JP2010202139A - Driving operation support device and driving operation support method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To reduce a sense of discomfort felt by a driver with respect to avoidance control. <P>SOLUTION: A microprocessor 9 relatively changes evaluation weight w<SB>f</SB>on time series data δ<SB>SR</SB>* of steering operation input based on information input from stereo cameras 2a, 2b, an acceleration sensor 3, a yaw rate sensor 4, wheel speed sensors 5a, 5b, 5c, 5d and a steering angle sensor 6. An appropriate steering operation quantity corresponding to the external environment of a vehicle 1 and a vehicle state is thereby computed to reduce the sense of discomfort felt by the driver with respect to avoidance control. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a driving operation support device and a driving operation support method that support a vehicle operation performed by a driver to avoid contact with an obstacle.

  Conventionally, a steering pattern for avoiding contact with an obstacle existing in front of the traveling path of the host vehicle within the range of the traveling path is calculated by optimization calculation, and the host vehicle is driven so as to travel according to the calculated steering pattern. A vehicle control device for controlling is disclosed.

International Publication No. 2006/070865 Pamphlet

  When calculating a steering pattern that satisfies multiple conditions such as not contacting an obstacle or not deviating from the road by optimization calculation, in order to make the vehicle state advantageous as soon as possible with respect to the multiple conditions, the initial steering is performed. A steering pattern that makes the amount as large as possible may be calculated. However, when the host vehicle is controlled to travel according to such a steering pattern, the driver may feel uncomfortable with the avoidance control due to suddenly large avoidance control intervening.

  The present invention has been made to solve the above-described problems, and an object of the present invention is to provide a driving operation support apparatus and a driving operation support method that can reduce the uncomfortable feeling felt by the driver with respect to avoidance control.

  A driving operation support device and a driving operation support method according to the present invention provide relative weights of evaluation items constituting an evaluation function for evaluating a predicted traveling locus of a host vehicle according to an external environment and a traveling state of the host vehicle. Change to

  According to the driving operation support device and the driving operation support method according to the present invention, the weights of the evaluation items are appropriately set according to the external environment and the state of the host vehicle. The amount of operation can be calculated, and the uncomfortable feeling felt by the driver for avoidance control can be reduced.

It is a block diagram which shows the structure of the driving operation assistance apparatus used as the 1st Embodiment of this invention. It is a functional block diagram of the driving operation support device shown in FIG. It is a flowchart figure which shows the flow of the driving operation assistance process used as the 1st Embodiment of this invention. It is a figure which shows the driving | running | working scene where an obstacle crosses from the right side ahead of the driving lane of a vehicle toward the left side when the vehicle is driving | running | working the straight road of one lane of one side. It is a flowchart figure which shows the flow of a process of step S2 shown in FIG. It is a schematic diagram for demonstrating the parameter of a vehicle model. It is a flowchart figure which shows the flow of a process of step S3 shown in FIG. (A) is a diagram showing a time series change in the evaluation weights w _f steering operation input when the time-series change (b) a low urgency rating weights w _f steering operation input when there is a high degree of urgency. It is a flowchart figure which shows the flow of a process of step S4 shown in FIG. It is a figure which shows the optimization calculation result of steering angle command value (delta) _SR ^* in a prior art and this invention. It is a functional block diagram which shows the structure of the driving operation assistance apparatus used as the 2nd Embodiment of this invention. It is a flowchart figure which shows the flow of the evaluation weight setting process in the driving operation assistance apparatus which becomes the 2nd Embodiment of this invention. Evaluation formula L ₂ ', L _3' and the evaluation formula L ₂ ", L _3" is a diagram showing a time series change of. (A) is a diagram showing a time series change of the steering operation input evaluation weights w _f and (b) the correction factor K _C. It is a schematic diagram for demonstrating the correction process of evaluation weight. It is a block diagram which shows the structure of the driving operation assistance apparatus used as the 3rd Embodiment of this invention. It is a figure which shows the driving | running scene where the front of the road where a vehicle drive | works draws a curve, and the obstruction has stopped in the position approaching a curve part. It is a schematic diagram for demonstrating the parameter of a vehicle model. It is a flowchart figure which shows the flow of the evaluation weight setting process in the driving operation assistance apparatus which becomes the 3rd Embodiment of this invention. (A) is a diagram showing a time series change in the evaluation weights w _M and (b) the correction factor K _C1 (c) the correction factor K _C2. It is a schematic diagram for demonstrating parameter (DELTA) (theta). It is a flowchart figure which shows the flow of the evaluation weight setting process in the driving operation assistance apparatus which becomes the 4th Embodiment of this invention. It is a diagram for explaining a method of determining evaluation weights w _b with respect to the obstacle O. It is a flowchart figure which shows the flow of a process of step S73 shown in FIG. It is a figure for demonstrating the determination method of evaluation weight _wrR with respect to a right road boundary. It is a flowchart figure which shows the flow of a process of step S76 shown in FIG. It is a diagram for describing a method of updating the evaluation weights w _rR for the evaluation weights w _b and the right road boundary with respect to the obstacle.

Hereinafter, with reference to the drawings, a driving operation support device and its operation (driving operation support method) according to first to fourth embodiments of the present invention will be described.
[First Embodiment]
First, the configuration of the driving operation support apparatus according to the first embodiment of the present invention will be described.
[Configuration of driving support device]
As shown in FIG. 1, the driving operation support device according to the first embodiment of the present invention is mounted on a vehicle 1 having a steering system capable of controlling the steering amount of the front wheels, and includes stereo cameras 2 a and 2 b and an acceleration sensor 3. The yaw rate sensor 4, the wheel speed sensors 5a, 5b, 5c, 5d, the steering angle sensor 6, the EPS (electric power steering) motor 7, the EPS controller 8, and the microprocessor 9 are provided as main components.

  The stereo cameras 2a and 2b are provided in front of the passenger compartment and take images of the front of the vehicle 1. The stereo cameras 2a and 2b perform image processing on the image in front of the vehicle 1 to detect obstacles ahead of the vehicle 1, distances between the vehicles 1 and obstacles, roads (traveling roads), road boundaries (white lines, etc.), etc. The external environment information of the vehicle 1 is detected, and the detection result is input to the microprocessor 9. The stereo cameras 2a and 2b function as the external environment detection means 13 according to the present invention including the obstacle detection means 11 and the road boundary detection means 12 shown in FIG.

  The acceleration sensor 3 is configured by a known acceleration detection device formed by a piezoelectric element or the like, and detects acceleration in a specific direction generated in the vehicle 1. The acceleration sensor 3 inputs the detection value to the microprocessor 9. In the present embodiment, the acceleration sensor 3 detects the acceleration generated in the front-rear direction and the vehicle width direction of the vehicle 1 and integrates the detected values at predetermined time intervals, thereby speeding the vehicle 1 in the front-rear direction and the vehicle width direction. Is also configured to be detectable.

  The yaw rate sensor 4 is configured by a known yaw rate detection device formed of a crystal resonator, a semiconductor device, or the like, and detects the yaw rate generated at the center of gravity position of the vehicle 1. The yaw rate sensor 4 inputs the detection value to the microprocessor 9. The wheel speed sensors 5 a, 5 b, 5 c and 5 d detect the vehicle speed based on pulse signals generated according to the wheel rotation of the front, rear, left and right wheels of the vehicle 1, and input the detected values to the microprocessor 9.

  The steering angle sensor 6 is configured by an encoder installed on the pinion side of the front wheel steering mechanism configured by a rack and pinion system, and detects the steering angle (rotation angle) of the steering of the vehicle 1. The steering angle sensor 6 inputs a detection value to the microprocessor 9. The acceleration sensor 3, the yaw rate sensor 4, the wheel speed sensors 5a, 5b, 5c, 5d, and the steering angle sensor 6 function as the own vehicle state detection means 14 according to the present invention shown in FIG.

  The EPS motor 7 controls the steering torque in accordance with a control signal from the EPS controller 8. The EPS controller 8 calculates an assist torque based on information from a torque sensor (not shown) that detects the degree of twisting of the steering, and controls the EPS motor 7 based on the calculation result. In this embodiment, the steering torque is controlled by EPS control, but the steering torque may be controlled by hydraulic control. The EPS motor 7 and the EPS motor controller 8 function as the own vehicle control means 17 according to the present invention shown in FIG.

The microprocessor 9 includes an integrated circuit formed by an A / D conversion circuit, a D / A conversion circuit, a central processing unit, a memory, and the like, and includes stereo cameras 2a and 2b, an acceleration sensor 3, a yaw rate sensor 4, a wheel speed. Based on information input from the sensors 5a, 5b, 5c, 5d and the steering angle sensor 6, the operation of the EPS controller 8 is controlled. The microprocessor 9 functions as an evaluation function setting unit 15 and an optimum operation amount calculation unit 16 including the contact risk determination unit 18 according to the present invention shown in FIG. 2 when the internal CPU executes a control program.
[Driving operation support processing]
In the driving operation support apparatus having such a configuration, the microprocessor 9 executes the driving operation support process described below, thereby reducing the uncomfortable feeling that the driver feels with respect to the avoidance control. Hereinafter, with reference to the flowchart shown in FIG. 3, the operation of the microprocessor 9 when the driving operation support process is executed will be described.

