JP2009116614A - Operation support device for vehicle - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To improve safety by controlling a vehicle to pass an optimal evasion route by taking into consideration not only a current risk but also a risk predicted in the future, and to achieve the traveling of a natural evasion route by suppressing useless acceleration. <P>SOLUTION: A control unit 5 sets a current total risk function by using a white line, guard rail, side wall and three-dimensional object existing in the periphery of its own vehicle 1 as an object, and predicts the temporal change of the total risk function by predicting the temporal change of the position of each object, and converts the total risk function in each distance ahead its own vehicle, and sets an evaluation function J based on the converted total risk function and the longitudinal acceleration ax0 and lateral acceleration ay0 of its own vehicle and a parameter showing the weight of a traveling time, and calculates the longitudinal acceleration ax0 and lateral acceleration ay0 of its own vehicle to minimize the evaluation function J as controlled variables. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention sets the degree of danger for white lines and three-dimensional objects around the vehicle detected by a stereo camera, a monocular camera, a millimeter wave radar, etc., performs steering control to drive the optimum route, or performs braking control. The present invention relates to a driving support apparatus for a vehicle to be performed.

  In recent years, in vehicles, the on-board camera or laser radar device detects the driving environment ahead, recognizes obstacles and leading vehicles from this driving environment data, and executes alarms, automatic brakes, and automatic steering for safety. Various technologies for improving the performance have been developed and put into practical use.

For example, in Japanese Patent Application Laid-Open Nos. 2004-110346 and 2007-253745, an obstacle present around the host vehicle is detected, a current risk potential for the obstacle of the host vehicle is calculated, and this risk potential is calculated. Based on the above, there are disclosed a technique for controlling the operation of the vehicle equipment so as to prompt the driver to perform driving operations related to the longitudinal and lateral movements of the host vehicle, and a technique for calculating the steering amount so as to avoid obstacles.
JP 2004-110346 A JP 2007-253745 A

  However, in the technique disclosed in Patent Document 1 described above, since control according to the current risk potential is performed, it is possible to effectively cope with the risk that changes due to movement of the host vehicle or an obstacle. There is a problem that you can not. In other words, even if the route seems to be optimal at present, there are many cases where the degree of risk will increase in the future, and it is difficult to appropriately respond to such an ever-changing traffic environment. There's a problem. In addition, it is natural and desirable to select an optimum avoidance route that can travel without unnecessary acceleration as much as possible.

  On the other hand, Patent Document 2 described above discloses a technique for calculating an avoidance route in consideration of a future risk level. However, in that technology, the future risk level is calculated by adding every minute time. Therefore, when the risk level is set by the size of the obstacle (vertical and horizontal length on the road), the obstacle is small and the If the difference in relative speed with respect to the vehicle is large, the distance resolution with respect to the obstacle becomes rough, and there is a possibility that an appropriate avoidance route cannot be obtained. Specifically, there is a possibility of calculating an avoidance route in which the vehicle speed is increased and the dangerous area is run in a short time without avoiding the obstacle.

  The present invention has been made in view of the above circumstances, and even when the size of the obstacle and the vehicle speed are different, control is performed so as to pass the optimum avoidance route in consideration of not only the present but also the predicted risk in the future. It is an object of the present invention to provide a vehicle driving support device that can improve safety and that can travel on a natural avoidance route while suppressing unnecessary acceleration.

  The present invention includes a surrounding environment recognizing unit for recognizing the surrounding environment of the host vehicle, a risk setting unit for setting a current risk level for each object in the recognized surrounding environment, and a position of each target for which the risk level is set. A risk change predicting means for predicting a temporal change of the risk and predicting a temporal change of the risk, and for each distance ahead of the host vehicle based on the temporal change of the risk predicted by the risk change predicting means. An evaluation function setting means for setting an evaluation function having a first calculation term of the risk function that is converted and set and a second calculation term that is set according to the acceleration state of the host vehicle at least for each forward distance; Control amount calculation means for calculating the acceleration state of the host vehicle that minimizes the evaluation function and outputting the acceleration state of the host vehicle as a control amount is provided.

  According to the vehicle driving support device of the present invention, even when the size of the obstacle and the vehicle speed are different, the vehicle is controlled so as to pass through the optimum avoidance route in consideration of not only the present but also the predicted risk in the future. Safety can be improved, and a natural avoidance route can be traveled by suppressing unnecessary acceleration.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.
1 to 6 show an embodiment of the present invention, FIG. 1 is a schematic configuration diagram of a driving support device mounted on a vehicle, FIG. 2 is a flowchart of a driving support control program, and FIG. 3 is a risk set for a three-dimensional object. 4 is an explanatory diagram of a risk function set for a three-dimensional object whose length in the front-rear direction is unknown, FIG. 5 is an explanatory diagram illustrating an example of a risk function set in front, and FIG. 6 is generated. It is explanatory drawing which shows an example of an avoidance route.

