detection result of the camera 1 according to the detection characteristics of the camera 1, that is, based on the nature of the detection error of the position coordinates (x _B , y _B ) of the obstacle SM. The existence probability distribution of the obstacle SM around the vehicle SW is estimated. The presence probability distribution of the obstacle SM, for example, as shown in FIGS. 5 and _{6, (μ x, μ Y} ) becomes the highest probability, gradually decreases the existence probability with increasing distance from (μ _x, μ _Y) 2 A normal distribution of dimensions can be used. μ _x is an average value in the X-axis direction, and μ _Y is an average value in the Y-axis direction.

5 and 6 are graphs showing the existence probability distribution of the obstacle SM.
Specifically, the camera 1 has a characteristic that the detection error of the position coordinates (x _B , y _B ) of the obstacle SM increases as the obstacle SM is farther away. Therefore, in this embodiment, the vertical position x _B of the obstacle SM is set to the average value μ _X , the horizontal position y _B is set to the average value μ _y , and the distance between the host vehicle SW and the obstacle SM is set. The dispersion values σ _X ² and σ _Y ² are set according to the distance R _s (= (X _B ² + Y _B ² ) ^1/2 ).

Thereby, the maximum value of the existence probability distribution of the obstacle SM and the existence probability around the highest value decrease as the distance R _s between the host vehicle SW and the obstacle SM increases.
Here, the average values μ _X , μ _y and variance values σ _X ² , σ _Y ² are set to the distance R _s between the host vehicle SW and the obstacle SM by setting the (R, θ) coordinate system. Can be expressed as a function. Therefore, the existence probability distribution f (X, Y, 0) of the obstacle SM has functions μ _X (R _s ), μ _Y (R _s ), σ _Y (R _s ) ² , σ _X (R _s ) ^2. Can be expressed as the following equation (2).
f (X, Y, 0) = N (μ _X (R _s ), μ _Y (R _s ), σ _x (R _s ) ² , σ _Y (R _s ) ² ) ……… (2)

The functions μ _X (R _s ), μ _Y (R _s ), σ _Y (R _s ) ² , and σ _X (R _s ) ² are obstacles to the distance R _s estimated at the design stage of the vehicle driving support device. This is set using the distribution of SM existence probabilities.
Next, in step S104, the steering assist torque is calculated based on the existence probability distribution f (X, Y, 0) of the obstacle SM and the future travel route of the host vehicle SW. Then, a signal instructing application of steering assist torque is output to the steering actuator 7.

Next, the calculation method of the steering assist torque performed in step S104 will be described with reference to the drawings.
FIG. 7 is a flowchart showing a process for calculating the steering assist torque.
As shown in FIG. 7, first, in step S201, a future travel route of the host vehicle SW is calculated.
Specifically, first, in order to calculate the future travel route of the host vehicle SW, a model describing the motion of the host vehicle SW is introduced. Here, a two-wheel model that approximates the movement of a four-wheel vehicle by the movement of a two-wheel vehicle is used as a model that describes the movement of the host vehicle SW.

In this two-wheel model, the running speed is constant, the yaw angle θ is sufficiently small, the lateral speed v _VY is sufficiently smaller than the longitudinal speed v _VX, and the tire lateral force saturation characteristic does not appear remarkably. In the state, it can be approximated as the following equation (3).
dx _V / dt = vcos (β + θ)
dy _V / dt = vsin (β + θ) ……… (3)
dθ / dt = γ
dβ / dt = -γ + [2K _f α _f + 2K _r α _r ] / MV _V
dγ / dt = [2L _f K _f α _f -2L _r K _r α _r ] / I
α _f = β + L _f γ / V _V -δ _f / N _G
α _r = β-L _f γ / V _V

FIG. 8 is a plan view for explaining state quantities of the host vehicle SW.
However, as shown in FIG. 8, β is a slip angle and γ is a yaw rate. M is the vehicle mass, I is the vehicle yaw moment of inertia, Lf is the distance from the vehicle center of gravity to the front wheel axis, and Lr is the distance from the vehicle center of gravity to the rear wheel axis. Further, Kf is a front wheel cornering power coefficient, and Kr is a rear wheel cornering power coefficient. Α _f is the tire slip angle of the front wheels 5FL and 5FR, and α _r is the tire slip angle of the rear wheels 5RL and 5RR. Furthermore, _NG is a gear ratio when the steering angle δ _f is converted into the turning angles of the front wheels 5FL and 5FR.

Moreover, said (3) Formula can be put together like following (4) Formula.
dx _V / dt = f _V (x _V , u _V ) (4)
x _V = (x _V , y _V , θ, β, γ)
u _V = (δ _f )
Using a two-wheel model and the steering angle [delta] _f introduced as described above, as to predict the state vector x _V between now and after the set time _{T T (t), t =} 0, Δt, 2Δt, ... , T _T −Δt and T _T , the state vector x _V (t) is calculated according to the above equation (4). The time series of the calculated state vector x _V (t) represents the future travel route of the host vehicle SW.

Next, in step S202, the maximum value P _m of the presence probability of the obstacle SM is extracted from the future travel route of the host vehicle SW calculated in step S201.
Specifically, first, referring to the existence probability distribution of the obstacle SM set in step S103, each point (x _V (t), y on the future travel route of the host vehicle SW calculated in step S201 is calculated. _V (t)), the presence probability of the obstacle SM is calculated. Then, the maximum one of the calculated existence probabilities is extracted as the maximum value P _m (= maxf (x _V (t), y _V (t), t)).

Next, in step S203, it performs the maximum value P _m of the existence probability of the obstacle computed SM, the leveling of determining whether the maximum value P _m is large or small. The most simple method, may be set the level of the pre-existence probability in several steps, it can be considered a method for determining whether belongs to which level the maximum value P _m.
Specifically, first, it is determined whether the maximum value P _m of the presence probability of the obstacle SM set in step S202 belongs to a plurality of preset levels.

Next, it is determined whether or not the determined level is _{equal to} or higher than the set level P _mTH . The set level P _mTH is a threshold value for determining whether the maximum value P _m of the presence probability of the obstacle SM is large or small. Then, it is determined if it is determined that the set level P _MTH or more, a large maximum value P _m of the existence probability of an obstacle SM. On the other hand, when it is determined that it is less than the set level P _mTH, it is determined that the maximum value P _m is small.

Next, in step S204, a steering assist torque is set based on the leveling result.
Specifically, first, when it is determined in step S203 that the maximum value P _m of the presence probability of the obstacle SM is large, it is determined that the host vehicle SW travels in an area where the presence probability of the obstacle SM is high, The absolute value of the steering assist torque is set to a relatively large value T _HIGH . On the other hand, if it is determined in step S203 that the maximum value P _m of the presence probability of the obstacle SM is small, it is determined that the host vehicle SW travels in a region where the presence probability of the obstacle SM is low, and the absolute value of the steering assist torque is determined. Set the value to a relatively small value T _LOW (<T _HIGH ).

Then, to calculate the steering angle [delta] _f 'obtained by increasing off steering angle [delta] _f. Then, based on the calculated steering angle δ _f ′, the flow of steps S201 and S202 is executed to calculate the maximum value P _m (δ _f ′) of the presence probability of the obstacle SM.
Next, it is determined whether or not the calculated maximum value P _m (δ _f ′) is smaller than the maximum value P _m (δ _f ) of the existence probability of the obstacle SM. If the value is smaller than the maximum value P _m (δ _f ), the direction of the steering assist torque is increased and set to the increasing direction. On the other hand, if the value is equal to or greater than the maximum value P _m (δ _f ), the direction of the steering assist torque is set to the switchback direction.
Next, a signal instructing application of the set steering assist torque is output to the steering actuator 7.

(Operation)
Periodically, based on signals from each sensor, the movement state, obstacle information, and road environment information of the host vehicle SW are acquired and converted into information developed on the same coordinates. The existence probability distribution of the obstacle SM around the host vehicle SW is estimated based on the information developed on the same coordinates.
Subsequently, the future travel route of the host vehicle SW is calculated. Then, the maximum value P _m of the existence probability at a position where the existence probability of the obstacle SM is highest in the future travel route is extracted.
Subsequently, it is determined whether the extracted maximum value P _m is large or small. If it is determined that the maximum value P _m is large, a signal instructing _application of a relatively large steering assist torque T _HIGH is output to the steering actuator 7. On the other hand, if it is determined that the maximum value P _m is small, a signal instructing _application of a relatively small steering assist torque T _LOW is output to the steering actuator 7.

The above is repeated periodically.
Thereby, the steering assist torque at each time can be calculated and controlled. As a result, it is possible to realize assistance for the obstacle avoidance operation by the driver.
In this embodiment, when the obstacle SM is detected far away from the host vehicle SW, the detection error increases due to the detection characteristics of the camera 1. For this reason, the existence probability distribution of the obstacle SM is expanded in a planar shape, and the existence probability of the obstacle SM is lowered. Therefore, in a scene where there is a margin for interference between the host vehicle SW and the obstacle SM, the amount of support is such that the driver's operation is not hindered.
Here, in this embodiment, the vehicle speed sensor 2 of FIG. 2 comprises the own vehicle state detection means. Similarly, the steering angle sensor 3 in FIG. 2 constitutes an operation state detecting means. Further, the camera 1 constitutes an obstacle detection unit. Further, the avoidance support controller 4 of FIG. 2 constitutes existence probability distribution estimation means.

(Effect of this embodiment)
(1) In the present embodiment, the obstacle detection means detects the position of the obstacle around the host vehicle. The existence probability distribution estimation means estimates the existence probability distribution of the obstacle around the host vehicle based on the detected position and the characteristics of the obstacle detection means based on the nature of the detection error included in the position. Then, the avoidance support means supports the obstacle avoidance operation by the driver based on the estimated existence probability distribution.

Therefore, for example, when the detection error included in the detection result due to the characteristic of the obstacle detection means, that is, the detection error of the position of the obstacle is large, the probability of existence of the obstacle can be reduced. Therefore, the support amount for the obstacle avoidance operation can be reduced. As a result, it is possible to suppress the assistance of the avoidance operation from interfering with the obstacle avoidance operation by the driver.
Therefore, the driver can more appropriately support the obstacle avoidance operation.

(2) In addition, when the existence probability distribution estimation means has a long distance between the host vehicle and the obstacle, the highest value of the obstacle existence probability distribution and the highest value of the obstacle are compared with the case where the distance is short. Reduce the existence probability of the surroundings.
Therefore, for example, when the obstacle detection means has a characteristic that the detection error included in the detection result increases as the obstacle is further away, when the distance between the own vehicle and the obstacle is far away, The existence probability can be reduced. Therefore, it is possible to prevent erroneous detection that an obstacle is present at a position different from the actual position and erroneously determining that there is a high possibility of interference between the detected obstacle and the host vehicle.