  The flowchart shown in FIG. 3 starts at the timing when the ignition switch of the vehicle 1 is switched from the off state to the on state, and the driving operation support process proceeds to step S1. In the following, as shown in FIG. 4, an example of a traveling scene in which an obstacle O crosses from the right side to the left side in front of the traveling lane of the vehicle 1 when the vehicle 1 is traveling on a straight road with one lane on one side. The operation of the microprocessor 9 when the driving operation support process is executed will be described.

In the process of step S1, the microprocessor 9 loads the captured images of the stereo cameras 2a and 2b and the detection values of the respective sensors into the internal memory, and stores the position information of the vehicle 1, the obstacle O, and the road boundary in the same coordinate system. Therefore, the coordinate system used in the subsequent processing is determined based on the captured images of the stereo cameras 2a and 2b. In the present embodiment, the microprocessor 9 sets a coordinate system in which the traveling direction of the vehicle 1 is the X axis and the direction perpendicular to the X axis is the Y axis, as shown in FIG. The microphone processor 9 then coordinates (X _v , Y _v ) of the center of gravity G of the vehicle 1 in the set coordinate system, coordinates G (X _B , Y _B ) of the rear center position G of the obstacle O, the left and right roads The Y coordinates Y _L and Y _{R of} the boundary and the Y coordinate Y _C of the center line of the road are calculated. Further, the microprocessor 9 calculates the moving speed of the vehicle 1 and the obstacle O in the set coordinate system. In the present embodiment, since the vehicle 1 is traveling along the X-axis direction, the speed (V _Vx , V _Vy ) of the vehicle 1 is calculated from the detected values of the wheel speed sensors 5a, 5b, 5c, 5d (V _V , 0). Since the obstacle O is moving along the Y-axis direction, the speed (V _Bx , V _By ) of the obstacle O is the difference between the position information of the obstacle acquired this time and the position information of the obstacle acquired last time. Based on the value, (0, V _B ) is calculated. Thereby, the process of step S1 is completed and a driving operation assistance process progresses to the process of step S2.

  In the process of step S2, the microprocessor 9 determines whether or not the vehicle 1 may come into contact with the obstacle O within a predetermined time based on the coordinate value calculated by the process of step S1 (determination). processing). As a result of the determination, if there is no possibility that the vehicle 1 will contact the obstacle O within the predetermined time, the microprocessor 9 returns the driving operation support process to the process of step S1. On the other hand, if the vehicle 1 may come into contact with the obstacle O within the predetermined time, the microprocessor 9 advances the driving operation support process to the process of step S3. Details of this determination processing will be described later with reference to a flowchart shown in FIG.

In the process of step S3, the microprocessor 9 sets the weight of the evaluation item constituting the evaluation function (evaluation weight setting process). Specifically, the microprocessor 9, the start of the evaluation time time t _0, from evaluation starting time t ₀ time T _f destination time t ₀ + T _f the evaluation ending time, discrete this section with a suitable number of divisions N An evaluation weight is set for N pieces of time series data of each evaluation item corresponding to the division number N. Details of the evaluation function and evaluation weight setting processing will be described later with reference to the flowchart shown in FIG. Thereby, the process of step S3 is completed and a driving operation assistance process progresses to the process of step S4.

  In the process of step S4, the microprocessor 9 sets an evaluation function using the evaluation weight set in the process of step S3, and performs optimization calculation using the set evaluation function, thereby starting avoidance control. A vehicle operation amount from time to end time is calculated (evaluation function setting process and optimization calculation process). Details of the evaluation function setting process and the optimization calculation process will be described later with reference to the flowchart shown in FIG. Thereby, the process of step S4 is completed and the driving operation support process proceeds to the process of step S5.

In the process of step S5, the microprocessor 9 outputs the vehicle operation amount from the start time to the end time of the avoidance control calculated by the process of step S4 to the EPS motor controller 8 as a command value. The EPS motor controller 8 By controlling the operation of the EPS motor 7 in accordance with the command value output from the microprocessor 9, avoidance control for avoiding the vehicle 1 from contacting an obstacle is executed. Thereby, the process of step S5 is completed and a series of driving operation support processes are completed.
[Discrimination process]
Next, the determination process in step S2 will be described in detail with reference to the flowchart shown in FIG.

In the process of step S11, the microprocessor 9 determines the position of the vehicle 1 based on the center-of-gravity position G (X _v , Y _v ) of the vehicle 1 at the current time t ₀ and the size of the vehicle 1 previously stored in the memory. The position coordinates of the four corners are calculated. Further, the microprocessor 9 determines the four corners of the obstacle O based on the rear center position G (X _B , Y _B ) of the obstacle O and the size of the obstacle O estimated from the captured images of the stereo cameras 2a and 2b. Calculate the position coordinates. The microprocessor 9 then, based on the position coordinates of the four corners of the vehicle 1 and the obstacle O, the area S _v (t ₀ ) of the vehicle 1 and the area S _{B of the} obstacle O in the coordinate system set by the processing of step S1. (T ₀ ) is calculated.

In this embodiment, the left front coordinate of the vehicle 1 is (X _v1 , Y _v1 ), the right front coordinate is (X _v2 , Y _v2 ), the left rear coordinate is (X _v3 , Y _v3 ), and the right rear coordinate is (X _v4). , Y _v4 ), the left front coordinate of the obstacle O is (X _B1 , Y _B1 ), the right front coordinate is (X _B2 , Y _B2 ), the left rear coordinate is (X _B3 , Y _B3 ), and the right rear coordinate is This is expressed as (X _B4 , Y _B4 ). In the following, the subscripts of the variables representing the coordinates of the vehicle 1 or the obstacle O are “1” for the left front position, “2” for the right front position, “3” for the left rear position, “4”. In the case of, it represents the right rear position. Thus, the position coordinates of the four corners of the vehicle 1 and the obstacle O are the _distance from the center of gravity position G (X _B , Y _B ) of the vehicle 1 to the front side surface d _Vxf , and the center of gravity position G (X _B , Y _B) from to the rear side lengths d _VXR, 2d _Vy the width of the vehicle 1, by the vertical width and the horizontal width both in 2d _B of the obstacle of O, represented by the following formula 1-4 . Therefore, the area S _v (t ₀ ) of the vehicle 1 and the area S _{B of the} obstacle O at the current time t ₀ are calculated from the coordinates of the four corners of the vehicle 1 and the obstacle O at the current time t ₀ calculated by the following equations 1 to 4. (T ₀ ) can be calculated. Thereby, the process of step S11 is completed and the determination process proceeds to the process of step S12.

In the process of step S12, the microprocessor 9 determines whether there is an area where the area S _v (t) of the vehicle 1 and the area S _B (t) of the obstacle O overlap at time t based on the following formula 5. Determine whether. Specifically, when the following Equation 5 does not hold at time t, the microprocessor 9 determines that there is an overlapping region and the vehicle 1 and the obstacle O may contact each other. On the other hand, when the following Expression 5 is satisfied at time t, the microprocessor 9 determines that there is no overlapping region and the possibility that the vehicle 1 and the obstacle O are in contact with each other is low. If there is an overlapping area as a result of the determination, the microprocessor 9 sets time t = t ₀ + T _TC (time T _TC : time until the vehicle 1 comes into contact with the obstacle O), and then performs a determination process. The process proceeds to step S3. On the other hand, if there is no overlapping area, the microprocessor 9 advances the determination process to the process of step S13.

In the process of step S13, when the microprocessor 9 advances the time t _CJ by determining whether or not the following formulas 6 and 7 are satisfied, the time t changes the current time t ₀ to the predetermined time T It is determined whether the time is earlier than the time when the _{CJ is} advanced. If the following expressions 6 and 7 are satisfied as a result of the determination, the microprocessor 9 determines that the time t is a time before the time obtained by advancing the current time t ₀ by a predetermined time T _CJ, and performs the determination process. The process proceeds to S14. On the other hand, if the following formulas 6 and 7 are not satisfied, the microprocessor 9 determines that the time t is not a time before the time obtained by advancing the current time t ₀ by a predetermined time T _CJ , and performs the determination process. The process returns to S1.

In the process of step S14, the microprocessor 9 calculates the region S _v (t) of the vehicle 1 and the region S _B (t) of the obstacle O at the time t from the speed information of the vehicle 1 and the obstacle O at the time t. . Specifically, in the traveling scene shown in FIG. 4, the vehicle 1 travels straight on the road, and the obstacle O crosses the road. Therefore, the four corner coordinates are expressed by the following equations 8 to 11. Therefore, the microprocessor 9 calculates the region S _v (t) of the vehicle 1 and the region S _B (t) of the obstacle O at time t using the following formulas 8 to 11.

As a method of predicting the trajectory of the vehicle 1 more accurately than the trajectory prediction method based on the speed information at the time t, there is a method of predicting the trajectory of the vehicle 1 using a vehicle model representing the motion of the vehicle 1. The following formulas 12 to 18 show an example of this vehicle model. The parameters θ, ν, β, γ, and δ _W in the mathematical formula respectively indicate the yaw angle, speed, slip angle, yaw rate, and front wheel turning angle of the vehicle 1 as shown in FIG. X _v , Y _v ) are treated as state variables of the vehicle 1 in this embodiment. Parameters δ _S and a _lon indicate the steering angle and the vehicle longitudinal acceleration / deceleration, respectively, and are treated as vehicle inputs in the present embodiment. Parameters M, I, L _f , L _r , N _G , and T _S are the vehicle mass, the inertia, the distance from the center of gravity G (X _v , Y _v ) of the vehicle 1 to the front wheel axis, and the center of gravity of the vehicle 1, respectively. A distance from G (X _v , Y _v ) to the rear wheel axle, a steering gear ratio, and a steering system time constant are shown and are vehicle parameters in the present embodiment.

