JP2018034549A - Vehicular motion control method and vehicular motion control apparatus - Google Patents

Abstract

To provide a vehicle motion control method capable of grasping vehicle motion generated by a system mounted on a vehicle without checking an information device and reducing a driver load. In a vehicle motion control method for controlling vehicle motion based on external environment recognition and own vehicle state recognition, the vehicle motion control method is calculated from a physical relative relationship between the own vehicle and an obstacle existing around the own vehicle. The risk feeling index value, which is a risk feeling index, is corrected for each driving scene based on the driving environment judgment to calculate risk feeling index correction values (RPf-2, RPr-2) (steps S103 to S106). The vehicle motion of the host vehicle is generated according to the index correction values (RPf-2, RPr-2) (steps S107 to S109). [Selection] Figure 4

Description

  The present disclosure relates to a vehicle motion control method and a vehicle motion control apparatus that control vehicle motion based on external environment recognition and host vehicle state recognition.

  Conventionally, a system generates a driving strategy plan related to positioning in a road area, and presents the driving strategy plan to a driver via a display. There is known a vehicle motion control method in which a driver sets a driving strategy in consideration of the proposed driving strategy, and the system generates a movement trajectory of the vehicle based on the set driving strategy (for example, Patent Document 1).

JP 2010-198578 A

  In the conventional vehicle motion control method, the driver must check the information presented on the display, check the actual situation around the vehicle, and respond with a correction input if the driving strategy plan cannot be accepted. There is. In other words, the driver must quickly understand the driving strategy proposed by the system and make necessary decisions. Therefore, the driving strategy is determined and instructed within a limited time, and there is a problem that the system becomes a heavy load on the driver.

  The present disclosure has been made paying attention to the above problem, and can make it possible to grasp the vehicle motion generated by the system mounted on the vehicle without confirming the information device, thereby reducing the driver load. It is an object to provide a motion control method and a vehicle motion control apparatus.

  In order to achieve the above object, the present disclosure is a vehicle motion control method for controlling vehicle motion based on recognition of the outside world and recognition of the state of the host vehicle, and includes the host vehicle and an obstacle existing around the host vehicle. A risk potential correction value is calculated by correcting the risk potential, which is a risk sensation index calculated from the physical relative relationship, for each driving scene based on the driving environment judgment. Then, the vehicle motion of the host vehicle is generated according to the calculated risk potential correction value.

  As a result, the vehicle motion generated by the system mounted on the vehicle can be grasped without confirming the information device, and the driver load can be reduced.

1 is an overall system configuration diagram illustrating a vehicle motion support system to which a vehicle motion control method and a motion control device according to a first embodiment are applied. It is explanatory drawing which shows the setting image of the weighting coefficient for every driving scene used when correcting a risk feeling index value. It is a block diagram which shows the control system of an actual risk index value. It is a flowchart which shows the flow of the vehicle motion control process performed with the vehicle control calculating apparatus of Example 1. FIG. 3 is a time chart showing characteristics of an actual risk index value controlled by the vehicle motion control apparatus of the first embodiment. It is explanatory drawing which shows the model learning image of the risk feeling index value by experiencing the vehicle motion in a some driving scene. It is a flowchart which shows the flow of the vehicle motion control process performed with the vehicle control calculating apparatus of Example 2. FIG. It is a time chart which shows the characteristic of the real risk feeling index value controlled by the movement control device of vehicles of Example 2.

  Hereinafter, a mode for carrying out a vehicle motion control method and a vehicle motion control device of the present disclosure will be described based on a first embodiment and a second embodiment shown in the drawings.

Example 1
First, the configuration will be described.
The vehicle motion control method and the motion control device according to the first embodiment are applied to a driving support vehicle equipped with a system that supports vehicle motion based on recognition of the outside world and recognition of the state of the host vehicle. Hereinafter, the configuration of the first embodiment will be described by dividing it into “entire system configuration” and “vehicle motion control processing configuration”.

[Overall system configuration]
FIG. 1 is an overall system configuration diagram illustrating a vehicle motion support system to which a vehicle motion control method and a motion control device according to a first embodiment are applied. The overall system configuration of the first embodiment will be described below with reference to FIG.

  As shown in FIG. 1, the vehicle movement support system according to the first embodiment includes an external environment recognition device 1, a host vehicle state recognition device 2, a vehicle control arithmetic device 3, a control information database 4, and a vehicle drive device 5. It is equipped with.