(Second Embodiment)
Next, a second embodiment of the present invention will be described with reference to the drawings. In addition, the same code | symbol is used about the same structure as said each embodiment.
The basic vehicle configuration of this embodiment is the same as that of the first embodiment as shown in FIG.
However, a gyro sensor 9 is provided. The gyro sensor 9 detects a roll rate, a pitch rate λ, and a yaw rate γ generated at the center of gravity position of the host vehicle SW. For example, it is possible to use the one configured using a crystal resonator or a semiconductor. Then, the detection signal is output to the avoidance support controller 4.

FIG. 10 shows a block diagram in which the above-described device configuration is summarized by function.
In the present embodiment, the host vehicle state detection means is constituted by the vehicle speed sensor 2 and the gyro sensor 9. And the signal which detected the vehicle speed sensor 2 and the gyro sensor 9 is processed in an integrated manner, and the function which acquires the information regarding the motion state of the own vehicle SW is implement | achieved.
The avoidance support controller 4 also serves as an avoidance route generation unit. And the function which produces | generates an avoidance path | route is implement | achieved based on the information obtained by the presence probability distribution detection means.
The avoidance support means realizes a function of supporting the obstacle avoidance operation by the driver by the steering actuator 7 based on the information obtained by the operation state detection means and the avoidance route calculation means.

Next, processing of the avoidance support controller 4 will be described with reference to the drawings.
FIG. 11 is a flowchart showing the process of the avoidance support controller 4.
FIG. 12 is a plan view showing an example of an assumed scene.
Here, in order to make the processing easy to understand by giving concreteness, description will be made assuming the scene shown in FIG. The scene shown in FIG. 12 assumes a scene in which a pedestrian crossing from the left in front of the host vehicle SW is detected when the host vehicle SW is traveling on a straight road. It is assumed that there is a space where the host vehicle SW can pass through on either side of the obstacle SM.
Note that the processing in FIG. 11 is repeatedly executed at the set sampling cycle.

As shown in FIG. 11, first, in step S301, signals detected by various sensors are read into a memory. Then, the movement state of the host vehicle SW, the operation state of the host vehicle SW, the position of the obstacle SM, and the distance between the obstacle SM are calculated as values on a unified coordinate system.
By setting the coordinate system, the position and speed of the host vehicle SW can be expressed in the form (X _V , Y _V , V _VX , V _VY ) = (0, 0, v _VX , 0). It becomes like this.

Further, the position and speed of the obstacle SM can be expressed in the form of (X _B , Y _B , V _BX , V _BY ) = (x _B , y _B , 0, v _BY ).
The lateral speed v _BY of the obstacle SM can be obtained from the camera 1.
The pitch rate λ and the yaw rate γ can be obtained from the gyro sensor 9.
Next, in step S302, the process branches depending on whether or not the own vehicle SW and the obstacle SM may interfere with each other. As a method for determining whether or not there is a possibility of interfering with the obstacle SM, for example, the determination is made based on whether or not the obstacle SM enters the traveling area of the host vehicle SW between the present time and after the set time T _CJ. A method is available.

Specifically, first, based on the longitudinal speed v _{VX of the host} vehicle SW and the longitudinal position x _B of the obstacle SM, the set time T _CJ is calculated according to the equation (1).
Then, based on the set time T _CJ calculated, the lateral position y _B (T _CJ) of the obstacle SM after the set time T _CJ + v to calculate the _BY × T _CJ.
Next, it is determined whether or not the absolute value of the calculated lateral position y _B (T _CJ ) + v _BY × T _CJ | y _B (T _CJ ) + v _BY × T _CJ | is less than the set value Y _TH. To do. If it is determined that the absolute value | y _B (T _CJ ) + v _BY × T _CJ | of the lateral position is less than the set value Y _TH , the obstacle SM enters the traveling area of the host vehicle SW. Determination is made (YES), and the process proceeds to step S303. On the other hand, the absolute value of the lateral position _{| y B (T CJ) +} v BY × T CJ | when it is determined that is the set value Y _TH or more, the traveling area of the own vehicle SW obstacle SM does not enter (NO), that is, it is determined that it is not necessary to support the obstacle avoidance operation by the driver, and the process ends.

Next, in step S303, the existence probability distribution of the obstacle SM around the host vehicle SW is estimated based on the nature of the detection error included in the detection result of the camera 1 based on the detection characteristics of the camera 1. The presence probability distribution of the obstacle SM, for example, as shown in FIG. 13 (a) (b), becomes the highest probability (μ _x, μ _Y), the presence probability with increasing distance from (μ _x, μ _Y) A gradually decreasing two-dimensional normal distribution can be used.

FIG. 13 is a graph showing the existence probability distribution of the obstacle SM.
Specifically, the camera 1, as the motion in the pitch direction of the vehicle SW is large, there is a characteristic that the detection error in the longitudinal position x _B of the obstacle SM is increased. Therefore, in the present embodiment, when the host vehicle SW is not performing a pitch motion (λ = 0), the vertical position x _B of the obstacle SM is set to the average value μ _X. On the other hand, when the host vehicle SW is performing pitch motion (λ ≠ 0), a value different from the longitudinal position x _B of the obstacle SM is set to the average value μ _X , and the host vehicle SW performs pitch motion. The variance value σ _X ² is set to a relatively large value as compared with the case where it is not.

Thereby, the greater the movement of the host vehicle SW in the pitch direction, the lower the maximum value of the presence probability distribution of the obstacle SM in the vertical direction of the host vehicle SW and the presence probability around the maximum value.
The camera 1 has a characteristic that the detection error of the lateral position y _B of the obstacle SM increases as the movement of the host vehicle SW in the yaw direction increases. Therefore, in this embodiment, when the host vehicle SW is not performing yaw motion (γ = 0), the lateral position y _B of the obstacle SM is set to the average value μ _Y. On the other hand, when the host vehicle SW is performing yaw motion (γ ≠ 0), a value different from the lateral position y _B of the obstacle SM is set to the average value μ _X , and the host vehicle SW performs yaw motion. The variance value σ _Y ² is set to a relatively large value as compared with the case where it is not.

As a result, the greater the movement of the host vehicle SW in the yaw direction, the lower the maximum value of the obstacle probability distribution of the obstacle SM in the lateral direction of the host vehicle SW and the presence probability around the maximum value.
In the present embodiment, the example in which the existence probability distribution of the obstacle SM is estimated based on the pitch rate λ and the yaw rate γ of the host vehicle SW has been described, but other configurations may be employed. For example, an acceleration sensor for detecting the acceleration of the host vehicle SW is provided, and the presence probability distribution of the obstacle SM is obtained by using the vertical acceleration and the lateral acceleration of the host vehicle SW instead of the pitch rate λ and the yaw rate γ. Can also be estimated.

Next, in step S304, first, an avoidance route that passes in the right direction of the obstacle SM is generated based on the existence probability distribution of the obstacle SM estimated in step S303. The avoidance route is a route through which the host vehicle SW passes through an area where the existence probability of the obstacle SM is lower than the level value TH _i . Each set level TH _i is set so that TH ₁ > TH ₂ >...> TH _NTH . That is, the setting level TH _i is set to a smaller value as the variable i is larger.

Next, a set of steering angles δ _f (hereinafter also referred to as “right avoidance steering angle pattern”) δ _Ri (i = 1 to N _TH ) for traveling along the generated avoidance route is calculated. The setting level TH _i, because a smaller value as the variable i is greater, the right avoidance steering angle pattern [delta] _Ri to generate in response to the set level TH _i is becomes a larger value as the variable i. A specific calculation method will be described later.
Next, in step S305, first, an avoidance route that passes in the left direction of the obstacle SM is generated based on the probability distribution of the obstacle SM estimated in step S303.

Next, a set of steering angles δ _f (hereinafter also referred to as “left avoidance steering angle pattern”) δ _Li for traveling on the generated avoidance route is calculated.
Note that the left avoidance steering angle pattern δ _Li also increases as the variable i increases.
Next, in step S306, the characteristics of the steering assist torque are set based on the right avoidance steering angle pattern δ _Ri calculated in step S304 and the left avoidance steering angle pattern δ _Li calculated in step S305.

FIG. 14 is a diagram showing the characteristics of the steering assist torque when N _TH is 2. As shown in FIG.
Specifically, first, the first assist torque characteristic diagram is set based on the right avoidance steering angle pattern δ _Ri calculated in step S304. The first support torque characteristic diagram is a diagram showing the relationship between the steering assist torque and the steering angle [delta] _f. For example, as shown in FIG. 14 (a), according to the steering angle [delta] _f, available diagram representing the steering assist torque applied to the steering column 8. The first steering assist torque characteristic diagram that consists of steering assist torque T _Ri corresponding to the right avoidance steering angle pattern [delta] _Ri and the right avoidance steering angle pattern _{δ Ri (δ Ri, T Ri} ) and on the two-dimensional plane Plotting is performed by connecting the plotted points (δ _Ri , T _Ri ) with straight lines. The steering assist torque T _Ri is a steering assist torque set in association with the right avoidance steering angle pattern δ _Ri . Each steering assist torque T _Ri is set such that T _R1 > T _R2 >...> T _RNTH (= 0).

Next, a second assist torque characteristic diagram is set based on the left avoidance steering angle pattern δ _Li calculated in step S305. The second support torque characteristic diagram is a diagram showing the relationship between the steering assist torque and the steering angle [delta] _f. For example, as shown in FIG. 14B, a point (δ _Li , T _Li ) consisting of a left avoidance steering angle pattern δ _Li and a steering assist torque T _Li corresponding to the left avoidance steering angle pattern δ _Li is placed on a plane. Plotting is performed by connecting the plotted points (δ _Li , T _Li ) with a straight line. The steering assist torque T _Li is a steering assist torque set in association with the left avoidance steering angle pattern δ _Ri . The steering assist torque T _Li corresponding to each left avoidance steering angle pattern δ _Li is set so that T _L1 <T _L2 <... <T _LNTH (= 0).

Then, based on the first and second support torque characteristic diagram set, it calculates the steering assist torque corresponding to the steering angle [delta] _f. Then, a signal instructing application of the calculated steering assist torque is output to the steering actuator 7.
As described above, in step S306, the steering assist torque is weakened when the driver is steering so that the host vehicle SW travels in an area where the presence probability of the obstacle SM is low. As a result, a steering torque that does not interfere with the steering operation is applied to a driver who intends to steer. For this reason, it is possible to provide assistance mainly based on the driver's operation.