Generally, the tire lateral force has a non-linear characteristic with respect to the tire slip angle, but it can be assumed to be linear when the tire slip angle is small. Since the tire slip angle can be regarded as being small in the normal driving region as shown in the driving scene shown in FIG. 4, the slip angles of the front and rear wheels are set to α _f and α _r , respectively, and the cornering power of the front and rear wheels is set to Assuming that K _f and K _r respectively, the tire lateral forces Fy _f and Fy _r of the front wheels and rear wheels of the vehicle 1 can be expressed as the following Expressions 19-22.

When the vehicle models shown in the above Expressions 12 to 22 are used, the motion state D _V of the vehicle 1 is a seven-dimensional vector, and the input u _V for operating the vehicle is a two-dimensional vector as in Expressions 23 to 25 below. expressed.

Each element of the initial state vector D _V (t ₀ ) of the vehicle model shown in Expression 23 is obtained as follows. The elements X _V and Y _V become the coordinate values X _V and Y _V calculated by the processing in step S1. The element γ is obtained from the detection value of the yaw rate sensor 4. The element θ is obtained by a method of integrating the detection value of the yaw rate sensor 4 on the basis of the vehicle posture at a certain time point or a method of determining from the angle formed by the traveling path of the vehicle 1 and the vehicle 1 from the captured images of the stereo cameras 2a and 2b. It is done. The element δ _W is obtained by dividing the steering angle δ _S by the gear ratio _NG . The element β is obtained by the following expression 26 as the speeds ν _X and ν _{Y in the} longitudinal direction of the vehicle 1 and in the vehicle width direction.

As the speeds ν _X and ν _{Y in the} longitudinal direction and the vehicle width direction of the vehicle 1, values obtained by integrating accelerations in the vehicle longitudinal direction and the vehicle width direction detected by the acceleration sensor 3 attached to the vehicle 1 can be used. Assuming that the slip angle β is very small, the vehicle speed can be approximated as ν = ν _X. The initial state vector D _V (t ₀ ) of the vehicle model can be acquired by the above processing. When the vehicle model in the initial state is continuously given the input u _V shown in Formula 24, the trajectory of the center of gravity position G (X _v , Y _v ) of the vehicle 1 can be obtained. Then, using the center-of-gravity coordinates (X _v (t), Y _v (t)) and the vehicle attitude θ (t) at the future time t, the position coordinates of the four corners of the vehicle 1 at the future time are expressed by the following equations 27-30. It is expressed in By calculating the area S _v (t) of the vehicle 1 and the area S _B (t) of the obstacle O using this position coordinate and performing contact determination, the area of the vehicle 1 is calculated using Equations 8 and 9. Reliability can be expected to be improved as compared with the case where contact determination is performed by calculating S _v (t) and the area S _B (t) of the obstacle O. Thereby, the process of step S14 is completed, and the driving operation support process returns to the process of step S12.

[Evaluation weight setting process]
Next, the evaluation weight setting process in step S3 will be described in detail with reference to the flowchart shown in FIG.

In the present embodiment, the evaluation function is based on the predicted value of the vehicle state vector D _V to the input u _V was applied to the vehicle 1 during the period from the present time t ₀ to a predetermined estimation time t ₀ + T _f, the following equation 31 It can be expressed as Here, the first term on the right side of Equation 31 is an equation for evaluating the vehicle motion state at the predetermined estimated time t ₀ + T _f (terminating evaluation equation), and the second term is from the current time t ₀ to the predetermined estimated time t ₀ + T _f . It is a formula (section evaluation formula) which evaluates a vehicle motion state in a section.

In general, the longer the time T _f , the longer the future can be predicted. However, when the time T _f is increased, the calculation load on the microprocessor 9 increases. Therefore, the microprocessor 9 weights the following evaluation items (1) to (4) by using the parameters w ₁ , w ₂ , w ₃ , and w ₄ , respectively.
(1) Terminal evaluation item 1: After the time _Tf , the vehicle posture is directed toward the road. It is represented by an evaluation formula ψ ₁ (t ₀ + T _f ) shown in the following formula 32. In Equation 32, the parameter θ ^* indicates the target posture angle after time T _f from time t ₀ set from the road information obtained at the current time t ₀ .

(2) Section evaluation item 2: Keep away from obstacles ahead for time _Tf . It is represented by an evaluation formula L ₂ (τ) shown in the following formula 33. In Expression 33, parameters σ _X and σ _Y indicate parameters representing the shape of the function.

(3) Section evaluation item 3: _Prevent the vehicle from deviating from the road boundary during the time _Tf . It is represented by an evaluation formula L ₃ (τ) shown in the following formula 34. In Equation 34, the parameter Δ indicates a margin width approaching the road boundary.

(4) Section evaluation item 4: During the time _Tf , the steering control input of the vehicle is made as small as possible. It is represented by an evaluation formula L ₄ (τ) shown in the following formula 35.

これにより、数式３１の第１項及び第２項はそれぞれ以下の数式３６，３７のように表される。

Thereby, the first term and the second term of Expression 31 are expressed as Expressions 36 and 37 below, respectively.

The time series data δ _SR ^* of the steering operation input from the time t ₀ that minimizes this evaluation function to the predetermined estimated time t ₀ + T _f is shown in the following equation 38. The evaluation weight w _f related to the time series data δ _SR ^* of the steering operation input represented by Expression 38 is expressed by Expression 39 below.

  Here, the larger the parameter N, the shorter the sampling time interval and the more accurate the predicted steering operation amount can be calculated. However, since the number of data used for the calculation greatly increases, the calculation load on the microprocessor 9 increases. End up. Therefore, it is desirable to set the parameter N so that the sampling time interval is about 10 to 100 msec. Hereinafter, the method for deriving Equation 39 will be described in detail with reference to the flowchart shown in FIG.

In the process of step S21, the microprocessor 9 sets a maximum value w _fMAX and a minimum value w _fMIN that can be taken by the evaluation weight w _f regarding the time series data δ _SR ^* of the steering operation input. Thereby, the process of step S21 is completed, and the derivation process proceeds to the process of step S22.

In the process of step S22, the microprocessor 9, the time that the vehicle 1 is supposed to have high urgency from time to contact with the obstacle O is preset as T _EM, time used in the process of step S2 T associate a time which is obtained by subtracting the time T _EM from _TC to evaluation time. Thereby, the process of step S22 is completed, and the derivation process proceeds to the process of step S23.

In the process of step S23, the microprocessor 9 determines whether or not the time T _TC and the time T _EM satisfy the condition shown in Equation 40 below. If the condition is satisfied as a result of the determination, the microprocessor 9 advances the derivation process to the process of step S24. On the other hand, if the condition is not satisfied, the microprocessor 9 advances the derivation process to the process of step S25.

In the process of step S24, the microprocessor 9 determines that the degree of urgency in the current traveling scene is high, and as shown in FIG. 8A, all the evaluation weights w _f relating to the time series data δ _SR ^* of the steering operation input. _Is set to the minimum value w _fMIN . Thereby, the process of step S24 is completed and a series of derivation processes are completed.

In the process of step S25, the microprocessor 9 calculates a setting parameter T _wf for calculating each element of the evaluation weight w _f . Specifically, the setting parameter T _wf is a time constant when it falls from the maximum value w _fMAX of the evaluation weight w _f regarding the time series data δ _SR ^* of the steering operation input to the minimum value w _{fMIN with a} first-order delay. Therefore, as shown in FIG. 8B, the microprocessor 9 _sets the interval between the maximum value w _fMAX and the minimum value w _fMIN of the evaluation weight w _f regarding the time series data δ _SR ^* of the steering operation input as 100%. Then, a setting parameter T _wf that _falls with a first-order delay from time t ₀ and reaches Q% at time T _TC −T _EM is calculated. In addition, since it is assumed that the driving scene is already highly urgent at the time T _TC -T _EM , it is desirable to set Q% within a range of about 75 to 95%. Thereby, the process of step S25 is completed, and the derivation process proceeds to the process of step S26.

In the process of step S26, the microprocessor 9 determines the elements of the evaluation weights w _f using the setting parameters T _wf calculated by the process at step S25. Specifically, the microprocessor 9 sets the evaluation weight at time t ₀ that is the evaluation start time as w _fMAX as shown in the following formula 41, and then the time t = T _TC based on the following formula 42. to calculate the value of each element of the until the -T _EM. Thereby, the process of step S26 is completed and a series of setting processes are complete | finished.

[Evaluation function setting process and optimization calculation process]
Next, the evaluation function setting process and the optimization calculation process in step S4 will be described in detail with reference to the flowchart shown in FIG.

In the process of step S31, the microprocessor 9 sets a vehicle model and an obstacle model necessary for performing the optimization calculation of the evaluation function. As the vehicle model, the vehicle model set in the process of step S2 can be used. That vehicle model D _V is expressed by the following equation 43-45. Although a vehicle model different from that set in the process of step S2 can be used, it is necessary to provide means for detecting or estimating a newly added vehicle state. In the formula 44, the parameter a _lon shown indicates the current longitudinal acceleration and is assumed to be constant. The details of the vehicle model are expressed by Equations 12-22.