  In FIG. 1, reference numeral 1 denotes a vehicle such as an automobile (own vehicle), and a driving support device 2 is mounted on the vehicle 1. The driving support device 2 includes a stereo camera 3, a stereo image recognition device 4, a control unit 5, and the like as main parts.

  In addition, the host vehicle 1 is provided with a vehicle speed sensor 11 that detects the host vehicle speed V, a main switch 12 to which an ON-OFF signal for driving support control is input, and the host vehicle speed V is the same as that of the stereo image recognition device 4. An ON-OFF signal or the like for driving support control is input to the control unit 5.

  The stereo camera 3 is composed of a set of (left and right) CCD cameras using a solid-state imaging device such as a charge coupled device (CCD) as a stereo optical system. These left and right CCD cameras are respectively mounted at a certain interval in front of the ceiling in the vehicle interior, take a stereo image of an object outside the vehicle from different viewpoints, and input image data to the stereo image recognition device 4.

  The processing of the image from the stereo camera 3 in the stereo image recognition device 4 is performed as follows, for example. First, distance information is obtained from a pair of stereo image pairs captured in the traveling direction of the host vehicle 1 taken by the stereo camera 3 from the corresponding positional shift amount, and a distance image is generated. Then, based on this data, a well-known grouping process is performed and compared with frames (windows) such as three-dimensional road shape data, side wall data, and three-dimensional object data stored in advance. Side wall data such as guardrails and curbs that exist along the road are extracted, and three-dimensional objects are classified and extracted into other three-dimensional objects such as two-wheeled vehicles, ordinary vehicles, large vehicles, pedestrians, and utility poles.

  As shown in FIG. 3 (a), each of the above recognized data is calculated as a position in a coordinate system having the own vehicle 1 as the origin, the front-rear direction of the own vehicle 1 as the X axis, and the width direction as the Y axis. In particular, in the case of a three-dimensional object, the width Wy of each vehicle, and in the vehicle data of a two-wheeled vehicle, a normal vehicle, and a large vehicle, the length Wx in the front-rear direction is, for example, 3 m, 4.5 m, or 10 m in advance. Presumed. Based on the width Wy and the length Wx in the front-rear direction of the three-dimensional object, the center position of the three-dimensional object is calculated using the coordinates of (xobstacle, yobstacle). In addition, when the vehicle longitudinal direction length Wx can be obtained with high accuracy by inter-vehicle communication or the like, the above-described center position may be calculated using the length data. In addition, when the type of the three-dimensional object cannot be classified or the like, the front-rear direction length Wx of the three-dimensional object cannot be obtained. For example, as shown in FIG. You may make it consider that it is the same value as the width Wy.

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

  Each information obtained in this way, that is, white line data, guard rails existing along the road, side walls such as curbs, and solid object data (type, distance from own vehicle 1, center position (xobstacle, yobstacle), speed) (Vxobstacle, vyobstacle, etc.) is input to the control unit 5. Thus, in this embodiment, the stereo camera 3 and the stereo image recognition device 4 are provided as surrounding environment recognition means.

  The control unit 5 detects the vehicle speed V from the vehicle speed sensor 11, white line data from the stereo image recognition device 4, side wall data such as guardrails and curbs along the road, and solid object data (type, distance from the vehicle 1). , Center position (xobstacle, yobstacle), velocity (vxobstacle, vyobstacle), etc.) are input. Then, according to the driving support control program described later, based on each input signal described above, the current risk level is set as the risk function Dline, Dobstacle for each of the white line, guardrail, side wall, and three-dimensional object existing ahead. Then, the current total risk function D is set from these risk functions Dline and Dobstacle. Thereafter, the temporal change of the position of each target for which the total risk function D is set is predicted to predict the temporal change of the total risk function D, and the total risk function D is converted for each distance ahead of the host vehicle. An evaluation function J is set with the converted total risk function D, the longitudinal acceleration ax0 and the lateral acceleration ay0 of the host vehicle, and a parameter representing the weight of the running time, and the longitudinal acceleration ax0 of the host vehicle that minimizes the evaluation function J The lateral acceleration ay0 is calculated as a control amount. The lateral acceleration ay0 is converted into a target steering angle δft and a control signal is output to the automatic steering control device 23 serving as a steering control means to execute steering control, and the longitudinal acceleration ax0 is automatically used as a braking control means. A signal is output to the brake control device 22 to execute brake control. When signals are output to the automatic brake control device 22 and the automatic steering control device 23, the signals are visually displayed on the display 21 to notify the driver. That is, the control unit 5 is configured to have functions as a risk level setting unit, a risk level change prediction unit, an evaluation function setting unit, and a control amount calculation unit.