FIG. 15 is a diagram illustrating an application example.
In the present embodiment, based on the relationship between the steering assist torque and the steering angle [delta] _f, although an example of calculating the steering assist torque, it is possible to employ other configurations. For example, as shown in FIGS. 15A and 15B, the steering assist torque can be calculated based on the relationship between the steering angular velocity dδ _f / dt (= δ _vf ) and the steering assist torque. As a result, it is possible to provide torque support to the driver by making the driver's steering intention based on the steering angular velocity dδ _f / dt.
In addition, by using both the steering angle δ _f and the steering angular velocity dδ _f / dt, the driver's intention to steer can be achieved, so that the driver's intention to avoid can be grasped more accurately.

Next, a method for estimating the presence probability distribution of the obstacle SM performed in step S303 will be described with reference to the drawings.
FIG. 16 is a flowchart showing a method for calculating the presence probability distribution of the obstacle SM.
In the present embodiment, as the calculation method of the existence probability distribution of the obstacle SM, a method of mapping with four axes of vertical distance, horizontal distance, pitch rate, and yaw rate, and a function of vertical distance, horizontal distance, pitch rate, and yaw rate are used. A combination of methods for expressing an average value and a variance value is used.
As shown in FIG. 16, first, in step S401, an index corresponding to the position coordinates (x _B , y _B ) of the obstacle SM is extracted with reference to the control map. The control map uses the position coordinates (X ′ _B , Y ′ _B ) as an index, and the variance value σ when the distance between the host vehicle SW and the position coordinates (X ′ _B , Y ′ _B ) is R _s. It is a map showing _x (R _s ) ² and σ _Y (R _s ) ² . For example, map data stored in a memory in advance can be used.

FIG. 17 is a diagram illustrating a method of extracting a control map index.
Specifically, as shown in FIG. 17, the one closest to the position coordinate (x _B , y _B ) of the obstacle SM is extracted from the index (X ′ _B , Y ′ _B ) of the control map.
Next, in step S402, the control map is referred to, and dispersion values σ _X ² and σ _Y ² corresponding to the extracted indexes (X ′ _B , Y ′ _B ) are acquired.

Also, the index (X _'B, Y' _B) X component X 'of the _B and the average value mu _X, the index (X' set _B, Y and _{B 'B)} Y component Y' of the average value mu _Y.
In step S403, the dispersion values σ _X ² and σ _Y ² and the average values μ _X and μ _Y acquired in step S402 are corrected based on the pitch rate λ and the yaw rate γ.
Specifically, based on the pitch rate λ and the yaw rate γ, the average values μ _X and μ _Y and the dispersion values σ _X ² and σ _Y ² are corrected according to the following equation (5).
μ _X → μ _X + Δμ _X (λ)
μ _Y → μ _Y + Δμ _Y (γ)
σ _X ² → σ _X ² + Δσ _X ² (λ)
σ _Y ² → σ _Y ² + Δσ _Y ² (γ) (5)
0 ≦ Δσ _X ² (λ), Δσ _Y ² (γ) ≦ 1

However, Δμ _X (λ) is a function representing an offset between the vertical position x _B of the obstacle SM and the average value μ _X according to the pitch rate λ. Further, Δσ _X ² (λ) is a function representing an increase amount of the dispersion value σ _X ² according to the pitch rate λ. Similarly, Δμ _Y (λ) is a function representing an offset between the lateral position yB of the obstacle SM and the average value μ _Y according to the yaw rate γ. Further, Δσ _Y ² (γ) is a function representing an increase amount of the dispersion value σ _Y ² according to the yaw rate γ.
The functions Δμ _X (λ), Δμ _Y (γ), Δσ _X ² (λ), and Δσ _Y ² (γ) are motions in the pitch direction and the yaw direction estimated at the design stage of the vehicle driving support device. It is set using the distribution of the existence probability of the obstacle SM at the time.

Further, based on the corrected μ _X , μ _Y , σ _x ² , and σ _Y ² , the existence probability distributions f _X (X, 0) and f _Y (Y, 0) of the obstacle SM at t = 0 are as follows: It can be expressed as (6).
f _X (X, 0) = N (μ _X , σ _x ² )
f _Y (Y, 0) = N (μ _Y , σ _Y ² ) (6)
However, N (μ _X , σ _x ² ) is a one-dimensional normal distribution having an average value μ _X and a variance value σ _X ² , and N (μ _Y , σ _Y ² ) is an average value μ _Y , a variance value. This is a one-dimensional normal distribution of σ _Y ² .
By the processing so far, the existence probability based on the detection error of the stationary obstacle SM is obtained.

Next, in step S404, it is determined whether or not the obstacle SM is moving.
The existence probability with respect to the stationary obstacle SM described in step S403 is calculated as a probability density function. Becomes complicated. For this reason, in the following description regarding step S403, not a mathematically derived probability density function but one defined from a practical viewpoint is used as the existence probability for the moving obstacle SM.

Specifically, first, it is determined whether or not both the vertical speed v _BX and the horizontal speed v _{BY of} the obstacle SM are less than the set value v _TH . The set value v _TH is a threshold value for determining whether or not the obstacle SM is moving. For example, a relatively small value near “0” can be used. If it is determined that both the vertical speed v _BX and the horizontal speed v _BY of the obstacle SM are less than the set value v _TH, it is determined that the obstacle SM is stationary (YES), and processing Exit and return. On the other hand, if it is determined that at least one of the vertical speed v _BX and the horizontal speed v _BY is greater than or equal to the set value v _TH, it is determined that the obstacle SM is moving (NO), and the process proceeds to step S405. Transition.

In the scene shown in FIG. 12, since the pedestrian who is the obstacle SM crosses the road, the determination in step S404 is “NO”, and the process proceeds to step S405.
Next, in step S405, the existence probability distribution of the obstacle SM is corrected in consideration of the detection error included in the lateral speed v _BY of the obstacle SM.
Here, it is assumed that the obstacle SM is moving at a constant speed, and the maximum estimated portion v ′ _BYmax and the minimum estimated portion v ′ _BYmin of the moving speed are calculated using the speed information before the present.

FIG. 18 is a graph showing a change state of the lateral speed v _BY of the obstacle SM.
As shown in FIG. 18, the moving speed of the obstacle SM is acquired at predetermined time intervals in a form including detection errors. Then, the maximum value v _BYmax and the minimum value v _BYmin are recorded in the memory among the lateral speed v _BY of the obstacle SM between the present time and before the set time T _CJ . Then, based on the recorded maximum value v _BYmax and minimum value v _BYmin , the maximum estimated portion v ′ _BYmax and the minimum estimated portion v ′ _BYmin of the moving speed are calculated according to the following equation (7).
v ' _BYmax = (v _BYmax + v _BYmin ) / 2 + κ [v _BYmax- (v _BYmax + v _BYmax ) / 2]
v ' _BYmax = (v _BYmax + v _BYmin ) / 2-κ [v _BYmax- (v _BYmax + v _BYmax ) / 2] ……… (7)
0 ≦ κ ≦ 1
Where κ is a design parameter. When κ is 1, V ′ _BXmax = V _BXmax and V ′ _Bymax = v _{BYmax and} the value in the memory is used as it is.

19 and 20 are graphs showing the existence probability distribution of the obstacle SM.
As shown in FIG. 19A, when the obstacle SM moves by the maximum estimated amount v ′ _BYmax , the average value μ _Ymax of the existence probability distribution of the obstacle SM after t seconds from the present is calculated in the above-described step S403. in based on the average value mu _Y corrected, can be expressed as follows (8).
μ _Ymax = μ _Y + t × v _BYmax (8)

Further, when the obstacle SM moves at the minimum estimated amount v ′ _BYmin of the lateral velocity V _BY , the average value μ _Ymin of the existence probability distribution of the obstacle SM after t seconds from the present is obtained in step S403. Based on the corrected average value μY, it can be expressed as the following equation (9).
μ _Ymin = μ _Y + t × v _BYmin (9)
Here, it is assumed that the variance values σ _x ² and σ _Y ² do not change even when the obstacle SM moves.

Next, a one-dimensional normal distribution f _{min of} the calculated average value μ _Ymin and variance value σ _Y ^{2 and} a one-dimensional normal distribution f _max of the calculated average value μ _Ymax and variance value σ _Y ² are set.
Next, as shown in FIG. 19B, the maximum values of both normal distributions are complemented so that the maximum values of the set normal distributions f _min and f _max are connected by a straight line. The existence probability distribution f _Y (Y, t) of the obstacle SM after complementation can be expressed as the following equation (10).
f _Y (Y, t) = N (μ _Ymin , σ _Y ² ), Y ≦ μ _Ymin
f _Y (Y, t) = max [f _Y (μ _Ymin , T _CJ )], f _Y (μ _Ymax , T _CJ )], μ _Ymin <Y <μ _Ymax
f _Y (Y, t) = N (μ _Ymax , σ _Y ² ), μ _Ymin ≦ Y (10)

When the obstacle SM is moving in the Y-axis direction, the existence probability is estimated by the above equation (10).
In the scene shown in FIG. 12, since the obstacle SM has not moved in the X-axis direction, the above equation (10) can be expressed as the following equation (11).
f _Y (X, T _CJ ) = N (μ _X , σ _X ² ) (11)

Further, by summarizing the above equations (10) and (11), the existence probability distribution of the obstacle SM can be expressed as the following equation (12).
f (X, Y, t) = N (μ _X , μ _Ymin , σ _X ² , σ _Y ² ) / K _Now , Y ≦ μ _Ymin
f (X, Y, t) = max [f _Ya (μ _X , μ _Ymin , σ _X ² , σ _Y ² ), f _Ya (μ _X , μ _Ymax , σ _X ² , σ _Y ² )] / K _Now , Μ _Ymin <Y <μ _Ymax
f (X, Y, t) = N (μ _X , μ _Ymax , σ _X ² , σ _Y ² ) / K _Now , μ _Ymin ≦ Y (12)
Here, K _NOW is a coefficient for normalizing the existence probability distribution of the obstacle SM.
Here, the case where the obstacle SM is moving in the Y-axis direction has been described based on the scene shown in FIG. 12, but the same method is also used when the obstacle SM is moving in the X-axis direction. Thus, it is possible to estimate the existence probability distribution of the obstacle SM in the future.