Obstacle model D _B is described by the differential equation shown in the following equation 46.

If it is possible to detect the movement of the obstacle in more detail, can be more complex obstacles model D _B shown in Equation 46 and 47, the vehicle model D _V Similarly, in order to suppress the calculation load of the microprocessor 9 In the present embodiment, the simplest model as shown in the following formulas 48 and 49 is used. Thereby, the process of step S31 is completed and the process of step S32 is started.

  In the process of step S32, the microprocessor 9 sets an evaluation function used for calculating the avoidance operation amount. Since the specific contents have been described in the processing of S3, they are omitted. Thereby, the process of step S32 is completed and the process of step S33 is started.

In the process of step S33, the microprocessor 9 minimizes the evaluation function expressed by the mathematical formula 42 based on the vehicle model D _V , the obstacle model D _B , and the evaluation function set by the processes of steps S31 and S32. Perform optimization calculations. The problem of obtaining the manipulated variable that minimizes the evaluation function represented by Expression 42 is generally called an optimal control problem, and various algorithms are known for obtaining the numerical solution. Examples of known techniques include literature (T. Ohtsuka, “A continuation / GMRES method for fast computation of nonlinear receeding horizon control”, Automatica, vol, 40, 563/574, 2004.). When the optimum operation amount is calculated using such an algorithm, in the case of the present embodiment, the parameter a _lon is constant for the input u _v , so that the steering angle δ _S is subject to optimization according to Equation 44, and As shown in Equation 50, data of time series change of the steering angle δ _SR ^* from time t ₀ to time t ₀ + T _f is calculated. Then, the microprocessor 9 calculates time series change data of the steering angle δ _SR ^* from the avoidance start time to the end time as an avoidance operation amount, and outputs the operation amount to the EPS controller 8 as a command value according to the time. . Thereby, the process of step S33 is completed and a series of driving operation support processes are completed.

Finally, referring to FIG. 10, the present invention is compared with the prior art. In FIG. 10, a dotted line shows the optimization calculation result of a prior art, and a continuous line shows the optimization calculation result of this invention. As indicated by a broken line in FIG. 10, in the conventional technique, a step-like signal is commanded from zero at time t ₀ , so the avoidance performance is high, but the driver feels uncomfortable. On the other hand, in the present invention, since the weight of the steering evaluation at time t ₀ is large, as indicated by the solid line, an unnecessarily large command is not given in the initial movement, but the weight increases with time. By decreasing, a large steering command as in the prior art becomes gradually possible. For this reason, the sense of incongruity given to the driver can be suppressed while sacrificing avoidance performance within the avoidable range.

As is apparent from the above description, according to the driving operation support process according to the first embodiment of the present invention, the microprocessor 9 is connected to the stereo cameras 2a and 2b, the acceleration sensor 3, the yaw rate sensor 4, and the wheel speed sensor 5a. , 5b, 5c, 5d, and the information input from the steering angle sensor 6, the evaluation weight w _f regarding the time series data δ _SR ^* of the steering operation input is relatively changed. By calculating an appropriate amount of steering operation according to the vehicle state, it is possible to reduce a sense of discomfort felt by the driver for avoidance control.

Further, according to the driving operation support process according to the first embodiment of the present invention, the microprocessor 9 relatively changes the evaluation weight w _f regarding the time series data δ _SR ^* of the steering operation input for each prediction time. A more realistic steering operation amount can be calculated by appropriately setting the evaluation weight w _f regarding the time series data δ _SR ^* of the steering operation input according to the predicted time.

Further, according to the driving operation support process according to the first embodiment of the present invention, as the risk of contact with the obstacle is lower, the microprocessor 9 is more likely to receive a steering operation input as shown in FIG. Since the evaluation weight w _f regarding the series data δ _SR ^* is relatively increased, it is possible to perform the avoidance operation support while suppressing an unnecessarily large control intervention.

Further, according to the driving operation support processing according to the first embodiment of the present invention, as the risk of contact with the obstacle is lower, the microprocessor 9 has an initial evaluation weight w as shown in FIG. _Since the weight of _f is relatively increased, it is possible to suppress suddenly large avoidance control from intervening and to suppress the driver from feeling uncomfortable with the avoidance control.

  Further, according to the driving operation support process according to the first embodiment of the present invention, the microprocessor 9 sets an evaluation function including an evaluation item related to the degree of approach of the host vehicle with respect to the movement locus of the obstacle. The avoidance control can be performed in consideration of the movement of.

[Second Embodiment]
Next, the configuration of the driving operation support apparatus according to the second embodiment of the present invention will be described.

[Configuration of driving support device]
The configuration of the driving operation support apparatus according to the second embodiment of the present invention is the same as that of the first embodiment except that the avoidance path evaluation means 19 is included in the contact risk determination means 18 as shown in FIG. It has the same configuration as the driving operation support device.

[Driving operation support processing]
In the driving operation support apparatus having such a configuration, the microprocessor 9 executes the driving operation support process described below, thereby reducing the uncomfortable feeling that the driver feels with respect to the avoidance control. Hereinafter, the operation of the microprocessor 9 when executing the driving operation support process will be described. In the following, the operation of the microprocessor 9 when executing the driving operation support process will be described by taking the traveling scene shown in FIG. 4 as an example. Further, since the driving operation support process in this embodiment is different from the driving operation support process in the first embodiment only in the content of the weight setting process, the evaluation weight will be described below with reference to the flowchart shown in FIG. Only the setting process will be described.

[Evaluation weight setting process]
In the process of step S41, the microprocessor 9 determines whether or not the avoidance operation amount calculation is performed at the time one sample time before from the information stored in the buffer memory. As a result of the determination, if the avoidance operation amount calculation is not performed, the microprocessor 9 starts the process of step S42. On the other hand, when the avoidance operation amount calculation is performed, the microprocessor 9 starts the process of step S48. Note that the processing in steps S42 to S47 is the same as the processing in steps S21 to S26 shown in FIG.

In the processing of step S48, the microprocessor 9 calculates an avoidance path using the avoidance operation amount at the time one sample time before. Specifically, the microprocessor 9 is advanced by one sample time period in order to adjust the steering pattern calculated by the optimization calculation at time t ₀ of one sample before time t ₀ which is represented by the formula 50 'to the current time . Assuming that one sample time is T _s, and an element that has become insufficient by one sample time T _{s is set} to 0, the steering pattern is expressed as Equation 51 below.

The microprocessor 9, the vehicle state vector D _v at the current time as shown in equation 23 obtained by various sensors, and calculates the avoidance path with the vehicle model shown in Equation 12-25. The route calculation results at this time are shown in the following formulas 52 and 53.

Similarly microprocessor 9 obtains the state vector D _B of the obstacle at the current time as shown in Equation 49 to calculate the obstacle path. The route calculation results at this time are shown in the following formulas 54 and 55. Thereby, the process of step S48 is completed, and the microprocessor 9 starts the process of step S49.

In the process of step S49, the microprocessor 9 calculates the evaluation formulas shown in the formulas 33 and 34 used in the evaluation function using the formulas 52 to 55, and uses the calculation results to determine the contact risk with the obstacle O. Correction is performed so as to lower the evaluation weight until the time when it is determined that the value is high. Hereinafter, this process will be described in detail with reference to FIG. Now, the calculation result of Expression 33 is expressed as evaluation expression L ₂ ′, and the calculation result of Expression 34 is expressed as evaluation expression L ₃ ′, as shown in Expressions 56 and 57 below.

First, as shown in FIGS. 13A and 13B, the microprocessor 9 _presets the threshold values of the evaluation expressions L ₂ ′ and L ₃ ′ representing the degree of urgency as L _2TH ′ and L _3TH ′. As shown in Equations 58 and 59, these are compared with each element of the evaluation equations L ₂ ′ and L ₃ ′.

In Expressions 58 and 59, the value of each element is −1 or 1, and during the time when it is 1, the time when there is a high possibility of contact with the obstacle O or the departure from the road. Next, as shown in FIGS. 13C and 13D, the microcomputer 9 determines the time when the element value is the latest as 1 for the evaluation expressions L ₂ ″ and L ₃ ″ as time t ₀ + T _L2 , set to t ₀ + T _L3, and compares the time t ₀ + T _L2 and time t ₀ + T _L3, sets a larger time to time t ₀ + T _COM. Here, when all the elements are −1 in the evaluation formula L ₂ ″, the time T _L2 = 0. Similarly, with respect to the evaluation formula L ₃ ″ as well, when all the elements are −1, the time T _{L3 is set.} = 0. Thereby, the process of step S49 is completed, and the microprocessor 9 starts the process of step S50.

In the process of step S50, the microprocessor 9 corrects the evaluation weight w _f related to the time series data δ _SR ^* of the steering operation input calculated one sample time ago based on the evaluation result of step S49. Here, with reference to FIG. 14, a method of determining the correction coefficient will be described. Correction coefficient K _C as shown in FIG. 14 (b), the period from time t ₀ to the final time t ₀ + T _COM that risk of contact or road departure is considered high and obstacle from 0 to 1 assuming a value Q ', and later gradually value increases, the final time t ₀ + T correction coefficient when the _COM from a predetermined time [Delta] T the aforementioned time last time _{t 0 + T COM + ΔT K} C = 1 , and the time t It is set to keep 1 until ₀ + _Tf . Here, the magnitude of Q ′ is determined by the maximum values of the evaluation formula L ₂ ″ and the evaluation formula L ₃ ″ calculated by the processing in step S49, as shown in the following formula 60.