The external environment recognition device 1 is a device that is provided in the host vehicle and recognizes a traffic environment such as moving obstacle information, road shape, and stationary obstacle information existing around the host vehicle. Here, the “moving obstacle” is an obstacle that is moving (movable) such as another vehicle running around the host vehicle, a pedestrian, or a bicycle. Further, the “stationary obstacle” is an obstacle that is stationary (not moving) such as a lane marking, a curb, a fence, or a wall provided on the road surface.
As the outside recognition apparatus 1, a generally used laser range finder, a clearance sonar using ultrasonic waves, a monocular camera that captures images and obtains captured image information, and a stereo camera having a plurality of imaging units Etc. are used. The laser range finder is a device that can irradiate a target with an infrared laser and measure the distance to the target based on the degree of reflection. The distance information to the detected object can be acquired as point cloud information. ing. Furthermore, the external environment recognition apparatus 1 includes a distance measuring sensor, measures the distance between the host vehicle and surrounding obstacles, and generates distance data.
The traffic environment information around the vehicle recognized by the external recognition device 1 is output to the vehicle control arithmetic device 3.

The own vehicle state recognition device 2 is a device that is provided in the own vehicle and recognizes own vehicle state information such as the traveling speed and traveling position of the own vehicle.
For example, a wheel speed sensor, a steering angle sensor, or a yaw rate sensor is used as the vehicle state recognition device 2. The vehicle speed sensor detects the vehicle speed by detecting the rotational speed of the axle. The steering angle sensor detects the steering angle (the inclination of the wheel with respect to the vehicle longitudinal direction) by detecting the rotation axis of the steering actuator. The yaw rate sensor detects the yaw rate (change speed of the rotation angle in the turning direction).
The own vehicle state information recognized by the own vehicle state recognition device 2 is output to the vehicle control arithmetic device 3.

  The vehicle control computation device 3 is a device that computes a travel plan of the host vehicle based on information obtained by the outside world recognition device 1 and the host vehicle state recognition device 2, and is based on the outside world recognition and the host vehicle state recognition. Controller for controlling vehicle motion. The vehicle control calculation device 3 includes a travel environment determination unit 31, a risk feeling index value calculation unit 32, and a vehicle travel plan unit 33.

The travel environment determination unit 31 is based on the traffic environment information input from the external environment recognition device 1, the vehicle state information input from the vehicle state recognition device 2, and route information to a preset destination. Thus, the traveling environment in which the host vehicle will travel is determined. A driving scene that will occur in the future is determined from the determination of the driving environment.
Here, the “driving scene” is, for example, a “preceding vehicle follow-up approach scene” approaching a vehicle traveling immediately before the host vehicle, or a vehicle traveling immediately after the host vehicle approaches the host vehicle. For example, “rear vehicle following approach scene”.

  The risk sensation index value calculation unit 32 (risk potential correction value calculation unit) is based on the obstacle information input from the external environment recognition device 1 and the vehicle state information input from the vehicle state recognition device 2. A physical relative relationship between the host vehicle and an obstacle existing around the host vehicle (hereinafter referred to as “vehicle surrounding obstacle”) is detected. Then, the risk index value (= risk potential) calculated from the physical relative relationship is corrected in accordance with the risk sensitivity that differs for each driving scene. Then, the risk feeling index value corrected according to the risk sensitivity for each driving scene is further corrected so as to be proportional to the driver's sense scale, and a risk feeling index correction value (= risk potential correction value) is calculated.

  “Risk index value (= risk potential)” is the distance between an obstacle (inter-vehicle distance), relative speed with the obstacle, arrival time, etc. This is a risk index calculated from the physical relative relationship (an index of the risk of approaching the vehicle to the obstacle). “Risk sensitivity” is the ease with which a driver feels risk. In a driving situation with high risk sensitivity, the driver feels that the risk is high even if the risk index values are the same. The “driver's sense scale” is a risk judgment standard based on a driver's sense of risk.

Here, the risk feeling index value uses an inter-vehicle time THW (time headway) as an index representing a steady state with respect to surrounding vehicles and a margin time TTC (time to collision) as an index representing a transient state with respect to a preceding vehicle. Is calculated from the following equation (1).
RP = 1 / THW + 1 / TTC (1)
Where THW = inter-vehicle distance / vehicle speed
TTC = distance between vehicles / relative speed.

On the other hand, in order to correct the risk feeling index value obtained by the above formula (1) in accordance with the risk sensitivity that differs for each driving scene, the risk feeling index value is corrected using a weighting coefficient that is preset for each driving scene. In addition, the risk feeling index value (risk feeling index value reflecting the risk sensitivity corresponding to the driving scene) corrected according to the risk sensitivity that differs for each driving scene is referred to as a “first risk potential correction value”.
For example, the first risk potential correction value RP _f−1 in the “preceding vehicle following approach scene” is calculated from the following equation (2f). Further, the first risk potential correction value RP _r−1 in the “rear vehicle following approaching scene” is calculated from the following equation (2r).
RP _f-1 = Af / THW + Bf / TTC (2f)
RP _r-1 = Ar / THW + Br / TTC (2r)
As a result, the expression (2f) and the expression (2r) have the same form of the arithmetic expression, but the weighting coefficients (Af, Bf, Ar, Br) are different, so that the risk feeling correction value is different for each scene. The value is corrected according to the risk sensitivity.