Next, the driving support control program executed by the driving support device 2 will be described with reference to the flowchart of FIG.
First, in step (hereinafter, abbreviated as “S”) 101, necessary parameters, specifically, white line data, side data of guardrails, curbs, etc. existing along the road, and three-dimensional object data (type, own vehicle 1 Distance, center position (xobstacle, yobstacle), speed (vxobstacle, vyobstacle), etc.) and own vehicle speed V are read.

Next, the process proceeds to S102, and the current risk function Dline for the white line (the guard rail and the side wall are handled in the same way as the white line) is calculated by the following equation (1).
Dline = exp (aR · y ⁴ ) −1 ... (1)
aR = (2 / WR) ⁴ · log (Ds + 1) (2)
Here, WR indicates the road width, and Ds indicates a preset risk degree at the road edge (ie, y = ± WR / 2).

Next, the process proceeds to S103, and the current risk function Dobstacle for a three-dimensional object (two-wheeled vehicle, ordinary vehicle, large vehicle, pedestrian, utility pole or other three-dimensional object) is targeted so that the risk level is smoothly distributed. The calculation is performed according to the following equation (3).
Dobstacle = DG · exp (−px · (x−xobstacle) ²
−py · (y-yobstacle) ² ) (3)
DG = DEx · (DEy / DR) ^{Wy · Wy / (4 · q · (Wy + q))} (4)
px = − (2 / Wx) ² · log (DEx / DR) (5)
py = (1 / (b · (Wy + q))) · log (DEy / DR) (6)
Here, DEx, DEy, DR, and q are preset as shown in FIG. That is, the three-dimensional object is set so that the risk set in the front-rear direction is different from the risk set in the left-right direction, and the risk set in the front-rear direction is the center position (xobstacle in FIG. 3C). , Yobstacle), the risk level of the front and rear upper ends is set to a preset value DEx as shown in the X-direction cross section. Further, as shown in the Y-direction cross section passing through the center position (xobstacle, yobstacle) in FIG. 3B, the risk degree set in the left-right direction is such that the risk degree at the left and right upper ends becomes a preset value DEy. Further, the degree of risk at a position q away from the left and right edges is set to a preset value DR. In other words, the driver feels a difference between the case of avoiding the existing three-dimensional object by braking / driving control and the case of avoiding by passing through the side of the three-dimensional object. By setting the degree of danger to be set differently in the direction, natural avoidance control according to actual avoidance driving can be performed.

Next, it progresses to S104 and calculates the present total risk function D by the following (7) Formula. Thereby, the front total risk function D is expressed as shown in FIG. 5, for example.
D = Dline + Dobstacle (7)

Next, in S105, the position of the three-dimensional object (xobstacle (t), yobstacle (t)) after t seconds is estimated by the following equation (8).
(Xobstacle (t), yobstacle (t))
= (Xobstacle + vxobstacle.t, yobstacle + vyobstacle.t) (8)

  Next, the process proceeds to S106, and the position of the three-dimensional object (xobstacle (t), yobstacle (t)) estimated in S105 is substituted into x and y of the total risk function D calculated in S104. Then, the total risk function D (xobstacle (t), yobstacle (t)) after t seconds is set.

Next, the process proceeds to S107, and the vehicle position information at the forward distance S is calculated. The vehicle position after t seconds can be expressed by using vx0 for the longitudinal velocity of the vehicle, vy0 for the lateral velocity, ax0 for the longitudinal acceleration, and ay0 for the lateral acceleration.
(Vx0 · t + (1/2) · ax0 · t ² ,
vy0 · t + (1/2) · ay0 · t ² ) (9)

Then, at the S = vx0 · t + (1/2 ) · ax0 · t 2, when calculated back time t, the time ts required to reach the forward distance S, is given by the following equation (10).

ts = (− vx0 + (vx0 ² + 2 · ax0 · S) ^1/2 ) / ax0 (10)
Further, the lateral position of the host vehicle at the forward distance S can be obtained by substituting the time ts obtained by the above equation (10) into the Y coordinate of the equation (9).