Since the function represented by the equation 60 can be considered to have a higher avoidance margin as the maximum value of the evaluation formula L ₂ ′ and the evaluation formula L ₃ ′ is smaller, the value is closer to 1, and conversely, the larger the larger the avoidance margin is, the lower the avoidance margin is. The value is close to zero. From the above, the correction coefficient K _C is calculated by the following formula 61.

And the evaluation weights w _f, which is calculated from the time t ₀ in one sample time before time t ₀ ', proceed with one sample time to match the current time, the made elements not enough _{w f (t 0 + T f} (( Assuming that N-1) / N)), the following formula 62 is obtained. Then, the value obtained by multiplying the evaluation weight w _f ′ of Expression 62 by the correction coefficient K _C of Expression 61 is newly set as the evaluation weight w _f regarding the time series data δ _SR ^* of the steering operation input at the current time. Thereby, the process of step S50 is completed, and the driving support process proceeds to the process of step S4.

Now, in FIG. 15 (a), the vehicle trajectory at the current time calculated based on the optimization calculation result one sample time ago can be avoided, but it is better to avoid it slightly away from the obstacle. Suppose that it is judged. Such correction of the evaluation weights w _f as shown in FIG. 15 (b) is performed when, than in the previous sample becomes small evaluation weight w _f of the steering operation amount, greater than in the previous sample Avoidance can be achieved by steering. In addition, it is possible to compensate for the difference between the own vehicle model and the actual movement and the sudden movement change of the obstacle by this sequential update.

  As is clear from the above description, according to the driving support device according to the second embodiment of the present invention, the microprocessor 9 repeats the calculation every predetermined time and updates the driving support amount. Even if the movement of O changes, appropriate avoidance support can be performed. Moreover, according to the driving operation support apparatus according to the second embodiment of the present invention, the microprocessor 9 performs the time series driving operation amount calculated at the time before the current time at the current time. Evaluate the travel route, and based on the evaluation result, the weight of the evaluation item related to the amount of driving operation up to the time when the risk of contact with obstacles and road boundaries is high is relatively small. It is possible to appropriately cope with the case where the person who avoids can prevent contact with an obstacle or departure from the road.

[Third Embodiment]
Next, the configuration of the driving operation support apparatus according to the third embodiment of the present invention will be described.

[Configuration of driving support device]
As shown in FIG. 16, the driving operation support device according to the third embodiment of the present invention includes, in addition to the configuration of the driving operation support device according to the first embodiment, brakes 21 a and 21 b for front and rear wheels of the vehicle 1. , 21c, 21d, and a DYC (Direct Yaw-moment Control) controller 22 for controlling the brakes 21a, 21b, 21c, 21d in order to control the movement of the vehicle 1 in the yaw direction. The DYC controller 22 functions as the vehicle control means 16 according to the present invention shown in FIG. In addition, this invention is not limited to DYC by each wheel independent braking, For example, DYC by each wheel braking motor may be sufficient.

[Driving operation support processing]
In the driving operation support apparatus having such a configuration, the microprocessor 9 executes the driving operation support process described below, thereby reducing the uncomfortable feeling that the driver feels with respect to the avoidance control. Hereinafter, the operation of the microprocessor 9 when executing the driving operation support process will be described. In the following, driving operation support processing will be described by taking a driving scene in which the front of the road on which the vehicle 1 travels as shown in FIG. 17 draws a curve and the obstacle O stops at the position approaching the curved portion as an example. The operation of the microprocessor 9 during execution will be described. Since the flow of the driving operation support process is the same as the flowchart shown in FIG. 3, the flow of the driving operation support process in the present embodiment will be described below with reference to the flowchart shown in FIG.

  In the process of step S1, the microprocessor 9 reads the detection values of various sensors and determines the coordinate system. In this embodiment, as shown in FIG. 17, in addition to the XY coordinates used in the first and second embodiments, the S coordinate axis and the S coordinate axis set along the traveling direction on the center line of the road as the coordinate system. SR coordinate system with R coordinate axes orthogonal to is set. Since the road direction can be obtained from the captured images of the stereo cameras 2a and 2b, the coordinate conversion formula between the XY coordinates and the SR coordinates can be expressed as the following formulas 63 and 64. Thereby, the process of step S1 is completed and a driving operation assistance process progresses to the process of step S2.

  In the process of step S <b> 2, the microprocessor 9 determines whether there is an obstacle O that may come in contact with the front of the vehicle 1. Here, when the contact determination by the vehicle model is performed, a moment is generated by the wheel brakes 21a, 21b, 21c, and 21d. Therefore, it is necessary to change the vehicle model as shown in the following Expressions 65 to 71.

Parameter M _Y represents a yaw moment generated by multiplying one only stronger braking than normal braking of left and right wheels. For yaw moment M _Y, the forward direction indicated by the arrow shown in FIG. 18, the yaw moment M _Y is when positive increases the braking force of the left wheel is, the braking force of the right wheel when the yaw moment M _Y is negative increases It turns out that. From the above, the vehicle model used in the present embodiment is expressed as the following mathematical formulas 72 to 74. Thereby, the process of step S2 is completed and the driving operation support process proceeds to the process of step S3.

In the process of step S3, the microprocessor 9 determines an evaluation weight for each operation amount. Here, the operation input of the vehicle model assumed in this embodiment the steering angle [delta] _S according to Equation 71, the longitudinal acceleration a _lon, and the three yaw moment M _Y, subject to the operation input of the optimization in this Are the steering angle δ _S and the yaw moment M _Y. Hereinafter, the process of step S3 will be described in detail with reference to the flowchart shown in FIG.

In the process of step S51, the microprocessor 9 determines whether or not the avoidance operation amount calculation is performed at the time one sample time before from the information stored in the buffer memory. As a result of the determination, if the avoidance operation amount calculation is not performed, the microprocessor 9 starts the process of step S52. On the other hand, when the avoidance operation amount calculation is performed, the microprocessor 9 starts the process of step S58. Note that the processing in steps S52 to S57 is basically the same as the processing in steps S21 to S26 shown in FIG. That is, the evaluation weight w _f regarding the steering angle δ _SR ^* can be calculated by the same process as the process of steps S21 to S26 shown in FIG. Further, the evaluation weight w _M related to the yaw moment M _Y is set as a fixed value as shown in the following formula 75 set in advance at the time of steps S24 and S26.

In the process of step S58, the microprocessor 9 calculates the avoidance path using the time series data of the avoidance operation amount at the time one sample time before. More specifically, first, the microprocessor 9 is advanced by one sample time period the steering pattern calculated by the optimization calculation in one sample time before time t ₀ 'seconds from time t ₀ in order to match the current time. Assuming that one sample time is T _s, and an element that is not enough for one sample time T _{s is} advanced to zero, the steering pattern is expressed by the following formulas 76 and 77. In Equations 76 and 77, the parameter δ _SR ′ represents the avoidance steering pattern obtained by the optimization calculation, and the parameter M _Y ′ represents the yaw moment pattern obtained by the optimization calculation.

Then, the microprocessor 9, the vehicle state vector D _V at the current time given by Equation 72 obtained by various sensors, and calculates the avoidance path with the vehicle model as shown in Equation 65-71. The route calculation result at this time is expressed as (X _v , Y _v ) in the same form as Expressions 52 and 53, and the vehicle attitude is expressed as Expression 78 below. Thereby, the process of step S58 is completed, and the microprocessor 9 starts the process of step S59.

In the process of step S59, the microprocessor 9 uses Formulas 52 to 55 to calculate the evaluation formulas shown in Formulas 33 and 34 used in the evaluation function. The correction coefficient calculated here is set as K _C1 . The correction coefficient K _C1 can be represented by a shape as shown in FIG. 20B, as in the second embodiment. Thereby, the process of step S59 is completed, and the microprocessor 9 starts the process of step S60.

In the process of step S60, the microprocessor 9 uses the terminal vehicle posture represented by Expression 78 as shown in FIG. 22, and the target vehicle posture θ used in the terminal evaluation item 1 of the evaluation function at the previous sample time t ₀ ′ as shown in FIG. Compare with '(t ₀ '). The microprocessor 9 can determine that the scene where the return posture after avoidance is more difficult as the value of Δθ (see FIG. 21) shown in the following mathematical formula 79 is large. And the predicted time of passing the obstacle O from the obstacle prediction trajectory. As a result, the weight correction coefficient K _C2 based on the posture is expressed in such a manner that it is 1 at the initial stage of evaluation but decreases to Q ″ when the time t ₀ + T _REC is passed, as shown in FIG. The magnitude of Q ″ takes a value from 0 to 1, and is set to a value closer to zero as Δθ shown in Formula 79 is larger. Thereby, the process of step S60 is completed, and the microprocessor 9 starts the process of step S61.