An arithmetic expression (risk-specific risk index formula) for correcting the risk index value for each driving scene using this weighting coefficient (Af, Bf, Ar, Br) It is stored in the index formula storage unit 41. Here, the weighting coefficients (Af, Bf, Ar, Br) can be set from an average value obtained from driving behavior statistical data of manual driving.
Specifically, as shown in FIG. 2, first, driving behavior data for each driver is grouped from the data accumulated in the driving behavior database 100, and the driving behavior data of the same driver is classified by road category. The “road category” is a classification based on the size of the road, such as a main road with many lanes, a general road connecting the main road and a living road, and a living road around a residential area. The reason why driving behavior data is classified by road category is that driving methods differ depending on the size of the road.
Next, from the driving behavior data categorized by road category, select the data by driving scene, and based on the scene where the switching of driving behavior (brake, accelerator, steering operation (steering avoidance) was performed) The distribution of risk index values for each driving scene is determined for each driver.
Then, the risk sensitivity index value distribution by individual driving scene is classified for each identical driving scene, and the risk sensitivity in the driving scene is calculated from the average value of the risk feeling index value that causes switching of driving behavior in the driving scene. Is obtained. Then, a weighting coefficient (Af, Bf, Ar, Br) for each driving scene is calculated based on the obtained risk sensitivity.

  The risk sensitivity for each individual can be determined from the distribution of risk index values for each individual driving scene. That is, there is an individual difference in the ratio between the reciprocal of the inter-vehicle time THW and the reciprocal of the margin time TTC. Therefore, the individual risk sensitivity can be derived from the line (inclination) where the driving behavior indicated by the distribution with both elements as axes is switched. Therefore, correction based on differences in individual risk sensitivities (correction based on differences in individual sensitivities) may be added to the weighting coefficients (Af, Bf, Ar, Br) for each driving scene based on risk sensitivity.

That is, for example, the calculation formula for obtaining the first risk potential correction value RP _f-1 in the "preceding vehicle following approaching scene" is set as the following formula (2f-1) and formula (2f-2) for each driver. The
Driver α: RP _f-1-α = Afα / THW + Bfα / TTC (2f-1)
Driver β: RP _f-1-β = Afβ / THW + Bfβ / TTC (2f-2)
Further, in the “rear vehicle following approach scene”, the calculation formula for obtaining the first risk potential correction value RP _r−1 is set for each driver as the following formula (2r−1) and formula (2r-2). .
Driver α: RP _r-1-α = Arα / THW + Brα / TTC (2r-1)
Driver β: RP _r-1-β = Arβ / THW + Brβ / TTC (2r-2)

  Further, in order to correct the first risk potential correction value calculated for each driving scene according to the above formula (2f) or formula (2r) so as to be proportional to the driver's sense scale, a risk set in advance for each driving scene is used. Correction is performed using the feeling correction coefficient. Thereby, for example, the risk index value in the preceding vehicle following approach scene and the risk index value in the rear vehicle following approach scene can be quantified on the same sensory scale.

A value obtained by correcting the first risk potential correction value so as to be proportional to the driver's sense scale is referred to as a “risk feeling index correction value”.
For example, the risk index correction value RP _f-2 for the “preceding vehicle following approach scene” is calculated from the following equation (3f). The risk index correction value RP _r-2 for the “rear vehicle following approaching scene” is calculated from the following equation (3r).
RP _f−2 = Cf (Af / THW + Bf / TTC) (3f)
RP _r-2 = Cr (Ar / THW + Br / TTC) (3r)
That is, the quantified risk sensation amount per unit amount varies depending on the setting of the risk sensation correction coefficient (Cf, Cr). Therefore, by appropriately setting this risk feeling correction coefficient (Cf, Cr), the criteria for judging the risk felt by the driver can be made equal across scenes (in any driving scene). .

The risk feeling correction coefficient (Cf, Cr) is stored in the inter-scene risk feeling correction coefficient storage unit 42 of the control information database 4. Here, the risk feeling correction coefficient (Cf, Cr) can be set from an average value obtained from driving behavior statistical data.
Specifically, first, driving behavior data of individual drivers is recorded for each driving scene classified according to road conditions, driving environment, and the like. Next, in a driving situation where the risk index value is set to the initial value, when the driving behavior is switched (when braking, accelerator, steering operation (steering avoidance) is performed), the risk index value is It is considered that the initial value has been exceeded, and a provisional correction coefficient is set for each driving scene. Subsequently, the provisional correction coefficient is calculated in the same manner for each of a plurality of driving scenes and individual drivers. Then, the risk sensation correction coefficient (Cf, Cr) for each driving scene is calculated by statistically deriving the average value of the calculated provisional correction values between a plurality of scenes.