Next, in S108, the own vehicle position information at the forward distance S calculated in S107 is substituted into the total risk function D (xobstacle (t), yobstacle (t)) after t seconds set in S106. Then, the total risk function D (s) converted for each distance S in front of the host vehicle is set, and the evaluation function J shown in the following equation (11) is set.
J = ∫ (K1 · D (s)) ds (integral range 0 to S)
+ ∫ (C + K 2 · ax 0 ² + K 3 · ay 0 ² ) dt (integral range 0 to Tf) (11)
Here, C is a parameter representing the weight of travel time, K1 is a weight parameter for risk, K2 is a weight parameter for longitudinal acceleration, and K3 is a weight parameter for lateral acceleration.

That is, the calculation term of ∫ (K1 · D (s)) ds (integration range 0 to S) of the evaluation function J is the first calculation term, and ∫ (C + K2 · ax0 ² + K3 · ay0 ² ) dt ( The calculation term in the integration range 0 to Tf) is the second calculation term.

Then, the process proceeds to S109, where the longitudinal acceleration ax0 and the lateral acceleration ay0 that minimize the evaluation function J set in S108 described above are obtained by a well-known optimization calculation. The lateral acceleration ay0 is converted to the target steering angle δft by the following equation (12) and output to the automatic steering control device 23, and the program exits. An example of an avoidance route that travels with the longitudinal acceleration ax0 and the lateral acceleration ay0 that minimizes the evaluation function J is shown in FIG.
δft = ((N · (1 + A0 · V ² )) / V ² ) · ay0 (12)
Here, N is a steering gear ratio, and A0 is a stability factor.

  That is, the evaluation function J set in the present embodiment reduces the risk as much as possible by the first calculation term based on the total risk function D, and by the second calculation term based on the longitudinal acceleration ax0 and the lateral acceleration ay0 of the host vehicle. By suppressing unnecessary acceleration and including a parameter representing the weight of the travel time in the second calculation term, it is possible to travel an efficient avoidance route that does not require time.

  In the present embodiment, an example in which brake control and steering control can be performed has been described, but either one may be performed.

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

  Furthermore, in this embodiment, the surrounding environment is recognized based on the captured image from the stereo camera 3, but it may also be detected by a monocular camera, millimeter wave radar, or the like.

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

Schematic configuration diagram of a driving support device mounted on a vehicle Flow chart of driving support control program Explanatory drawing of the risk function set for a three-dimensional object Explanatory drawing of the risk function set for a three-dimensional object whose longitudinal length is unknown Explanatory drawing which shows an example of the risk function set ahead Explanatory drawing which shows an example of the avoidance route produced | generated

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Own vehicle 2 Driving support device 3 Stereo camera (Ambient environment recognition means)
4 Stereo image recognition device (peripheral environment recognition means)
5 Control unit (risk level setting means, risk level change prediction means, evaluation function setting means, control amount calculation means)
11 Vehicle speed sensor 12 Main switch 21 Display 22 Automatic brake control device (braking control means)
23 Automatic steering control device (steering control means)

Claims (5)

A surrounding environment recognition means for recognizing the surrounding environment of the host vehicle;
Risk level setting means for setting the current risk level for each of the recognized surrounding environment objects;
A risk change predicting means for predicting a time change of the risk by predicting a time change of the position of each target for which the risk is set;
A first calculation term of a risk function that is converted and set for each distance ahead of the host vehicle based on the temporal change in the degree of risk predicted by the risk level change predicting means, and acceleration of the host vehicle at least for each distance ahead. An evaluation function setting means for setting an evaluation function having a second operation term set in accordance with the state;
Control amount calculation means for calculating the acceleration state of the host vehicle that minimizes the evaluation function and outputting the acceleration state of the host vehicle as a control amount;
A vehicle driving support apparatus comprising:   2. The vehicle driving support apparatus according to claim 1, wherein the risk level setting unit sets the risk level based on a risk level corresponding to a three-dimensional object existing in the vicinity and a risk level corresponding to a travel lane.   The risk level corresponding to the three-dimensional object existing in the surroundings is set such that the risk level set in the front-rear direction of the three-dimensional object is different from the risk level set in the left-right direction. Vehicle driving support device.   The vehicle according to any one of claims 1 to 3, wherein the second calculation term of the evaluation function set by the evaluation function setting means includes a calculation term representing a weight of travel time. Driving assistance device.   5. The system according to claim 1, further comprising at least one of a steering control unit that performs steering control based on the control amount calculated by the control amount calculation unit and a braking control unit that performs braking control. The vehicle driving support apparatus according to one of the above.

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