Next, the calculation method of the right avoidance steering angle pattern δ _Ri performed in step S304 will be described with reference to the drawings.
Figure 21 is a flowchart illustrating a process for calculating the right avoidance steering angle pattern [delta] _f.
As shown in FIG. 21, first, in the step S501, it determines whether the variable i is set value N _TH. The variable i is a variable for counting the number of executions of a flow from steps S502 to S505 described later, and is set to 1 at the start of this process. If it is determined that the variable i is the set value _NTH (YES), the process is terminated and the process returns. On the other hand, if it is determined that the variable i is not the set value N _TH (NO), the process proceeds to step S502.

Next, in step S502, a steering angle pattern δ _f (t) is set. As the rudder angle pattern δ _f (t), for example, as shown in FIG. 22, a rudder angle pattern or a constant rudder angle in which there is an increase operation and a return operation can be used. An example of using a steering pattern as shown in FIG.
Specifically, first, a small positive constant value Δδ is set as the steering angle pattern δ _f (t). The sign of the steering angle pattern δ _f (t) is a positive value for the steering angle for turning the front wheels 5FL, 5FR to the right, and a negative value for the steering angle for turning the front wheels 5FL, 5FR to the left.

Next, in step S503, when the vehicle is steered with the steering angle pattern δf (t) set in step S502, the host vehicle SW travels only in a region where the presence probability of the obstacle SM is lower than the set level TH _i. It is determined whether or not it is possible.
Specifically, first, based on the steering angle pattern δf (t), the future travel route (x _V (t), y _V (t )) Is calculated.

Next, with reference to the existence probability distribution of the obstacle SM set in step S303, for each point on the calculated future travel route (x _V (t), y _V (t)) of the host vehicle SW. The existence probability of the obstacle SM is calculated. Then, the maximum value is extracted as the maximum value P _m (= maxf (x _V (t), y _V (t), t)) from the calculated existence probability.
Next, it is determined whether or not the calculated maximum probability P _{m of} the obstacle SM is smaller than the set level TH _i . If it is determined that the vehicle is smaller than the set level TH _i , it is determined that an avoidance route that can travel only in an area where the presence probability of the obstacle SM is lower than the set level TH _i is generated (YES). The process proceeds to step S505. On the other hand, when it is determined that the set level TH _i above, it is determined that the existence probability of an obstacle SM is avoidance path that passes through the region higher than the set level TH _i (NO), the process proceeds to step S504 .

Next, in step S504, the steering angle pattern δ _f (t) is generated according to the following equation (13) so as to generate an avoidance route that can travel only in an area where the presence probability of the obstacle SM is lower than the set level TH _i. To reset. Thereafter, the process proceeds to step S503.
δ _f (t) = δ _f (t) + Δδ (13)
On the other hand, in step S505, the steering angle pattern δ _f (t) set in step S502 is stored in the memory as the right avoidance steering angle pattern δ _Ri . A value obtained by adding “1” to the variable i is set as a new variable i. Thereafter, the process proceeds to step S501.
Note that the calculation method of the left avoidance steering angle pattern δ _Li performed in step S305 is the same as the calculation method of the right avoidance steering angle pattern δ _Ri .
However, Δδ is set to a negative value. The steering angle pattern δ _f (t) set in step S502 is stored in the memory as the left avoidance steering angle pattern δ _Li .

(Operation)
By repeating the above processing every predetermined control cycle, the steering assist torque at each time can be calculated and controlled. As a result, an obstacle avoidance operation support by the driver can be realized.
In this embodiment, when the host vehicle SW is performing a yaw motion or a pitch motion, the detection error is increased due to the detection characteristics of the camera 1. Therefore, the existence probability distribution of the obstacle SM is expanded in a plane to reduce the existence probability of the obstacle SM. Therefore, in a scene where there is a margin for interference between the host vehicle SW and the obstacle SM, the amount of support is such that the driver's operation is not hindered.
Here, in the present embodiment, the vehicle speed sensor 2 of FIG. 10 constitutes the own vehicle state detection means. Similarly, the steering angle sensor 3 of FIG. 10 constitutes an operation state detecting means. Further, the camera 1 constitutes an obstacle detection unit. Furthermore, the avoidance support controller 4 of FIG. 10 constitutes an existence probability distribution estimation unit and an avoidance route generation unit. Further, the steering assist torque constitutes an assist control amount.

(Effect of this embodiment)
(1) In the present embodiment, when the existence probability distribution estimation means has a large movement based on at least one of the movement in the lateral direction and the movement in the yaw direction of the host vehicle, it is compared with the case in which the movement is small. Thus, the highest value of the obstacle existence probability distribution in the lateral direction of the host vehicle and the existence probability around the highest value are reduced.
Therefore, for example, when the obstacle detection means has a characteristic that the detection error included in the detection result increases as the movement of the host vehicle in the lateral direction increases, the movement of the host vehicle in the lateral direction is large. , The probability of obstacles can be reduced. Similarly, when there is a characteristic that the detection error included in the detection result increases as the movement of the host vehicle in the yaw direction increases, the probability of the obstacle being present is determined when the movement of the host vehicle in the yaw direction is large. Can be reduced. Therefore, it is possible to prevent erroneous detection that an obstacle is present at a position different from the actual position and erroneously determining that there is a high possibility of interference between the detected obstacle and the host vehicle. As a result, it is possible to prevent the driver from feeling uncomfortable.

(2) In addition, when the existence probability estimation means is large based on at least one of the movement in the vertical direction and the movement in the pitch direction of the own vehicle, the own vehicle is compared with the case where the movement is small. The maximum value of the obstacle existence probability distribution in the vertical direction and the existence probability around the highest value are reduced.
Therefore, for example, when the obstacle detection means has a characteristic that the detection error included in the detection result increases as the movement of the host vehicle in the vertical direction increases, when the movement of the host vehicle in the vertical direction is large, Obstacle existence probability can be reduced. Similarly, when there is a characteristic that the detection error included in the detection result increases as the movement of the host vehicle in the pitch direction increases, the probability of obstacles is reduced when the movement of the host vehicle in the pitch direction is large. it can. Therefore, it is possible to prevent erroneous detection that an obstacle is present at a position closer to the actual position and erroneously determining that there is a high possibility of interference between the detected obstacle and the host vehicle. As a result, it is possible to prevent the driver from feeling uncomfortable.

(3) Furthermore, when the avoidance support means has a high probability of existence of an obstacle on the future travel route of the host vehicle, the support control amount for supporting the obstacle avoidance operation is compared with the case where the existence probability is low. Increase.
Therefore, for example, when the vehicle enters an area where the existence probability of an obstacle is high, the support amount for the avoidance operation can be increased. As a result, the possibility that the host vehicle interferes with the obstacle can be reduced, and interference between the host vehicle and the obstacle can be more appropriately avoided.

(4) Further, the vehicle operation detection means detects at least one of the steering angle and the steering angular velocity of the host vehicle.
Therefore, the obstacle avoidance operation by the driver can be assisted based on at least one of the steering angle and the steering speed. Therefore, the obstacle avoidance operation can be more appropriately supported by, for example, the driver's intention to steer based on the steering angle or the steering speed.

(5) Furthermore, the avoidance route generation means generates an avoidance route that passes through an area where the existence probability of the obstacle is lower than the set value. Further, the avoidance support means sets an assist control amount for supporting the obstacle avoidance operation based on the steering angle pattern that causes the host vehicle to travel the avoidance route and the operation state of the host vehicle.
Therefore, for example, by calculating the support amount so that the host vehicle travels along the avoidance route, the driver assists the avoidance operation so that the host vehicle travels only in the region where the obstacle existence probability is low. be able to. As a result, it is possible to provide more accurate support in consideration of the possibility of interference between the host vehicle and the obstacle in the future.

(Third embodiment)
Next, a third embodiment of the present invention will be described with reference to the drawings. In addition, the same code | symbol is used about the same structure as said each embodiment.
(Constitution)
The basic vehicle configuration of this embodiment is the same as that of the second embodiment as shown in FIG.
However, the camera 1 also serves to detect a road boundary ahead of the host vehicle SW. The road boundary is a boundary line that divides both sides of the road or traveling lane on which the host vehicle SW is traveling.
FIG. 23 shows a block diagram in which the above-described apparatus configuration is summarized by function.

In the present embodiment, the camera 1 also serves as a road boundary detection unit. And the function which extracts the information regarding the road boundary ahead of the own vehicle SW by image-processing the image imaged with the camera 1 is implement | achieved.
The avoidance route generation means realizes a function of generating an avoidance route based on information obtained by the own vehicle state detection means, the obstacle detection means, the existence probability distribution detection means, and the road boundary detection means.

Next, processing of the avoidance support controller 4 will be described with reference to the drawings.
FIG. 24 is a flowchart showing processing of the avoidance support controller 4.
FIG. 25 is a plan view showing an example of an assumed scene.
Here, in order to give concreteness and make the processing easy to understand, description will be made assuming the scene shown in FIG. The scene shown in FIG. 25 assumes a scene in which a pedestrian crossing from the left side in front of the own vehicle SW is detected when the own vehicle SW is traveling on a straight road with one lane on one side. In the first embodiment and the second embodiment, the left and right sides of the obstacle SM have a space through which the host vehicle SW can pass. However, in the scene shown in FIG. It is assumed that the space through which the SW can pass is limited.

Note that the processing of FIG. 24 is repeatedly executed at the set sampling cycle.
As shown in FIG. 24, first, in step S601, signals detected by various sensors are read into the memory. Then, the movement state of the host vehicle SW, the operation state of the host vehicle SW, the position of the obstacle SM, and the distance between the obstacle SM are calculated as values on a unified coordinate system.
By setting the above coordinate system, the position of the road boundary at the left end of the road can be expressed in the form of Y = Y _L and the position of the road boundary at the right end of the road in the form of Y = Y _R.

The positions of the left end and the right end of the road are detected by road boundary detection by the camera 1.
Next, in step S602, the process branches depending on whether or not the host vehicle SW and the obstacle SM may interfere with each other. For example, a process similar to step S102 of the first embodiment or a process similar to step S302 of the second embodiment can be used. If it is determined that the host vehicle SW and the obstacle SM may interfere (YES), the process proceeds to step S603. On the other hand, the processing branches depending on the presence or absence. On the other hand, if it is determined that there is no possibility that the host vehicle SW and the obstacle SM interfere with each other, the (NO) process is terminated.

Next, in step S603, the existence probability distribution of the obstacle SM around the host vehicle SW is estimated based on the detection error characteristics included in the detection result of the camera 1 based on the detection characteristics of the camera 1. For example, a process similar to step S103 of the first embodiment or a process similar to step S303 of the second embodiment can be used.
Next, in step S604, first, an avoidance route that passes in the right direction of the obstacle SM is generated based on the existence probability distribution of the obstacle SM calculated in step S603.