The vehicle travel planning unit 33 (vehicle motion generation unit) is based on the risk feeling index correction value calculated by the risk feeling index value calculation unit 32 and a preset risk feeling control value. (Vehicle motion) is generated. The travel plan information generated by the vehicle travel plan unit 33 is input to the vehicle drive device 5.
The “travel plan” is a future speed of the host vehicle, a distance between vehicles, a travel locus, and the like, and is a general future vehicle motion. Further, the “risk sense control value” is a reference value of a risk sense index when generating a controlled vehicle motion for sharing the sense of risk felt by the driver among a plurality of driving scenes. This risk feeling control value is an arbitrary value based on experiments, etc. based on the balance between the driver's sense of security with respect to vehicle movement and the driving feeling that gives priority to driving efficiency. And stored in the risk feeling control value storage unit 43 of the control information database 4.

Then, the vehicle travel planning unit 33 generates a vehicle motion of the host vehicle based on a risk index that does not exceed the risk control value. That is, when the risk feeling index correction value calculated by the risk feeling index value calculation unit 32 is equal to or greater than the risk feeling control value, the vehicle motion of the host vehicle is generated according to the risk feeling control value. Further, when the risk feeling index correction value calculated by the risk feeling index value calculation unit 32 is less than the risk feeling control value, the vehicle motion of the host vehicle is generated according to the risk feeling index correction value.
“Generate the vehicle motion of the host vehicle according to the risk index correction value” means that the actual risk index value that changes due to the vehicle motion becomes the risk index correction value. Is set, and the vehicle is controlled aiming at the target value.

Specifically, as shown in FIG. 3, first, the risk index correction value is input to the target RP setting unit 101 as a target value. On the other hand, based on the vehicle speed of the host vehicle, the relative speed with the surrounding vehicles, and the inter-vehicle distance, the actual RP calculation unit 102 calculates an actual risk index value. Then, the difference (deviation) between the risk feeling index correction value that is the target value and the actual risk feeling index value is calculated, and this difference is input to the RP adjustment unit 103.
The RP adjustment unit 103 calculates a speed control value for controlling the speed of the host vehicle so as to reduce the difference between the risk index correction value and the actual risk index value by a gain corresponding to the input difference. The Then, the speed calculation unit 104 adds the influence of disturbance to the calculated speed control value to calculate the vehicle speed of the host vehicle. In addition, by controlling the vehicle speed, the relative speed with the surrounding vehicle and the inter-vehicle distance also change.
Then, by performing feedback control using the changed vehicle speed, the relative speed with the surrounding vehicles, and the inter-vehicle distance, a vehicle motion that matches the actual risk index value with the risk index correction value is generated.

On the other hand, the same applies to “generate the vehicle motion of the host vehicle according to the risk control value”, and the actual risk index value that changes due to the vehicle motion becomes the risk control value. A target value is set, and the vehicle is controlled aiming at the target value.
That is, as shown in FIG. 3, first, the risk feeling control value is input to the target RP setting unit 101 as a target value. On the other hand, based on the vehicle speed of the host vehicle, the relative speed with the surrounding vehicles, and the inter-vehicle distance, the actual RP calculation unit 102 calculates an actual risk index value. Then, the difference (deviation) between the risk feeling control value that is the target value and the actual risk feeling index value is calculated, and this difference is input to the RP adjustment unit 103.
The RP adjustment unit 103 calculates a speed control value for controlling the speed of the host vehicle so as to reduce the difference between the risk feeling control value and the actual risk feeling index value by a gain corresponding to the input difference. . Then, the speed calculation unit 104 adds the influence of disturbance to the calculated speed control value to calculate the vehicle speed of the host vehicle. In addition, by controlling the vehicle speed, the relative speed with the surrounding vehicle and the inter-vehicle distance also change.
Feedback control is performed using the changed vehicle speed, relative speed to surrounding vehicles, and inter-vehicle distance, so that the vehicle motion that does not exceed the risk control value, that is, the actual risk index value A vehicle motion that matches the sense control value is generated.

  The vehicle drive device 5 is an actuator for driving the host vehicle, and drives the host vehicle according to the travel plan information planned by the vehicle travel plan unit 33 of the vehicle control arithmetic device 3. As the vehicle drive device 5, a drive actuator, a brake actuator, a steering actuator, a select range & shift position actuator, or the like is used.

[Vehicle motion control processing configuration]
FIG. 4 is a flowchart illustrating a flow of a vehicle motion control process executed by the vehicle control arithmetic device according to the first embodiment. Hereinafter, each step of FIG. 4 showing the vehicle motion control processing configuration will be described.

In step S101, the information obtained by the external environment recognition device 1 is read, the traffic environment around the host vehicle is recognized, and the process proceeds to step S102.
Here, the external environment recognition apparatus 1 reads the position, moving speed, and moving direction of a moving obstacle (for example, a surrounding vehicle). Also, the coordinates of the moving obstacle collated with the map information, the type and coordinates of the stationary obstacle around the vehicle, and the lane line (lane, traffic zone) information as the road shape information are read.