In the process of step S61, the microprocessor 9 corrects the evaluation weight based on the correction coefficients K _C1 and K _C2 calculated by the processes of steps S59 and S60. Since the command value at the current time may be greatly changed by correcting the evaluation weight, the yaw moment is reduced in this embodiment in order to reduce the uncomfortable feeling given to the driver. only to correct evaluation weights w _M of. Specifically, as shown in the following Expression 80, the microprocessor 9 multiplies the correction coefficients K _C1 and K _C2 calculated by the process of Step S69 to evaluate the yaw moment evaluation weight w _M at the current time t ₀ . Is calculated. Further, the microprocessor 9 is obtained by advancing the evaluation weight element calculated at the time t ₀ ′ one sample earlier by one sample as the evaluation weight w _f of the steering operation at the current time t ₀ as shown in Formula 81. Use. Thereby, the process of step S61 is completed, and the microprocessor 9 starts the process of step S4.

  In the process of step S4, the microprocessor 9 sets an evaluation function using the evaluation weight set by the process of step S3, and performs optimization calculation. Since the process flow of step S4 is the same as the process flow of step S4 in the first embodiment, the process of step S4 will be described below with reference to the flowchart shown in FIG.

 In the process of step S31, the microprocessor 9 sets a vehicle model and an obstacle model necessary for performing the optimization calculation. Since this process is the same as the process of step S31 in the first embodiment, detailed description thereof is omitted. Thereby, the process of step S31 is completed and the process of step S32 is started.

In the process of step S32, the microprocessor 9 sets an evaluation function used for calculating the avoidance operation amount. Similar to the evaluation function in the first embodiment, the evaluation function is classified into a terminal evaluation item and a section evaluation item as in Expression 31, and in this embodiment, the evaluation function is as follows. Evaluation items (1), (2), and (4) are the same as evaluation items (1), (2), and (4) in the first embodiment. The evaluation item (3) is expressed by the following formula 82 because the distance from the center of the road is expressed by the R coordinate instead of the Y coordinate in the road curve portion. Further, the section evaluation item (5) to be newly evaluated is expressed by the following mathematical formula 83.
(1) Terminal evaluation item 1: After the time _Tf , the vehicle posture is directed toward the road. (2) Section evaluation item 2: Keep away from the front obstacle during the time _Tf .
(3) Section evaluation item 3: Preventing the vehicle from deviating from the road boundary during the time _Tf (4) Section evaluation item 4: During the time _Tf , the steering control input of the vehicle is made as small as possible (5) Section evaluation item 5: The yaw moment input of the vehicle is made as small as possible during the time _Tf.

Here, in Equation 82, the parameter r _L represents the R-axis distance of the left road boundary from the road center, the parameter r _R represents an R-axis distance of the right road boundary from the road center. In addition, Formula 63 is used to correspond the evaluation formula shown in Formula 34 expressed in SR coordinates to XY coordinates. From the above, the first and second terms of Expression 42 are expressed as Expressions 84 and 85 below. Thereby, the process of step S32 is completed and the process of step S33 is started.

In the process of step S33, the microprocessor 9 performs an optimization calculation based on the vehicle model, the obstacle model, and the evaluation function set by the processes of steps S31 and S32. Also in this embodiment, an optimization algorithm similar to that used in the process of step S33 in the first embodiment can be used. When the optimum operation amount is calculated using such an algorithm, in the present embodiment, the vehicle longitudinal acceleration a _lon is constant for the input u _v , so the steering angle δ _s and the yaw moment _MY are optimized. subject, as shown in the following formula 86, 87, each operation amount from time t ₀ to time t ₀ + T _f is calculated in a time series.

The microprocessor 9 calculates the avoidance operation amount from the avoidance start time to the end time, and outputs the operation amount to the EPS controller 8 and the DYC controller 22 as a command value according to the time. In the present embodiment, it is not conducted corrected by sequential update with respect to the steering angle [delta] _s, since the correction by sequentially updated relative yaw moment M _Y is performed, given to the driver by the change in steering command while suppressing a sense of discomfort, it is possible to compensate for the avoidance performance by the yaw moment M _Y. Thereby, the process of step S33 is completed and a series of driving operation support processes are completed.

As apparent from the above description, according to the third embodiment become driving operation support of the present invention, the microprocessor 9, the higher the risk of departing from the road boundary, evaluation weights of yaw moment M _Y Since w _M is relatively increased, the vehicle posture can be returned while suppressing deviation from the road.

Further, according to the driving operation support processing according to the third embodiment of the present invention, the microprocessor 9 determines whether it is difficult to return the vehicle posture after avoidance based on the evaluation result of the travel route of the host vehicle 1. discriminated, when the attitude restoration of the vehicle 1 is difficult, because the relatively small evaluation weight w _M yaw moment M _Y, in the case with large posture change after avoiding the contact with the obstacle In addition, the operation can be smoothly shifted from the avoidance operation.

[Fourth Embodiment]
Finally, a driving operation support device according to a fourth embodiment of the present invention will be described.

[Configuration of driving support device]
The configuration of the driving operation support device according to the fourth embodiment of the present invention is the same as that of the driving operation support device according to the second embodiment.

[Driving operation support processing]
In the driving operation support apparatus having such a configuration, the microprocessor 9 executes the driving operation support process described below, thereby reducing the uncomfortable feeling that the driver feels with respect to the avoidance control. Hereinafter, the operation of the microprocessor 9 when executing the driving operation support process will be described. In the following, the operation of the microprocessor 9 when executing the driving operation support process will be described by taking the traveling scene shown in FIG. 4 as an example. Further, the driving operation support process in the present embodiment is different from the driving operation support process in the first embodiment only in the content of the evaluation weight setting process. Therefore, the evaluation will be described below with reference to the flowchart shown in FIG. Only the weight setting process will be described.

[Evaluation weight setting process]
In the present embodiment, the microprocessor 9 sets the evaluation weight in the same manner as the processing in step S3 in the first embodiment, but the process flow changes with the sequential updating of the evaluation weight. Specifically, evaluation start time of the current time t _0, from evaluation starting time t ₀ time T _f seconds destination future time t ₀ + T _f the evaluation ending time, by discretizing the interval with a suitable number of divisions N, N evaluation weights corresponding to the division number N are calculated. In the first to third embodiments, the evaluation weight for the operation amount is determined, whereas in the present embodiment, the obstacle weight and the road boundary are determined. Determine the evaluation weight. Here, the process of actually setting the evaluation function is the process of step S4, but the evaluation function will be described first. Evaluation function, be expressed on the basis of the predicted value of the vehicle state vector D _V from the current time t ₀ to a predetermined estimation time t ₀ + T _f for the input vector u _V was applied to the vehicle 1 in a similar manner as equation 31 Can do. Evaluation items used in the present embodiment are as follows. The difference from the first embodiment is that the evaluation is divided between the right road boundary and the left road boundary.

(1) Terminal evaluation item 1: After T _f seconds, the vehicle posture is directed toward the road.

(2) Section evaluation item 2: Keep away from obstacles ahead for T _f seconds

数式中、パラメータσ_X，σ_Yは関数の形状を表すパラメータである。

In the formula, parameters σ _X and σ _Y are parameters representing the shape of the function.

(3) Section evaluation item 3-1: Prevent vehicle 1 from deviating from the right road boundary for T _f seconds.

数式中、パラメータΔは道路境界に接近する余裕幅を示す。

In the formula, the parameter Δ indicates a margin width approaching the road boundary.

(4) Section evaluation item 3-2: The vehicle 1 is prevented from deviating from the left road boundary for T _f seconds.

数式中、パラメータΔは道路境界に接近する余裕幅を示す。

In the formula, the parameter Δ indicates a margin width approaching the road boundary.

(5) Section evaluation item 4: The steering control input of the vehicle 1 is made as small as possible for T _f seconds.

The evaluation items are weighted using parameters w ₁ , w _b (τ), w _rL (τ), w _rR (τ), and w ₄ , respectively. From the above, the first term and the second term of the evaluation function represented by Expression 31 are represented as Expressions 93 and 94 shown below.

Hereinafter, a method for setting the evaluation weights w _b (τ), w _rL (τ), and w _rR (τ) will be described with reference to the flowchart shown in FIG.

  In the process of step S71, the microprocessor 9 refers to the information stored in the buffer memory and determines whether or not the avoidance operation amount calculation is performed at the time one sample before. As a result of the determination, if the avoidance operation amount calculation is not performed, the microprocessor 9 advances the weight setting process to the process of step S72. On the other hand, when the avoidance operation amount calculation is performed, the microprocessor 9 advances the weight setting process to the process of step S75.

In the process of step S72, the microprocessor 9 determines the evaluation weight w _b (τ) for the obstacle O. In the present embodiment, the microprocessor 9 determines the evaluation weight w _b for the obstacle O as shown in FIG. 23 using the parameter _TTC indicating the urgency level of the avoidance scene calculated by the process of step S2. In FIG. 23, the parameter w _bHIGH is a value determined by the parameter T _TC , and increases as the parameter T _TC decreases. The parameter w _bLOW indicates a preset fixed value, and the parameter ΔT ′ indicates the time from the time t ₀ + T _TC until the host vehicle 1 passes the obstacle area based on the size of the obstacle area and the own vehicle speed. Determined as a guide. Therefore, the evaluation weight w _b (τ) for the obstacle is expressed as the following Expression 95. Thereby, the process of step S72 is completed, and the weight setting process proceeds to the process of step S73.