Next, a right avoidance steering angle pattern δ _Ri for traveling on the generated avoidance route is calculated.
A specific method for generating the avoidance route will be described later.
Next, in step S605, first, an avoidance route that passes in the left direction of the obstacle SM is generated based on the probability distribution of the obstacle SM estimated in step S603.
Next, a left avoidance steering angle pattern δ _Li for traveling on the generated avoidance route is calculated.
Next, in step S606, characteristics of the steering assist torque are set based on the right avoidance steering angle pattern δ _Ri calculated in step S604 and the left avoidance steering angle pattern δ _Li calculated in step S605.

FIG. 26 is a diagram showing the characteristics of the steering assist torque when N _TH is 4.
Specifically, first, a first assist torque characteristic diagram is set based on the right avoidance steering angle pattern δ _Ri calculated in step S604. The first support torque characteristic diagram, as shown in FIG. 26 (a), for example, a point consisting of the steering assist torque T _Ri corresponding to the right avoidance steering angle pattern [delta] _Ri and the right avoidance steering angle pattern [delta] _Ri ([delta] _Ri , T _Ri ) are plotted on a two-dimensional plane, and the plotted points (δ _Ri , T _Ri ) are connected by straight lines. The steering assist torque T _Ri is set so that T _R1 > T _R2 >...> T _RNTH . Here, the avoidance route corresponding to the kth right avoidance rudder angle pattern _δRk falls within the road boundary, but the avoidance route corresponding to the (k + 1) th right avoidance rudder angle pattern _{δRk + 1} is within the road boundary. It will not fit. In this case, the steering assist torque T _{Rk is set} to “0”, and the steering assist torques T _{Rk + 1} ,..., T _RNTH are _set to negative values. That is, the steering assist torque T _{i is, T R1> T R2> ...} > T Rk (= 0)> ... set such that> T _RNTH. The sign of the steering assist torque T _i is the front wheel 5FL, a steering assist torque for steering the 5FR rightward to a positive value, the front wheel 5FL, a steering assist torque for steering the 5FR leftward to a negative value.

Next, a second assist torque characteristic diagram is set based on the left avoidance steering angle pattern δ _Li calculated in step S605. For example, as shown in FIG. 26 (b), the second assist torque characteristic diagram includes a left avoiding steering angle pattern δ _Li and a point (δ including a steering assist torque T _Li corresponding to the left avoiding steering angle pattern δ _{Li. Li} , T _Li ) are plotted on a plane, and the plotted points (δ _Li , T _Li ) are connected by straight lines. Steering assist torque T _Li is set so that _{T L1 <T L2 <... <} T LNTH. Here, the avoidance route corresponding to the kth left avoidance steering angle pattern δ _Lk falls within the road boundary, but the avoidance route corresponding to the (k + 1) th left avoidance steering angle pattern _{δLk + 1} is within the road boundary. It will not fit. In this case, the steering assist torque T _{Lk is set} to “0”, and the steering assist torques T _{Lk + 1} ,..., T _LNTH are _set to positive values. That is, the steering assist torque T _{Li is, T L1 <T L2 <...} <T Lk (= 0) <... < set to be T _LNTH.
Then, based on the first and second support torque characteristic diagram set, it calculates the steering assist torque corresponding to the steering angle [delta] _f. Then, a signal instructing application of the calculated steering assist torque is output to the steering actuator 7.

As described above, in step S606, the characteristic of the steering assist torque is set in consideration of whether or not the avoidance route is within the road boundary. For example, as shown in FIGS. 25 and 26, the space left obstacle SM is narrow, the third corresponding avoidance path to the left around the steering angle pattern [delta] _L3 is not fit within the road boundary. In this case, as the steering angle [delta] _f is close to the left around the steering angle pattern [delta] _L3, possibly vehicle SW deviates from the road boundary becomes higher. Therefore, the steering angle [delta] _f is the case close to the left around the steering angle pattern [delta] _L3 is reverse torque for notifying cut too much steering, that is, to impart a steering assist torque in the steering back direction. Also, it is assumed that the avoidance route corresponding to the fourth right avoidance steering angle pattern δ _R4 does not fall within the road boundary. And In this case, the closer the steering angle δ _f is to the right turn route δ _R4 , the higher the possibility that the host vehicle SW will depart from the road boundary. Therefore, when the steering angle [delta] _f is close to the right turning path [delta] _R4 is reverse torque, i.e., to generate a steering assist torque in the steering back direction.
In the present embodiment, based on the steering angle [delta] _f, although an example of calculating the steering assist torque, it is possible to employ other configurations. For example, it is also possible based on the steering angle [delta] _f and the steering angular velocity d? _F / dt, to more properly calculate the deviation possibility of vehicle SW.

Next, the calculation method of the right avoidance steering angle pattern δ _Ri performed in step S604 will be described with reference to the drawings.
FIG. 27 is a flowchart illustrating a process for calculating a right avoidance steering angle pattern.
As shown in FIG. 27 is determined, first, in step S701, the whether or not the variable i is set value N _TH. The variable i is a variable for counting the number of executions of a flow from steps S702 to S706 described later, and is set to 1 at the start of this process. If it is determined that the variable i is the set value _NTH (YES), the process is terminated and the process returns. On the other hand, if it is determined that the variable i is not the set value N _TH (NO), the process proceeds to step S702.

Next, in step S702, a steering angle pattern δ _f ^′ (k) for obstacle avoidance is set.
In the present embodiment, the steering angle pattern δ _f ^′ (k) is set by setting the gain Kδ for adjusting the obstacle avoidance operation to the steering angle pattern δ _f (t) set in advance.
As the rudder angle pattern δ _f (t), in consideration of performing the operation from turning back to turning back, as shown in FIG. 22, the rudder angle pattern δ _f (t ) Is available. The timing for performing the switching is set by adjusting the parameter of ΔT _X in advance with _TCJ as a reference.

The steering angle pattern δ _f (t) is divided by an appropriate number of steps N and discretized, and the value of the steering angle pattern δ _f (t) at each step is calculated. As a result, N pieces of time-series data as shown below can be obtained.
δ _f (k) = [δ _f (0 ^* 2 × (T _CJ + ΔT _X ) N), δ _f (1 ^* 2 × (T _CJ + ΔT _X ) N), ..., δ _f ((N-1 ) ^* 2 × (T _CJ + ΔT _X ) N)]
Further, based on the N pieces of time series data, the steering angle pattern δ _f ^′ (k) can be expressed as the following equation (14).
δ _f '(k) = Kδ × δ _f (k) (14)
However, the gain Kδ is set to a small positive value ΔKδ at the start of this process.

Next, in step S703, the process is branched depending on whether or not the route that can be traveled by the steering angle pattern δ _f ^′ (k) set in step S702 is a route that falls within the road boundary. As a method of branching depending on whether or not the route is within the road boundary, for example, when the host vehicle SW is steered by the steering angle pattern δ _f ^′ (k), 2 × ( _TCJ Before + ΔT _X ), it is determined whether or not the host vehicle SW deviates from the road boundary.

Specifically, first, based on the steering angle pattern δ _f ^′ (k), the future travel route (x _V (t), y _V (t)) of the host vehicle SW is calculated according to the above equation (4). . The calculated future travel route (x _V (t), y _V (t)) is set as an avoidance route (x _V (t), y _V (t)).
Next, the minimum value y _min (= min (yV (k))) of the Y component y _V (k) is extracted from the calculated avoidance path (x _V (t), y _V (t)). Since it is the road boundary at the right end of the road that may deviate, the minimum value y _min is extracted from the current Y component y _V ^* (k) 2 × (T _CJ + ΔT _X ) To target.

Next, it is determined whether or not the extracted minimum value y _min is smaller than the set value Y _R. The set value Y _R is the position of the road boundary at the right end of the road. If it is determined that it is smaller than the set value Y _R , it is determined that the avoidance route is within the road boundary (YES), the process is terminated, and the process returns. On the other hand, if it is determined that the value is equal to or greater than the set value Y _R, it is determined that the avoidance route is not within the road boundary and deviates from the road boundary (NO), and the process proceeds to step S704.

Next, in step S704, when the vehicle is steered with the steering angle pattern δ _f ^′ (k) set in step S702, the host vehicle SW travels only in an area where the presence probability of the obstacle SM is lower than the set level TH _i. It is determined whether or not it is possible.
Specifically, first, referring to the existence probability distribution of the obstacle SM set in step S603, each point on the avoidance route (x _V (t), y _V (t)) set in step S703 is set. On the other hand, the existence probability of the obstacle SM is calculated. Then, the maximum one of the calculated existence probabilities is extracted as the maximum value P _m (= maxf (x _V (t), y _V (t), t)).

Next, it is determined whether or not the maximum value P _m of the presence probability of the extracted obstacle SM is smaller than the set level TH _i . If it is determined that the vehicle is smaller than the set level TH _i , it is determined that an avoidance route that can travel only in an area where the presence probability of the obstacle SM is lower than the set level TH _i is generated (YES). The process proceeds to step S706. On the other hand, when it is determined that the set level TH _i above, it is determined that the existence probability of an obstacle SM is avoidance path that passes through the region higher than the set level TH _i (NO), the process proceeds to step S705 .

Next, in step S705, the gain Kδ is reset according to the following equation (15) so as to generate an avoidance route that can travel only in an area where the presence probability of the obstacle SM is lower than the set level TH _i . Thereafter, the process proceeds to step S703.
Kδ = Kδ + ΔKδ (15)
On the other hand, in step S706, the steering angle pattern δ _f ^′ (k) set in step S702 is stored in the memory as the right avoidance steering angle pattern δ _Ri . A value obtained by adding “1” to the variable i is set as a new variable i. Thereafter, the process proceeds to step S701.

The calculation method of the left avoidance steering angle pattern δ _Li performed in step S605 is the same as the calculation method of the right avoidance steering angle pattern δ _Ri .
However, ΔKδ is set to a negative value. The steering angle pattern δ _f ^′ (k) set in step S702 is stored in the memory as the left avoidance steering angle pattern δ _Li .
Here, in the present embodiment, the vehicle speed sensor 2 and the gyro sensor 9 of FIG. 23 constitute the own vehicle state detection means. Similarly, the steering angle sensor 3 in FIG. 23 constitutes an operation state detection unit. Further, the camera 1 constitutes an obstacle detection unit. Further, the avoidance support controller 4 of FIG. 23 constitutes an existence probability distribution estimation unit and an avoidance route generation unit.