In step S102, following the recognition of the traffic environment in step S101, the information obtained by the own vehicle state recognition device 2 is read to recognize the running state of the own vehicle, and the process proceeds to step S103.
Here, the own vehicle state recognition device 2 reads the traveling speed of the own vehicle, the traveling position collated with the map information, and the traveling direction on the map.

In step S103, following the recognition of the vehicle state in step S102, the traffic environment recognized in step S101, the vehicle state recognized in step S102, and route information to a preset destination are included. Based on the estimated driving environment, the driving scene to be generated is determined, and the process proceeds to step S104.
Here, the determination of the “driving scene” is made, for example, by dividing a scene where the host vehicle travels on a straight portion of the road and a scene where an intersection or a merge occurs in front of the host vehicle, It is determined whether the scene is to be maintained, follows the preceding vehicle, or changes the lane.

In step S104, following the determination of the driving scene in step S103, a scene-specific risk feeling index formula for correcting the risk feeling index value using the weighting coefficient set according to the driving scene determined in step S103 is obtained. Read and go to step S105.
Here, in order to read the risk sensation index formula for each scene, first, the driving scene information determined in step S103 is input to the control information database 4. In the control information database 4, based on the inputted driving scene information, the scene-specific risk feeling index formula related to the driving scene is retrieved from the scene-specific risk feeling index formula storage unit 41 and output.
For example, when it is determined that it is a driving scene in which approaching to the preceding vehicle, following traveling, lane change, and merging occur, a risk index expression (formula (2f), formula (2r), etc.) in the vehicle longitudinal direction is read. .

In step S105, following the reading of the scene-specific risk feeling index formula in step S104, the risk feeling correction coefficient (Cf, Cr) corresponding to the driving scene determined in step S103 is read, and the process proceeds to step S106.
Here, the “risk feeling correction coefficient” is a correction coefficient for correcting the risk feeling index value corrected in accordance with the risk sensitivity that differs for each driving scene so as to be proportional to the driver's sense scale.
In order to read the risk correction coefficient, first, the driving scene information determined in step S103 is input to the control information database 4. In the control information database 4, based on the inputted driving scene information, the risk feeling correction coefficient related to the driving scene is searched from the inter-scene risk feeling correction coefficient storage unit 42 and output.
Here, the risk feeling correction coefficient (Cf, Cr) in the approach tracking scene is used as a reference, and the risk feeling correction coefficient corresponding to the scene is read in the lane change scene and the merge scene.

In step S106, following the reading of the risk sensation correction coefficient in step S105, the risk sensation index value calculated from the physical relative relationship between the host vehicle and the obstacles around the vehicle is set according to the risk sensitivity that differs for each driving scene. A risk sense index correction value (RP _f-2 , RP _r-2 ) obtained by correcting the corrected values (RP _f−1 , RP _r−1 ) to be proportional to the driver's sense scale, Proceed to S107.
Here, the calculation of the risk index correction value is based on the traffic environment information around the host vehicle recognized in step S101 and the running state information of the host vehicle recognized in step S102, and in step S104. Calculation is performed by multiplying the read risk sensation index formula for each scene and the risk sensation correction coefficient for each driving scene read in step S105 (see formulas (3f) and (3r)).

In step S107, following the calculation of the risk sensation index correction value in step S106, it is determined whether or not the risk sensation index correction value (RP _f-2 , RP _r-2 ) is equal to or greater than the risk sensation control value. . If YES (risk sense index correction value ≧ risk sense control value), the process proceeds to step S108. If NO (risk sense index correction value <risk sense control value), the process proceeds to step S109.
Here, the risk feeling control value is read from the risk feeling control value storage unit 43 of the control information database 4.

  In step S108, following the determination that the risk feeling index correction value ≧ risk feeling control value in step S107, the actual risk feeling index value is reduced so that the actual risk feeling index value does not exceed the risk feeling control value. A vehicle motion is generated and the process proceeds to step S110. That is, a vehicle motion that matches the actual risk feeling index value with the risk feeling control value is generated.

In step S109, following the determination that risk sense index correction value <risk sense control value in step S107, the actual risk sense index value becomes risk sense index correction (RP _f-2 , RP _r-2 ). Then, a vehicle motion that continues (not restricts) the fluctuation of the actual risk index value is generated, and the process proceeds to step S110. That is, a vehicle motion that matches the actual risk index value with the risk index correction value (RP _f-2 , RP _r-2 ) is generated.

  In step S110, following the generation of the vehicle motion in step S108 or step S109, a vehicle drive signal that makes the actual vehicle motion of the host vehicle corresponding to the generated vehicle motion is output to the vehicle drive device 5, Go to the end.

  Next, the operations of the vehicle motion control method and the motion control apparatus according to the first embodiment are referred to as “risk sense index value scene-by-scene correction operation”, “risk sense index value cross-scene correction operation”, and “risk sense index value control”. The description will be divided into “control action”.