In the processing of step S73, the microprocessor 9 calculates the evaluation weight w _rR (τ) for the right road boundary. Hereinafter, with reference to the flowchart shown in FIG. 24, the flow of processing when calculating the evaluation weight w _rR (τ) for the right road boundary will be described.

In the process of step S81, the microprocessor 9 uses the following expression 96 to calculate the right vehicle for avoiding contact with an obstacle at the time t ₀ + T _TC calculated by the process of step S2. A lateral movement amount L _{VB # R of 1} is calculated. Thereby, the process of step S81 is completed, and the calculation process proceeds to the process of step S82.

In the process of step S82, the microprocessor 9 acquires the initial state B _v (t ₀ ) of the vehicle model by the same process as in the first embodiment. Thereby, the process of step S82 is completed and the calculation process proceeds to the process of step S83.

In the process of step S83, the microprocessor 9 sets a command steering angle [delta] _ref, to predict the trajectory of the vehicle 1 when the predetermined input to the vehicle model. Specifically, when the steering wheel is turned to the right by the angle Δδ _S from the current steering angle δ _S (t ₀ ), the command steering angle δ _ref is expressed as Equation 97 below.

Therefore, the microprocessor 9 calculates the motion state of the host vehicle 1 using Formulas 12 to 25 when the command steering angle δ _ref shown in Formula 97 is input as a constant and the acceleration a _lon is input as is at the current time. Thereby, the process of step S83 is completed, and the calculation process proceeds to the process of step S84.

In the process of step S84, the microprocessor 9 refers to the calculated motion model of the host vehicle 1 and uses the following formula 98 to calculate the lateral movement amount of the host vehicle 1 at the predicted contact time _TTC . It is determined whether or not it is larger than the movement amount L _{VB # R.} As a result of the determination, when the lateral movement amount of the host vehicle 1 is larger than the target lateral movement amount L _{VB # R} , the microprocessor 9 advances the calculation process to the process of step S86. On the other hand, when the lateral movement amount of the host vehicle 1 is smaller than the target lateral movement amount _{LVB # R} , the microprocessor 9 advances the calculation process to the process of step S85.

In the process of step S85, the microprocessor 9, using Equation 99 shown below, to reset the steering angle cut to the right command steering angle [delta] _ref from the handle further angle .DELTA..delta _S to the command steering angle [delta] _ref, The motion model of the host vehicle 1 when the command rudder angle δ _ref is constant input and the acceleration a _lon is constant input at the current time is calculated again using Equations 12-25. Thereby, the process of step S85 is completed, and the driving operation support process returns to the process of step S84.

In the process of step S86, the microprocessor 9 uses the command steering angle δ _ref and the vehicle model in which the own vehicle lateral movement amount is larger than the target lateral movement amount L _{VB # R} , and the own vehicle 1 has the command steering angle δ _ref . The time to deviate from the right road boundary when performing constant steering is calculated. Specifically, the microprocessor 9 calculates the minimum time T _{XR in} which the following formula 100 is satisfied as the off-road departure time. Thereby, the process of step S86 is completed, and the calculation process proceeds to the process of step S87.

In the process of step S87, the microprocessor 9 determines the evaluation weight w _rR for the right road boundary as shown in FIG. The maximum value w _rHigh of the evaluation weight w _rR for the right road boundary in FIG. 26 is calculated based on the off-road departure time T _XR calculated by the process of step S86. The fact that the out-of-road departure time T _XR is small means that the time for returning the vehicle attitude after avoiding an obstacle is short, so the maximum value _wrHigh is set to a large value. The minimum value w _rLow rating weights w _rR for the right road boundary, like the minimum value of the evaluation weights w _b w _Blow against obstacles of O, a preset fixed value. Therefore, the time series data _wrR of the evaluation weight of the right road boundary is expressed as the following formula 101. Thereby, the process of step S87 is completed and a series of calculation processes are completed.

In the process of step S74, the microprocessor 9 calculates the evaluation weight w _rL for the left road boundary. Specifically, the amount of lateral movement L _{VB in} the left direction for the microprocessor 9 to avoid contact with the obstacle at time t ₀ + T _TC calculated by the processing of step S2 by the following formula 102 _#L is calculated.

Then the microprocessor 9, the same processing as in the first embodiment acquires the vehicle model initial state B _v (t _0). Next, the microprocessor 9 sets the command steering angle δ _ref and predicts the trajectory of the vehicle 1 when a constant input is made to the vehicle model. Specifically, when the steering wheel is turned to the left by the angle Δδ _S from the current steering angle δ _S (t ₀ ), the command steering angle δ _ref is expressed as the following Expression 103.

Therefore, the microprocessor 9 calculates the own vehicle motion when the command steering angle δ _ref shown in Formula 103 is input as a constant and the acceleration a _lon is input as is at the current time using Formulas 12-25. Next, the microprocessor 9 determines whether or not the lateral movement amount of the host vehicle 1 at the estimated contact time _TTC is larger than the target lateral movement amount L _{VB # R} using the following formula 104.

As a result of the determination, when the lateral movement amount of the host vehicle 1 is smaller than the target lateral movement amount L _{VB # R} , the microprocessor 9 uses the following formula 105 to move the steering wheel to the left by the angle Δδ _S further than the command steering angle δ _ref. The motion angle model of the host vehicle 1 when the steering angle δ _ref is reset to the command steering angle δ _ref , the command steering angle δ _ref is constant input, and the acceleration a _lon is constant input at the current time is expressed by Equations 12-25. And calculate again. Then, the above-described determination process is performed again.

On the other hand, when the lateral movement amount of the own vehicle 1 is larger than the target lateral movement amount L _{VB # R} , the microprocessor 9 instructs the command steering angle δ at which the lateral movement amount of the own vehicle 1 is larger than the target lateral movement amount L _{VB # L. Using ref} and the vehicle model, the time at which the vehicle 1 deviates from the left road boundary when the vehicle 1 performs constant steering at the command steering angle δ _ref is calculated. Specifically, the microprocessor 9 calculates the minimum time T _XL when the following formula 106 is satisfied as the road departure time T _XL .

Next, the microprocessor 9 calculates an evaluation weight w _rL for the left road boundary as shown in the following Expression 107 by the same method as the evaluation weight w _rR for the right road boundary. Thereby, the process of step S74 is completed and a series of weight setting processes are completed.

  In the process of step S75, the microprocessor 9 calculates the avoidance path using the time series data of the avoidance operation amount at the time one sample time before. Specifically, the microprocessor 9 calculates an avoidance path by the same process as the process of step S48 in the second embodiment. Therefore, the own vehicle trajectory and the obstacle trajectory can be expressed as Equations 52 to 55. Thereby, the process of step S75 is completed, and the weight setting process proceeds to the process of step S76.

  In the process of step S76, the microprocessor 9 performs the individual evaluation of each evaluation item by calculating the mathematical expressions 89 to 91 used in the evaluation function using the mathematical expressions 52 to 55, and updates each evaluation weight. Hereinafter, the evaluation method for each evaluation item will be described with reference to the flowchart shown in FIG.

  In the process of step S91, the microprocessor 9 determines whether or not the host vehicle 1 is trying to avoid the obstacle O to the right from the calculation result one sample time ago. As a result of the determination, if the host vehicle 1 is trying to avoid the obstacle O to the right, the microprocessor 9 advances the evaluation process to the process of step S92. On the other hand, when the vehicle 1 tries to avoid the obstacle to the left, the microprocessor advances the evaluation process to the process of step S94.

In the process of step S92, the microprocessor 9 evaluates the risk of contact with an obstacle and the possibility of deviating from the right road boundary based on the formulas 89 and 90 as shown in the following formulas 108 and 109. Specifically, the microprocessor 9 sets the threshold value of the evaluation formula L ₂ ′ as L _2TH and the threshold value of the evaluation formula L ₃ ′ as L _3TH in advance, and sets the threshold value L as shown in the following formulas 110 and 111: _2TH and L _3TH are compared with each element of the evaluation expressions L ₂ ′ and L ₃ ′.

The value of each element of the mathematical formulas 110 and 111 is −1 or 1, and during the time when it is 1, it is a time when there is a high possibility of contact with an obstacle or deviation from the road. Then, the microprocessor 9 sets time t ₀ + T _L2 and t ₀ + T _{L3 as} the times when the element value becomes 1 at the most future time in the evaluation expressions L ₂ ″ and L ₃ ″, respectively. In the evaluation formula L ₂ ″, when all the elements are −1, the time T _L2 = 0. Similarly, in the evaluation formula L ₂ ″, when all the elements are −1, the time T _{L3 is set.} = 0. Thereby, the process of step S92 is completed, and the evaluation process proceeds to the process of step S93.

In the process of step S93, the microprocessor 9 updates each evaluation weight based on the evaluation process result of step S92. Specifically, the evaluation weights w _b with respect to the obstacle, as shown in FIG. 27 (c), is constant from time t ₀ to time t ₀ + T _L2, from the time t ₀ + T _L2 at time t ₀ + T _L2 + [Delta] T The interval decreases monotonically until _{reaching the} minimum value w _bLOW , and thereafter becomes constant at the minimum value w _bLOW . The minimum value w _bLOW is a preset fixed value, and the maximum value w _bHIGH is set to a larger value as the parameter L _2M ′ shown in the following Expression 112 is larger.