(Effect of this embodiment)
(1) In the present embodiment, the avoidance route generation means generates a route that falls within the road boundary as the avoidance route.
Therefore, it is possible to generate an avoidance route in which the own vehicle does not deviate from the road boundary. Therefore, for example, by calculating the support amount so that the host vehicle travels along the generated avoidance route, the avoidance operation by the driver can be supported so that the host vehicle stays within the road boundary. As a result, even when the driver steers more than necessary to avoid an obstacle, the vehicle can be prevented from deviating from the road boundary.

(Fourth embodiment)
Next, a fourth embodiment of the present invention will be described with reference to the drawings. In addition, the same code | symbol is used about the same structure as said each embodiment.
FIG. 28 is a conceptual diagram of the device configuration of the host vehicle SW equipped with the vehicle driving support device of the fourth embodiment.
The basic vehicle configuration of the present embodiment is the same as that of the second embodiment as shown in FIG.
However, the acceleration sensor 10 is provided. The acceleration sensor 10 detects acceleration in a specific direction generated in the host vehicle SW. For example, a configuration using a piezoelectric element or the like can be used. Then, the detection signal is output to the avoidance support controller 4.
FIG. 29 is a block diagram summarizing the above-described device configuration by function.
In the present embodiment, the host vehicle state detection means is constituted by the vehicle speed sensor 2, the gyro sensor 9, and the acceleration sensor 10. And the function which acquires the information regarding the movement state of the own vehicle SW by processing the signal detected by each sensor in an integrated manner is realized.

Next, processing of the avoidance support controller 4 will be described with reference to the drawings.
FIG. 30 is a flowchart showing processing of the avoidance support controller 4.
Here, in order to give concreteness and make the processing easy to understand, description will be made assuming the scene shown in FIG. The scene shown in FIG. 25 assumes a scene in which a pedestrian crossing from the left side in front of the own vehicle SW is detected when the own vehicle SW is traveling on a straight road with one lane on one side. In the first embodiment and the second embodiment, the left and right sides of the obstacle SM have a space through which the host vehicle SW can pass. However, in the scene shown in FIG. It is assumed that the space through which the SW can pass is limited. In addition, it is assumed that the driver of the host vehicle SW recognizes the presence of the obstacle SM and performs a braking operation.

Note that the processing in FIG. 30 is repeatedly executed at the set sampling cycle.
As shown in FIG. 30, first, in step S801, signals detected by various sensors are read into the memory. Then, the movement state of the host vehicle SW, the operation state of the host vehicle SW, the position of the obstacle SM, and the distance between the obstacle SM are calculated as values on a unified coordinate system.
The deceleration a _VX of the _host vehicle SW can be obtained from the acceleration sensor 10.

Next, in step S802, it is determined whether there is a possibility that the host vehicle SW and the obstacle SM interfere between the present time and the set time. A specific determination method will be described later.
Next, in step S803, the existence probability distribution of the obstacle SM around the host vehicle SW is estimated based on the detection error characteristics included in the detection result of the camera 1 based on the detection characteristics of the camera 1. For example, a process similar to step S103 of the first embodiment or a process similar to step S303 of the second embodiment can be used.

For example, as shown in FIG. 19, when the obstacle SM is moving at a constant speed, the existence probability f _c (X, Y, t) of the obstacle SM can be expressed as the following equation (16). .
f _c (X, Y, t) = N (μ _X , μ _YC , σ _X ² , σ _Y ² ) (16)
When the obstacle SM is moving at an accelerated speed, the existence probability f _a (X, Y, t) of the obstacle SM can be expressed as the following equation (17).
f _a (X, Y, t) = N (μ _X , μ _YC , σ _X ² , σ _Y ² ), Y ≦ μ _YC
f _a (X, Y, t) = max [f _Ya (μ _X , μ _YC , σ _X ² , σ _Y ² ), f _Ya (μ _X , μ _YA , σ _X ² , σ _Y ² )], μ _YC <Y <μ _YA
f _a (X, Y, t) = N (μ _X , μ _YA , σ _X ² , σ _Y ² ), μ _YA ≦ Y (17)

Further, when the obstacle SM is moving at a reduced speed, the existence probability f _d (X, Y, t) of the obstacle SM can be expressed as the following equation (18).
f _d (X, Y, t) = N (μ _X , μ _YD , σ _X ² , σ _Y ² ), Y ≦ μ _YD
f _d (X, Y, t) = max [f _Ya (μ _X , μ _YD , σ _X ² , σ _Y ² ), f _Ya (μ _X , μ _YC , σ _X ² , σ _Y ² )], μ _YD <Y <μ _YC
f _d (X, Y, t) = N (μ _X , μ _YD , σ _X ² , σ _Y ² ), μ _YC ≦ Y (18)
Here, the above equation (18) is a continuous function and can be differentiated with respect to X and Y.
Further, based on the above equations (16) to (18), the existence probability of the obstacle SM when considering the movement of the obstacle SM can be summarized as the following equation (19).
f (X, Y, t) = [K _d × f _d (X, Y, t) + f _c (X, Y, t) + K _a × f _a (X, Y, t)] / [K _Now (1 + Kd + Ka)] ……… (19)

Next, in step S804, first, an avoidance route that passes in the right direction of the obstacle SM is generated based on the existence probability distribution of the obstacle SM calculated in step S803.
Next, a right avoidance steering angle pattern δ _Ri for traveling on the generated avoidance route is calculated.
A specific method for generating the avoidance route will be described later.
Next, in step S805, first, an avoidance route that passes in the left direction of the obstacle SM is generated based on the probability distribution of the obstacle SM estimated in step S803.
Next, a left avoidance steering angle pattern δ _Li for traveling on the generated avoidance route is calculated.
Next, in step S806, the characteristics of the steering assist torque are set based on the right avoidance steering angle pattern δ _Ri calculated in step S804 and the left avoidance steering angle pattern δ _Li set in step S805.

FIG. 31 is a diagram showing a right avoidance steering angle pattern δ _Ri when N _TH is 4.
FIG. 32 is a diagram illustrating characteristics of the steering assist torque.
As shown in FIG. 31, the right avoidance steering angle pattern δ _Ri (t ₀ ) at the current time t ₀ has the same value. Therefore, in the present embodiment, the characteristic of the steering assist torque is set using the right avoidance steering angle pattern δ _Ri (t ₀ + Δt) after Δt seconds from the current time t ₀ .

Specifically, first, a first assist torque characteristic diagram is set based on the right avoidance steering angle pattern δ _Ri (t ₀ + Δt) calculated in step S804. As shown in FIG. 32, the first assist torque characteristic diagram shows, for example, steering corresponding to the right avoidance steering angle pattern δ _Ri (t ₀ + Δt) and the right avoidance steering angle pattern δ _Ri (t ₀ + Δt). A point (δ _Ri (t ₀ + Δt), T _Ri ) consisting of the assist torque T _Ri is plotted on a two-dimensional plane, and a straight line between the plotted points (δ _Ri (t ₀ + Δt), T _Ri ). Generate by tying with. The steering assist torque T _Ri is set so that T _R1 > T _R2 >...> T _RNTH . Here, the avoidance route corresponding to the kth right avoidance steering angle pattern δ _Rk (t ₀ + Δt) falls within the road boundary, but the (k + 1) th right avoidance steering angle pattern δ _{Rk + 1} (t ₀ + It is assumed that the avoidance route corresponding to Δt) does not fit within the road boundary. In this case, the steering assist torque T _{Rk is set} to “0”, and the steering assist torques T _{Rk + 1} ,..., T _RNTH are _set to negative values. That is, the steering assist torque T _{i is, T R1> T R2> ...} > T Rk (= 0)> ... set such that> T _RNTH.

Next, a second assist torque characteristic diagram is set based on the left avoidance steering angle pattern δ _Li (t ₀ + Δt) calculated in step S805. The second assist torque characteristic diagram includes, for example, a left avoidance steering angle pattern δ _Li (t ₀ + Δt) and a steering assist torque T _Li corresponding to the left avoidance steering angle pattern δ _Li (t ₀ + Δt). (Δ _Li (t ₀ + Δt), T _Li ) is plotted on a plane, and the plotted points (δ _Li (t ₀ + Δt), T _Li ) are connected by straight lines. Steering assist torque T _Li is set so that _{T L1 <T L2 <... <} T LNTH. Here, the avoidance route corresponding to the kth left avoidance steering angle pattern δ _Lk (t ₀ + Δt) is within the road boundary, but the (k + 1) th left avoidance steering angle pattern δ _{Lk + 1} (t ₀ + It is assumed that the avoidance route corresponding to Δt) does not fit within the road boundary. In this case, the steering assist torque T _{Lk is set} to “0”, and the steering assist torques T _{Lk + 1} ,..., T _LNTH are _set to positive values. _{That, T L1 <T L2 <...} <T Lk (= 0) <... < set to be T _LNTH.

Then, based on the first and second support torque characteristic diagram set, it calculates the steering assist torque corresponding to the steering angle [delta] _f. Then, a signal instructing application of the calculated steering assist torque is output to the steering actuator 7.
By sequentially performing this process for each sampling period, it is possible to sequentially correct the driver's uncertainty, and it is possible to improve the reliability with respect to a deviation in prediction.

Next, a method for determining whether or not there is a possibility of interference between the host vehicle SW and the obstacle SM performed in step S802 will be described with reference to the drawings.
FIG. 33 is a flowchart showing a process for determining whether or not there is a possibility of interference between the host vehicle SW and the obstacle SM.
As shown in FIG. 33, first, in step S901, it is determined whether or not the host vehicle SW can be stopped before the obstacle SM by maintaining the deceleration a _VX of the host vehicle SW.

Specifically, it is determined whether v _VX ² is less than (−2 × a _VX × x _B ). If it is determined that it is less than ( ₋₂ × a _VX × x _B ), it is determined that the _host vehicle SW can be stopped before the obstacle SM by maintaining the deceleration a _VX of the host vehicle SW. (YES), end the process and return. With this return, the process proceeds to step S801. On the other hand, if it is determined that it is ( ₋₂ × a _VX × x _B ) or more, there is a possibility that the own vehicle SW and the obstacle SM may interfere even if the deceleration a _VX of the own vehicle SW is maintained. It is determined that there is (NO), and the process proceeds to step S902.

Next, in step S902, an arrival time T _CJ ′ until the host vehicle SW reaches the position of the obstacle SM is calculated.
Specifically, a positive value t that satisfies the following equation (20) is calculated based on the deceleration a _{VX of} the host vehicle SW, the longitudinal speed v _{VX of the host} vehicle SW, and the longitudinal position x _B of the obstacle SM. . The calculated value t is defined as an arrival time T _CJ ′.
0.5 × a _VX × t ² + V _VX × t = X _B ……… （20）
Next, in step S903, it is determined whether or not the obstacle SM enters the traveling area of the host vehicle SW between the present time and the time after the arrival time T _CJ '.
More specifically, first, the 'on the basis of, the arrival time T _CJ' calculated arrival time T _CJ in step S902 to calculate a lateral position _{y B + v BY × T CJ} ' obstacle SM after.