[Risk index value by scene correction]
In the vehicle motion control apparatus according to the first embodiment, the vehicle motion control process shown in FIG. 3 is executed during traveling. That is, the process proceeds from step S101 to step S102, the traffic environment around the host vehicle is recognized based on the information from the external environment recognition device 1, and the traveling state of the host vehicle is recognized based on the information from the host vehicle state recognition device 2. Then, the process proceeds to step S103 to determine a driving scene that will occur in the future.

When the driving scene is determined, the process proceeds to step S104, where the risk feeling index formula for correcting the risk feeling index value using the weighting coefficient set according to the driving scene (for example, Expression (2f), Expression (2r)). ). Using this scene-specific risk index formula, a risk index value (RP _f−1 , RP _r−1 = first risk potential correction value) that reflects the risk sensitivity according to the driving scene can be calculated. it can.

Subsequently, the process proceeds from step S105 to step S106, and the first risk potential correction value (RP _f−1 , RP _r−1 ) is corrected so as to be proportional to the driver's sense scale. The risk correction index (Cf, Cr) is read, and the read risk feeling correction coefficient (Cf, Cr) is multiplied by the risk-sensitive index formula for each scene (see formulas (3f) and (3r)), and the risk sense index Correction values (RP _f-2 , RP _r-2 ) are calculated.

Then, After calculating risk feeling index correction value _{(RP f-2, RP r} -2) , the routine proceeds to step S107, the risk feeling index correction value _{(RP f-2, RP r} -2) is the risk sense control It is determined whether or not the risk control value read from the value storage unit 43 is equal to or greater. If the risk feeling index correction value ≧ the risk feeling control value, the process proceeds from step S107 to step S108 to step S110, and the actual risk feeling index value is set so that the actual risk feeling index value does not exceed the risk feeling control value. A vehicle motion to be reduced is generated, and a vehicle driving signal is output so as to be the generated vehicle motion.
On the other hand, if the risk feeling index correction value <risk feeling control value, the process proceeds from step S107 to step S109 to step S110, and the actual risk feeling index value is the risk feeling correction value (RP _f-2 , RP _r-2 ). Thus, a vehicle motion that continues (does not limit) the fluctuation of the actual risk index value is generated, and a vehicle drive signal is output so as to be the generated vehicle motion.

As described above, the vehicle control arithmetic device 3 according to the first embodiment corrects the risk index value according to the risk sensitivity that is different for each driving scene, using, for example, the expressions (2f) and (2r). Then, the vehicle motion of the host vehicle is generated according to the value using the corrected risk feeling index value (RP _f−1 , RP _r−1 = first risk potential correction value).
Therefore, the host vehicle moves according to the risk index value that reflects different risk sensitivities for each driving scene, and the movement of the host vehicle matches the driving scene. As a result, the driver can estimate the movement of the own vehicle by grasping the driving scene, and can grasp the movement of the own vehicle sensuously.

  That is, when the driver senses the movement of the host vehicle, the driver senses the control characteristics of the vehicle, for example, “This vehicle (own vehicle) merges at such a distance between the vehicles and the relative speed”. I can understand. Therefore, the control characteristics of the system (vehicle control arithmetic device 3) can be transmitted to the driver using vehicle movement (vehicle movement) as medium information.

  As a result, the driver can grasp the vehicle movement generated by the system (vehicle control arithmetic device 3) mounted on the vehicle without checking the information device such as a display, for example. The load (burden) can be reduced.

It should be noted that, for example, using the formulas shown in Formula (2f-1), Formula (2f-2), Formula (2r-1), and Formula (2r-2), a risk sensation index corresponding to a different risk sensitivity for each driving scene In the case of correcting the value, correction based on individual sensitivity difference can be added to the correction of the risk index value. Therefore, the calculated risk index correction values (RP _f-1-α , RP _f-1-β , RP _r-1-α , RP _r-1-β ) more reflect the individual risk sensitivities. Of the vehicle that generates the vehicle motion in accordance with the risk index correction values (RP _f-1-α , RP _f-1-β , RP _r-1-α , RP _r-1-β ). The control characteristics are closer to the driving feeling of the driver. Therefore, the movement of the vehicle can be matched to the driving sensation of the driver who rides the vehicle, the control characteristics of the vehicle can be properly understood by the sense, the estimation accuracy for the movement of the vehicle is increased, and the system is The driver can be given a sense of security with respect to the generated vehicle movement.

[Cross-scene correction of risk index values]
Further, in the first embodiment, as described above, the risk feeling correction coefficient (Cf, Cr) for each driving scene is multiplied by the scene-specific risk feeling index formula (formula (2f), formula (2r)) to obtain a sense of risk. An index correction value (RP _f-2 , RP _r-2 ) is calculated (step S104 → step S105 → step S106). Here, the “risk sense index correction value” is a risk sense index value (RP _f−1 , RP _r−1 = first risk potential correction value) reflecting the risk sensitivity according to the driving scene. In this embodiment, the final movement of the host vehicle is generated using this “risk index correction value”.