On the other hand, evaluation weights w _rR for the right road boundary, as shown in FIG. 27 (d), a constant at the minimum value w _Rlow from time t ₀ to time t ₀ + T _L3, the time t ₀ + T _L3 from the time t ₀ + T _{During L3} + ΔT, it increases monotonically until _{reaching the} maximum value w _rHIGH , and thereafter becomes constant at the maximum value w _rHIGH . The minimum value w _rLOW is a preset fixed value, and the maximum value w _rHIGH is set to a larger value as the evaluation formula L _3M ′ shown in the following Expression 113 is larger.

According to such processing, the time series data of the evaluation weight w _b for the obstacle and the evaluation weight w _rR for the right road boundary are determined. At this time, the evaluation weight w _rL for the left road boundary is fixed to the minimum value w _rLOW . Value. Thereby, the process of step S93 is completed, and a series of evaluation / update processes ends.

The processing of step S94 and step S95 evaluates the risk of contact with an obstacle and the possibility of deviating from the left road boundary based on mathematical expressions 89 and 91, and the evaluation weight w _b for the obstacle based on the evaluation result. _This is a process for updating the evaluation weight _wrL for the left road boundary, and can be performed in the same manner as the processes in steps S92 and S93, and therefore will not be described below.

As is clear from the above description, according to the driving operation support process according to the fourth embodiment of the present invention, the microprocessor 9 increases the evaluation weight w _b for the obstacle as the risk of contact with the obstacle increases. Therefore, the avoidance operation support according to the margin that can avoid contact with the obstacle can be performed. Further, according to the driving operation support process according to the fourth embodiment of the present invention, the microprocessor 9 sets the evaluation weight w _b for the obstacle while approaching the obstacle as the risk of contact with the obstacle increases. Since it is relatively large, it is possible to perform avoidance support with particular attention to the obstacle when it is necessary to avoid the obstacle.

Further, according to the driving operation support process according to the fourth embodiment of the present invention, the microprocessor 9 increases the evaluation weight _wrR for the right road boundary or the evaluation weight for the left road boundary as the risk of deviating from the road boundary increases. Since w _rL is relatively large, it is possible to perform avoidance support according to the possibility of deviating from the road boundary.

  Further, according to the driving operation support processing according to the fourth embodiment of the present invention, the microprocessor 9 determines the contact risk between the obstacle and the road boundary based on the evaluation result of the travel route of the host vehicle 1. The weights of the evaluation items related to the object having a high contact risk are relatively increased, so that weighting suitable for the front environment can be performed.

  As mentioned above, although embodiment which applied the invention made by the present inventors was described, this invention is not limited by the description and drawing which make a part of indication of this invention by this embodiment. That is, it should be added that other embodiments, examples, operation techniques, and the like made by those skilled in the art based on the above embodiments are all included in the scope of the present invention.

1: Driving operation support devices 2a, 2b: Stereo camera 3: Acceleration sensor 4: Yaw rate sensors 5a, 5b, 5c, 5d: Wheel speed sensor 6: Steering angle sensor 7: EPS motor 8: EPS controller 9: Microprocessor 11: Obstacle detection means 12: road boundary detection means 13: external environment detection means 14: own vehicle state detection means 15: evaluation function setting means 16: optimum operation amount calculation means 17: own vehicle control means 18: contact risk determination means

Claims (14)

An external environment detection means for detecting information related to an external environment of the host vehicle including an obstacle existing around the host vehicle and a boundary of a road on which the host vehicle is traveling;
Own vehicle state detecting means for detecting the running state of the own vehicle;
Predicted travel of the host vehicle within a predetermined evaluation time using at least the degree of approach of the host vehicle with respect to the obstacle and the boundary of the road and the driving operation amount of the host vehicle necessary to realize the predicted travel locus as evaluation items An evaluation function setting means for setting an evaluation function for evaluating the trajectory;
Optimal operation amount for determining a predicted travel locus in which the value of the evaluation function set by the evaluation function setting means is a minimum value and calculating a driving operation amount of the host vehicle necessary to realize the determined predicted travel locus A calculation means;
Own vehicle control means for supporting the driving operation of the own vehicle according to the driving operation amount calculated by the optimum operation amount calculating means,
The evaluation function setting means relatively changes the weight of the evaluation item according to an output from at least one of the external environment detection means and the own vehicle state detection means. The driving operation support device according to claim 1,
The evaluation function setting means relatively changes the weight of the evaluation item for each prediction time according to an output from at least one of the external environment detection means and the own vehicle state detection means. Support device. In the driving operation support device according to claim 1 or 2,
The evaluation function setting unit is configured to determine a contact risk level for determining a contact risk level between the obstacle and the boundary portion of the road according to an output from at least one of the external environment detection unit and the own vehicle state detection unit. A driving operation support apparatus characterized in that the weight of the evaluation item related to the driving operation amount of the host vehicle is relatively increased as the contact risk determined by the contact risk determination unit decreases. In the driving operation support device according to any one of claims 1 to 3,
The evaluation function setting unit is configured to determine a contact risk level for determining a contact risk level between the obstacle and the boundary portion of the road according to an output from at least one of the external environment detection unit and the own vehicle state detection unit. A driving operation support apparatus characterized in that the weight of an evaluation item related to the initial driving operation amount of the host vehicle is relatively increased as the contact risk determined by the contact risk determination unit decreases. In the driving operation support device according to any one of claims 1 to 4,
The evaluation function setting unit includes a contact risk determination unit that determines a contact risk level with the obstacle according to an output from at least one of the external environment detection unit and the own vehicle state detection unit. The driving operation support apparatus characterized by relatively increasing the weight of the evaluation item regarding the degree of approach to the obstacle as the contact risk determined by the risk determination means is higher. In the driving support device according to any one of claims 1 to 5,
The evaluation function setting unit includes a contact risk determination unit that determines a contact risk level with the obstacle according to an output from at least one of the external environment detection unit and the own vehicle state detection unit. A driving operation support apparatus that relatively increases the weight of an evaluation item related to the degree of access to the obstacle while approaching the obstacle as the contact risk determined by the risk determination unit increases. . In the driving operation support device according to any one of claims 1 to 6,
The evaluation function setting unit includes a contact risk determination unit that determines a contact risk with the boundary portion of the road according to an output from at least one of the external environment detection unit and the own vehicle state detection unit. The driving support device is characterized in that, as the contact risk determined by the contact risk determination means is higher, the weight of the evaluation item regarding the degree of approach to the boundary portion of the road is relatively increased. The driving operation support device according to any one of claims 1 to 7,
The evaluation function setting unit includes a contact risk determination unit that determines a contact risk with the boundary portion of the road according to an output from at least one of the external environment detection unit and the own vehicle state detection unit. The higher the contact risk determined by the contact risk determination means, the higher the weight of the evaluation item related to the degree of approach with the boundary of the road when the host vehicle returns to the posture. Driving operation support device. The driving operation support device according to any one of claims 1 to 8,
The evaluation function setting means sets the evaluation function including an evaluation item related to the degree of approach of the host vehicle to the obstacle movement trajectory predicted based on the obstacle information detected by the external environment detection means. A driving operation support device. In the driving support device according to any one of claims 1 to 9,
The optimum operation amount calculating means updates the operation amount by executing a calculation every predetermined time. In the driving support device according to claim 10,
Having avoidance route evaluation means for evaluating the travel route of the vehicle when the time series driving operation amount calculated by the optimum operation amount calculation means at the time prior to the current time is executed at the current time;
The evaluation function setting means relatively sets the weight of the evaluation item related to the amount of driving operation until the time when the risk of contact with the obstacle and the boundary portion of the road is high based on the evaluation result by the avoidance route evaluation means. A driving operation support device characterized by being made smaller. In the driving operation support device according to claim 10 or 11,
Having avoidance route evaluation means for evaluating the travel route of the vehicle when the time series driving operation amount calculated by the optimum operation amount calculation means at the time prior to the current time is executed at the current time;
The evaluation function setting means compares the contact risk with the obstacle and the contact risk with the boundary portion of the road based on the evaluation result by the avoidance route evaluation means, and relates to an object having a high contact risk. A driving support device characterized by relatively increasing the weight of an evaluation item. The driving operation support device according to any one of claims 10 to 12,
Having avoidance route evaluation means for evaluating the travel route of the vehicle when the time series driving operation amount calculated by the optimum operation amount calculation means at the time prior to the current time is executed at the current time;
The evaluation function setting means determines whether it is difficult to return the attitude of the vehicle after avoidance based on the evaluation result by the avoidance path evaluation means. A driving operation support device characterized by relatively reducing the weight of an evaluation item related to the amount of driving operation when returning. As an evaluation item, at least the degree of approach of the host vehicle and the amount of driving operation of the host vehicle necessary to realize the predicted traveling locus with respect to obstacles around the host vehicle and the boundary of the road on which the host vehicle is traveling are predetermined. A first process for setting an evaluation function for evaluating a predicted travel locus of the host vehicle within the evaluation time;
A second process for determining a predicted travel locus in which a value of the set evaluation function is a minimum value, and calculating a driving operation amount of the host vehicle necessary to realize the determined predicted travel locus;
And a third process for supporting the driving operation of the host vehicle according to the calculated driving operation amount,
The first process includes a process of relatively changing a weight of the evaluation item according to at least one of an external environment and a running state of the host vehicle.

Images