Then, the calculated lateral position y _B + v 'absolute value of _{| y B + v BY × T} CJ' | BY × T CJ determines whether less than the set value Y _TH. When it is determined that the absolute value | y _B + v _BY × _TCJ ′ | of the lateral position is less than the set value Y _TH, it is determined that the obstacle SM enters the traveling area of the host vehicle SW ( YES), finish the process and return. On the other hand, when it is determined that the absolute value | y _B + v _BY × _TCJ ′ | of the lateral position is equal to or greater than the set value Y _TH , no obstacle enters the traveling area of the host vehicle SW (NO), That is, it is determined that it is not necessary to support the obstacle avoidance operation by the driver, and the process is terminated and the process returns. With this return, the process proceeds to step S801.

Next, the calculation method of the right avoidance steering angle pattern δ _Ri performed in step S804 will be described with reference to the drawings.
FIG. 34 is a flowchart showing a process for calculating the right avoidance steering angle pattern δ _Ri .
As shown in FIG. 34, first, in the step S1001, the determining whether the variable i is set value N _TH. The variable i is a variable for counting the number of executions of steps S1002 to S1007 described later, and is set to 1 at the start of this process. If it is determined that the variable i is the set value _NTH (YES), the process is terminated and the process returns. On the other hand, if it is determined that the variable i is not the set value N _TH (NO), the process proceeds to step S1002.

In step S1002, an evaluation function J (u) for obstacle avoidance is set.
In the present embodiment, an avoidance route that passes through an area in which the presence probability of the obstacle SM is lower than the set level TH _i is generated by optimization calculation using the vehicle model and the evaluation function J (u).
Specifically, first, in order to calculate the future travel route of the host vehicle SW, a two-wheel model that considers deceleration of the host vehicle SW is used as a two-wheel model that describes the motion of the host vehicle SW.

In this two-wheel model, the running speed is constant, the yaw angle θ is sufficiently small, the lateral speed v _VY is sufficiently smaller than the longitudinal speed v _VX, and the tire lateral force saturation characteristic does not appear remarkably. In the state, it can be approximated as the following equation (21).
dx _V / dt = vcos (β + θ)
dy _V / dt = vsin (β + θ) ……… (21)
dθ / dt = γ
dv / dt = a _VX
dβ / dt = -γ + [2K _f (α _f , a _VX ) + 2Y _r (α _r , a _VX )] / MV _V
dγ / dt = [2L _f K _f (α _f , a _VX ) -2L _r Y _r (α _f , a _VX )] / I
dδ _f / dt = -δ _f / T _s -δ _f / T _s

However, Y _f (α _f , α _VX ) and Y _r (α _r , α _VX ) are simplified functions that model tire characteristics. For example, those described in "Bakker, E., Nyborg, L. and Pacejka, HB: Tire Modeling for Use in Vehicle Dynamics Studies, SAE Tech. Pap. Ser., No. 870495 (1987)" can be used. . In Y _f (α _f , α _VX ) and Y _r (α _r , α _VX ), the force generated by the tire has a frictional circle constraint, and even in the same tire slip angle α _f , α _r If it occurs, the lateral force decreases.

Further, when the tire slip angles α _f and α _r are the same as those in the above formula (3), the above formula (21) can be summarized as the following formula (22).
dx _V / dt = f _V (x _V , u _V ) ……… (22)
x _V = (x _V , y _V , θ, v, β, γ, δ _f )
u _V = (δ _f ^* )
Based on the above equation (22), based on the predicted value of the vehicle state vector x _V for the operation amount u applied to the host vehicle SW between the current time t ₀ and the estimated time t ₀ + T _f , The evaluation function J (u) can be expressed as the following equation (23).

Here, the first term on the right side of the equation (23) is a terminal evaluation equation for evaluating the vehicle motion state at the estimated time t ₀ + T _f . The terminal evaluation formula has a first evaluation item Ψ ₁ (t ₀ + T _f ). The first terminal evaluation item ψ ₁ (t ₀ + T _f ) is an item for causing the vehicle posture of the host vehicle SW to face the road traveling direction after the estimated time t ₀ + T _f .

The second term on the right side is a section evaluation formula for evaluating the vehicle motion state between the current time t ₀ and the estimated time t ₀ + T _f . The section evaluation formula has first to third section evaluation items. The first section evaluation item L ₂ (τ) is an item for preventing the host vehicle SW from approaching the obstacle SM from the current time t ₀ to the estimated time t ₀ + T _f . The second section evaluation item L ₃ (τ) is an item for preventing the host vehicle SW from deviating from the road boundary from the current time t ₀ to the estimated time t ₀ + T _f . A third section evaluation items L ₄ (tau) is the period from the current time t0 to time estimate t ₀ + T _f, is the evaluation formula for reducing the operation amount u _v of the vehicle SW.

Further, w ₁ , w ₂ , w ₃ , and w ₄ are weight parameters.
Next, in step S1003, based on the evaluation function J (u) set in step S1002, optimization calculation for minimizing the evaluation function J (u) is performed.
The problem of obtaining an operation amount that minimizes the evaluation function J (u) is generally called an optimal control problem, and various algorithms are considered as known techniques for obtaining a numerical solution thereof.
An example of a known technique is T. Ohtsuka, “A continuation / GMRES method for fast computing of nonlinear receeding horizon control”, Automatica, vol, 40, 563/574, 2004.
Further, in order to obtain the right avoidance steering angle pattern δ _Ri , it is necessary to read the steering angle pattern for turning the front wheels 5FL and 5FR in the right direction as an initial solution for the optimization calculation. This initial solution is stored in advance in the memory according to conditions such as the speed and the distance R _s between the obstacle SM and the like.

In the case of the present embodiment, the manipulated variable uv is the steering angle δ _f , so the numerical solution obtained by the optimization calculation is a time series of the steering amount from the current time t ₀ to the time t ₀ + T _f. .
In the actual calculation, performed discretized by dividing the evaluation interval t ₀ ~t ₀ + T _f at an appropriate number of steps N, calculates the values of the manipulated variables at each step time. As a result, N time-series manipulated variables δ _f ^* as shown below can be obtained.
δ _f ^* = (δ _f ^* (t ₀ ), δ _f ^* (t ₀ + T _f / N), ..., δ _f ^* (t ₀ + (N-1) × T _f / N))

Here, the larger the number of steps N, the shorter the sampling time interval, and the more accurate predicted manipulated variable δ _f ^* can be calculated. However, as the number of steps N increases, the number of data to be calculated increases significantly, and the calculation load on the avoidance support controller 4 increases. Therefore, the number of steps N is set so that the accuracy of the predicted manipulated variable δ _f ^* and the calculation addition of the avoidance support controller 4 are appropriate. For example, the sample time interval is determined to be 10 to 100 msec.

Next, in step S1004, when the host vehicle SW is steered with the predicted operation amount set in step S1003 (hereinafter also referred to as “steering angle pattern δ _f ^* ”), a future travel route on which the host vehicle SW travels. ^{_{(x V * (t),}} y V * (t)) is calculated. Then, the calculated future travel path ^{_{(x V * (t),}} y V * (t)) of the avoidance path ^{_{(x V * (t),}} y V * (t)) and.
Specifically, based on the steering angle pattern δ _f ^* , according to the two-wheel model defined as the above formulas (21) and (22), the future travel route (x _V ^* (t), y _V ^* (t)) is calculated. As a result, N pieces of time-series data as shown below can be obtained.
x _V ^* = (x _V ^* (t ₀ ), x _V ^* (t ₀ + T _f / N), ..., x _V ^* (t ₀ + T _f ))
y _V ^* = (y _V ^* (t ₀ ), y _V ^* (t ₀ + T _f / N), ..., y _V ^* (t ₀ + T _f ))

Next, in step S1005, it is determined whether or not the host vehicle SW deviates from the road boundary when steering is performed with the steering angle pattern δ _f ^* (t) set in step S1003.
Specifically, first, the minimum value of the Y component y _V ^* (k) from the future travel route (x _V ^* (t), y _V ^* (t)) of the host vehicle SW calculated in step S1004. Extract y _min . Since it is the road boundary at the right end of the road that may deviate, the minimum value y _min is extracted from the current Y component y _V ^* (k) 2 × (T _CJ + ΔT _X ) To target.

Next, it is determined whether or not the extracted minimum value y _min is smaller than the set value Y _R. When it is determined to be smaller than the set value Y _R can be steered by the steering angle pattern δ _f ^* (k), the avoidance route is determined to fall within the road boundary (YES), processing Exit and return. On the other hand, if it is determined that it is equal to or greater than the set value Y _R, it is determined that the avoidance route is not within the road boundary and deviates from the road boundary (NO), and the process proceeds to step S1006.

Next, in step S1006, when the vehicle is steered with the steering angle pattern δ _f ^* (t) set in step S1003, the host vehicle SW travels only in a region where the presence probability of the obstacle SM is lower than the set level TH _i. It is determined whether or not it is possible.
Specifically, with reference to the existence probability distribution of the obstacle SM set in step S803, each point on the avoidance route (x _V ^* (t), y _V ^* (t)) calculated in step S1004 is determined. On the other hand, the existence probability of the obstacle SM is calculated. Then, the maximum one is extracted as the maximum value P _m (= maxf (x _V ^* (t), y _V ^* (t), t)) from the calculated existence probability.

Next, it is determined whether or not the maximum value P _m of the presence probability of the extracted obstacle SM is smaller than the set level TH _i . If it is determined that the vehicle is smaller than the set level TH _i , it is determined that an avoidance route that can travel only in an area where the presence probability of the obstacle SM is lower than the set level TH _i is generated (YES). The process proceeds to step S1008. On the other hand, if it is determined that it is equal to or higher than the set level TH _i, it is determined that the path is an avoidance path that passes through an area where the presence probability of the obstacle SM is higher than the set level TH _i (NO), and the process proceeds to step S1007. .

Next, in step S1007, the weight w ₄ is reset to a lower value.
On the other hand, in step S1008, the steering angle pattern δ _f ^* set in step S1003 is stored in the memory as the right avoidance steering angle pattern δ _Ri . A value obtained by adding “1” to the variable i is set as a new variable i. Thereafter, the process proceeds to step S1001.
The calculation method of the left avoidance steering angle pattern δ _Li performed in step S805 is the same as the calculation method of the right avoidance steering angle pattern δ _Ri .