  Therefore, the standard for judging the risk felt by the driver is equal across scenes (in any driving scene), and the movement of the host vehicle can be further adapted to the driver's sense. Therefore, the driver can more appropriately estimate the movement of the host vehicle based on the driving scene, and the driver load can be further reduced.

[Risk index value control action]
Further, in the first embodiment, as described above, the vehicle motion of the host vehicle is generated according to the risk index value that does not exceed the preset risk control value. In other words, it is determined whether or not the risk sensation index correction value is greater than or equal to the risk sensation control value, and if the risk sensation index correction value ≥ the risk sensation control value, the actual risk sensation index value does not exceed the risk sensation control value So as to generate vehicle motion. Further, if the risk feeling index correction value <the risk feeling control value, the vehicle motion is generated so that the actual risk feeling index value becomes the risk feeling correction value.

Here, the traveling environment of the vehicle changes with time. For example, even if the traveling state (vehicle speed) of the host vehicle is constant in the following traveling scene following the preceding vehicle, the traveling state of the preceding vehicle is not always constant and may change. Therefore, in such a situation, the risk index value calculated from the relative positional relationship (physical relative relationship) between the preceding vehicle and the host vehicle changes.
And even if such a risk index value is corrected according to different risk sensitivities for each driving scene and further corrected so as to be proportional to the driver's sense scale, the risk index correction value that is the correction value changes. .

  Therefore, when the vehicle motion is generated according to such a risk index correction value and the drive control is performed according to the generated vehicle motion, the physical relationship between the host vehicle and obstacles around the host vehicle is detected. The actual risk index value (actual risk index value) calculated from the target relative relationship fluctuates according to the driving environment around the vehicle, as indicated by the broken line in FIG.

  On the other hand, using a common risk feeling control value between driving scenes, the vehicle risk that the actual risk feeling index value (actual risk feeling index value) does not exceed the risk feeling control value as shown by the solid line in FIG. For example, when the risk feeling index correction value becomes higher than the risk feeling control value due to approaching the preceding vehicle in the preceding vehicle following traveling scene, the vehicle is decelerated to reduce the relative speed difference and maintain the inter-vehicle distance. Control is performed. That is, the actual risk feeling index value (actual risk feeling index value) is controlled so as not to generate a vehicle motion that exceeds the risk feeling control value, and the actual risk feeling index value can be controlled.

  That is, as shown in FIG. 6, there is a driving scene A in which the host vehicle is following the preceding vehicle at a vehicle speed Vh, and there are pedestrians and bicycles in front of the host vehicle traveling at the vehicle speed Vh. Even if the driving scene is different as in driving scene B, the risk feeling index values RPa and RPb are limited by the risk feeling control value, and a vehicle motion corresponding to the controlled risk feeling index value is generated. Thereby, the fluctuation | variation of the risk feeling which a driver feels during a driving | operation is suppressed.

  If the driver feels a plurality of driving scenes in which the risk index values are controlled in this way, the driver can sensuously grasp the degree of the risk index values controlled in the plurality of driving scenes. In other words, the driver uses the individual movement information to understand how the vehicle movement of the vehicle will be in a driving environment where the road structure and other vehicles traveling around the vehicle are always different. Instead, the model learning of the risk feeling index value makes it possible to grasp based on the risk feeling that is a criterion for determining vehicle motion. As a result, even in the driving scene (driving scene n) that the driver encounters for the first time, the driver can sensuously grasp the degree of the risk index value RPn controlled at that time, and the movement of the host vehicle Can be estimated in advance. Since the movement of the vehicle can be estimated, the driving of the host vehicle can be left to the system with peace of mind, and the driver's confidence in the system can be improved.

  In addition, as in the first embodiment, when the risk feeling index correction value exceeds the risk feeling control value, the actual risk feeling index value is suppressed. Does not suppress fluctuations in risk index values. As a result, the actual risk feeling index value is not increased more than necessary, and the vehicle motion emphasizing the driver's sense of security can be achieved.

Next, the effect will be described.
In the vehicle motion control method and the vehicle motion control apparatus according to the first embodiment, the following effects can be obtained.

(1) In a vehicle motion control method for controlling vehicle motion based on external world recognition and own vehicle state recognition,
The risk potential (risk index value), which is a risk index calculated from the physical relative relationship between the host vehicle and obstacles around the host vehicle, is corrected for each driving scene based on the driving environment judgment. Calculating a risk potential correction value (risk sensation index correction value = RP _f-2 , RP _r-2 ) (steps S103 to S106);
According to the risk potential correction value (risk feeling index correction value = RP _f−2 , RP _r−2 ), the vehicle motion of the host vehicle is generated (step S107 to step S109).
Accordingly, it is possible to grasp the vehicle motion generated by the system mounted on the vehicle without confirming the information device, and to reduce the driver load.