However, in order to obtain the left avoiding steering angle pattern δ _Li , it is necessary to read the steering angle pattern for steering the front wheels 5FL and 5FR to the left as the initial solution for the optimization calculation. The set steering angle pattern δ _f ^* is stored in the memory as the left avoidance steering angle pattern δ _Li .
Here, in this embodiment, the vehicle speed sensor 2, the gyro sensor 9, and the acceleration sensor 10 of FIG. 29 constitute the vehicle state detection means. Similarly, the steering angle sensor 3 of FIG. 29 constitutes an operation state detecting means. Further, the camera 1 constitutes an obstacle detection unit. Furthermore, the avoidance support controller 4 of FIG. 29 constitutes an existence probability distribution estimation unit and an avoidance route generation unit. Further, the steering assist torque constitutes an assist control amount.

(Effect of this embodiment)
(1) In this embodiment, a route that maximizes or minimizes an evaluation function that numerically evaluates the avoidance route is generated as the avoidance route.
Therefore, an avoidance path that maximizes or minimizes the evaluation function can be calculated. Therefore, for example, by setting a function that increases the evaluation of the evaluation item at the time of avoidance when the function value is large or small as an evaluation function, an avoidance route with a high evaluation item evaluation, that is, a more appropriate route is set. Can be calculated. As a result, more appropriate support can be performed by assisting the avoidance operation so that the host vehicle travels along the calculated avoidance route.

(2) Further, the avoidance support means sequentially calculates a support control amount for supporting the obstacle avoidance operation.
Therefore, it is possible to calculate the assistance control amount according to the latest motion state of the host vehicle, the operation state of the host vehicle, and the obstacle probability distribution. Therefore, for example, when the obstacle movement is not predicted and the obstacle probability distribution on the future driving route of the host vehicle changes, the assist control amount is calculated according to the new vehicle motion state. Thus, the support control amount can be appropriately corrected. As a result, more appropriate support can be provided.

(3) Furthermore, the avoidance route generation means generates an avoidance route using a vehicle model that takes into account the friction circle of the tire of the host vehicle.
Therefore, for example, when the driver is performing a braking operation as well as steering, an avoidance route can be generated in consideration of a decrease in the lateral force generated by the host vehicle due to the braking operation. As a result, a more appropriate avoidance route can be generated.

Reference numeral 1 denotes a camera (obstacle detection means, road boundary detection means), 2 denotes a vehicle speed sensor (own vehicle state detection means), 3 denotes a steering angle sensor (operation state detection means), and 4 denotes an avoidance support controller (existence probability distribution estimation means) , Avoidance path generation means), 7 is a steering actuator (steering actuator), 8 is a steering column, 9 is a gyro sensor (own vehicle state detection means), 10 is an acceleration sensor (own vehicle state detection means), SW is the own vehicle, SM is an obstacle, steering assist torque is the amount of support control

Claims (13)

Own vehicle state detection means for detecting the movement state of the own vehicle;
Operation state detection means for detecting the operation state of the host vehicle;
Obstacle detection means for detecting the position of obstacles around the host vehicle;
Presence probability distribution for estimating the presence probability distribution of the obstacle based on the position of the obstacle detected by the obstacle detection means and the nature of the detection error included in the position by the detection characteristics of the obstacle detection means An estimation means;
Based on the movement state of the own vehicle detected by the own vehicle state detection means, the operation state of the own vehicle detected by the operation state detection means, and the existence probability distribution of the obstacle estimated by the existence probability distribution estimation means. And an avoidance support means for supporting an obstacle avoidance operation by the driver ,
The existence probability distribution estimation means is configured to determine whether the own vehicle has a large movement based on at least one of the movement in the lateral direction and the movement in the yaw direction of the own vehicle as compared with the case where the movement is small. highest and vehicular driving support apparatus which is characterized that you reduce the presence probability of near the maximum value of the existence probability distribution of the obstacle in the lateral direction. Own vehicle state detection means for detecting the movement state of the own vehicle;
Operation state detection means for detecting the operation state of the host vehicle;
Obstacle detection means for detecting the position of obstacles around the host vehicle;
Presence probability distribution for estimating the presence probability distribution of the obstacle based on the position of the obstacle detected by the obstacle detection means and the nature of the detection error included in the position by the detection characteristics of the obstacle detection means An estimation means;
Based on the movement state of the own vehicle detected by the own vehicle state detection means, the operation state of the own vehicle detected by the operation state detection means, and the existence probability distribution of the obstacle estimated by the existence probability distribution estimation means. And an avoidance support means for supporting an obstacle avoidance operation by the driver,
The existence probability distribution estimation means is configured to determine whether the own vehicle has a larger movement based on at least one of the movement in the vertical direction and the movement in the pitch direction of the own vehicle compared to the case in which the movement is small. existence probability distribution of the maximum values and the maximum value car dual driving support system you characterized by reducing the existence probability of the periphery of the obstacle in the vertical direction. Own vehicle state detection means for detecting the movement state of the own vehicle;
Operation state detection means for detecting the operation state of the host vehicle;
Obstacle detection means for detecting the position of obstacles around the host vehicle;
Presence probability distribution for estimating the presence probability distribution of the obstacle based on the position of the obstacle detected by the obstacle detection means and the nature of the detection error included in the position by the detection characteristics of the obstacle detection means An estimation means;
Based on the movement state of the own vehicle detected by the own vehicle state detection means, the operation state of the own vehicle detected by the operation state detection means, and the existence probability distribution of the obstacle estimated by the existence probability distribution estimation means. And an avoidance support means for supporting an obstacle avoidance operation by the driver,
The avoidance support means has a low existence probability when the existence probability of the obstacle on the future travel route of the host vehicle is high based on the existence probability distribution of the obstacle estimated by the existence probability distribution estimation means. If compared to the obstacle avoidance car dual driving support system you characterized by increasing the assist control amount to support the operation. Before Kimisao operation state detecting means, the vehicular driving support apparatus according to any one of claims 1 to 3, wherein the detecting at least one of the steering angle and the steering angular velocity of the vehicle. Own vehicle state detection means for detecting the movement state of the own vehicle;
Operation state detection means for detecting the operation state of the host vehicle;
Obstacle detection means for detecting the position of obstacles around the host vehicle;
Presence probability distribution for estimating the presence probability distribution of the obstacle based on the position of the obstacle detected by the obstacle detection means and the nature of the detection error included in the position by the detection characteristics of the obstacle detection means An estimation means;
Based on the movement state of the own vehicle detected by the own vehicle state detection means, the operation state of the own vehicle detected by the operation state detection means, and the existence probability distribution of the obstacle estimated by the existence probability distribution estimation means. Avoidance support means for supporting the obstacle avoidance operation by the driver,
Based on the presence probability distribution of the obstacle which the existence probability distribution estimation means has estimated, and a bypass route generating means for generating an avoidance path existence probability passes through lower region than the set value of the obstacle,
The avoidance support means sets a support control amount for supporting the obstacle avoidance operation based on a steering angle pattern for causing the host vehicle to travel the avoidance route and an operation state of the host vehicle. that car amphibious operation support device. Road boundary detecting means for detecting a road boundary around the host vehicle,
The vehicle driving support apparatus according to claim 5 , wherein the avoidance route generation unit generates a route that falls within the road boundary as the avoidance route. The avoidance path generating means, as the avoidance route, according to any one of claims 5 or 6, characterized in that to generate a path that maximizes or minimizes the evaluation function for evaluating the avoidance path numerically Driving support system for vehicles. The avoidance assist means, vehicular driving support apparatus according to any one of claims 5 to 7, characterized in that sequentially calculates the assist control amount to support the obstacle avoidance operation. The avoidance path generating means, said driving vehicle according to any one of claims 5, characterized in that to generate the avoidance path with the vehicle model considering friction circle of the tires of the vehicle 8 Support device. The position of the obstacle around the host vehicle is detected by the obstacle detection means, and based on the detected position and the nature of the detection error included in the position based on the characteristic of the obstacle detection means, the A vehicle for estimating obstacle existence probability distribution and supporting an obstacle avoidance operation by a driver based on the estimated obstacle existence probability distribution, the motion state of the host vehicle, and the operation state of the host vehicle. A driving support method,
The presence of the obstacle in the lateral direction of the host vehicle when the motion is large based on at least one of the lateral motion and the yaw motion of the host vehicle as compared to when the motion is small. A driving support method for a vehicle, characterized by reducing the highest value of the probability distribution and the existence probability around the highest value . The position of the obstacle around the host vehicle is detected by the obstacle detection means, and based on the detected position and the nature of the detection error included in the position based on the characteristic of the obstacle detection means, the A vehicle for estimating obstacle existence probability distribution and supporting an obstacle avoidance operation by a driver based on the estimated obstacle existence probability distribution, the motion state of the host vehicle, and the operation state of the host vehicle. A driving support method,
The presence of the obstacle in the longitudinal direction of the host vehicle when the motion is large based on at least one of the motion in the vertical direction and the motion in the pitch direction of the host vehicle as compared to the case where the motion is small. A driving support method for a vehicle, characterized by reducing the highest value of the probability distribution and the existence probability around the highest value. The position of the obstacle around the host vehicle is detected by the obstacle detection means, and based on the detected position and the nature of the detection error included in the position based on the characteristic of the obstacle detection means, the A vehicle for estimating obstacle existence probability distribution and supporting an obstacle avoidance operation by a driver based on the estimated obstacle existence probability distribution, the motion state of the host vehicle, and the operation state of the host vehicle. A driving support method,
Assistance for assisting the obstacle avoidance operation based on the obstacle existence probability distribution when the obstacle existence probability on the future travel route of the host vehicle is high compared to the case where the existence probability is low A vehicle driving support method characterized by increasing a control amount. The position of the obstacle around the host vehicle is detected by the obstacle detection means, and based on the detected position and the nature of the detection error included in the position based on the characteristic of the obstacle detection means, the A vehicle for estimating obstacle existence probability distribution and supporting an obstacle avoidance operation by a driver based on the estimated obstacle existence probability distribution, the motion state of the host vehicle, and the operation state of the host vehicle. A driving support method,
Based on the obstacle existence probability distribution, a steering angle pattern for generating an avoidance route that passes through an area where the obstacle existence probability is lower than a set value, and causing the host vehicle to travel the generated avoidance route, and A driving support method for a vehicle, wherein an assistance control amount for supporting the obstacle avoiding operation is set based on an operation state of the host vehicle.

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