(2) When calculating the risk potential correction value (risk sense index correction value = RP _f-2 , RP _r-2 ), the risk potential (risk sense index value) is changed to a different risk sensitivity for each driving scene. The first risk potential correction values (RP _f−1 , RP _r−1 ) are calculated by using the corresponding weighting coefficients (Af, Bf, Ar, Br) to calculate the first risk potential correction (step S104). The risk potential correction value (risk sense index correction value = RP _f ) is obtained by correcting the values (RP _f−1 , RP _r−1 ) using a risk sense correction coefficient (Cf, Cr) that is proportional to the driver's sense scale. _-2 , RP _r-2 ) (steps S105 to S106).
As a result, in addition to the effect of (1), the criteria for judging the risk felt by the driver can be made equal across scenes, and the driver can more accurately infer the movement of the vehicle based on the driving scene, and the driver load can be estimated. Further reduction can be achieved.

(3) When generating the vehicle motion (steps S107 to S109), a risk potential correction value (risk feeling index correction value = RP _f-2 , RP _r-2 ) that does not exceed a preset risk feeling control value. Accordingly, the vehicle motion of the host vehicle is generated.
As a result, in addition to the effects of (1) or (2), the actual risk index value can be controlled regardless of the driving scene, and the fluctuation of the risk feeling felt by the driver during driving can be suppressed, and the driving scene encountered for the first time. But you can guess the movement of the vehicle.

(4) When generating the vehicle motion (step S107 to step S109), when the risk potential correction value (risk sense index correction value = RP _f-2 , RP _r-2 ) is greater than or equal to the risk sense control value, When the vehicle motion of the host vehicle is generated according to the risk feeling control value, and the risk potential correction value (risk feeling index correction value = RP _f−2 , RP _r−2 ) is less than the risk feeling control value, The vehicle motion of the host vehicle is generated according to the risk potential correction value (risk feeling index correction value = RP _f−2 , RP _r−2 ).
As a result, in addition to the effect of (3), it is possible to suppress the feeling of risk that the driver feels during driving, and to make the vehicle movement that places importance on the driver's sense of security.

(5) When calculating the risk potential correction values (risk feeling index correction values = RP _f−2 , RP _r−2 ) (steps S103 to S106), a correction based on the individual sensitivity difference is added.
As a result, the control characteristics of the vehicle can be made closer to the driving sensation of the driver, the control characteristics of the vehicle can be appropriately understood by the sense, and a sense of security with respect to the movement of the vehicle can be further provided to the driver.

(6) In a vehicle motion control device equipped with a controller (vehicle control arithmetic device 3) that controls vehicle motion based on external world recognition and own vehicle state recognition,
The controller (vehicle control arithmetic device 3)
The risk potential (risk index value), which is a risk index calculated from the physical relative relationship between the host vehicle and obstacles around the host vehicle, is corrected for each driving scene based on the driving environment judgment. A risk potential correction value calculation unit (risk feeling index value calculation unit 32) for calculating a risk potential correction value (risk feeling index correction value = RP _f-2 , RP _r-2 );
A vehicle motion generation unit (vehicle travel planning unit 33) that generates a vehicle motion of the host vehicle in accordance with the risk potential correction value (risk sense index correction value = RP _f-2 , RP _r-2 );
It was set as the structure which has.
Accordingly, it is possible to grasp the vehicle motion generated by the system mounted on the vehicle without confirming the information device, and to reduce the driver load.

(Example 2)
Example 2 is an example of controlling the vehicle motion so that the risk sense control value has an upper limit value and a lower limit value, and the actual risk sense index value converges between the upper limit value and the lower limit value.

  FIG. 7 is a flowchart illustrating a flow of a vehicle motion control process executed by the vehicle control arithmetic device according to the second embodiment. In addition, about the same step as the vehicle motion control process of Example 1, the same step number as FIG. 4 is attached | subjected and description is abbreviate | omitted here. Hereinafter, steps different from the vehicle motion control process shown in FIG. 4 will be described with reference to FIG.

In step S201 (vehicle motion generation step), following the calculation of the risk feeling index correction value in step S106, this risk feeling index correction value (RP _f-2 , RP _r-2 ) is the upper limit value of the risk feeling control value. It is determined whether or not it is equal to or greater than the upper limit of risk feeling. If YES (risk feeling index correction value ≧ risk feeling upper limit value), the process proceeds to step S202. If NO (risk feeling index correction value <risk feeling upper limit value), the process proceeds to step S203.
Here, the risk feeling upper limit value is read from the risk feeling control value storage unit 43 of the control information database 4.

In step S202, following the determination that risk sense index correction value ≧ risk sense upper limit value in step S201, the actual risk sense index value is reduced so that the actual risk sense index value does not exceed the risk sense upper limit value. A vehicle motion is generated and the process proceeds to step S110.
As for the control step when reducing the actual risk index value, as in the case of the first embodiment, the actual risk index value is feedback-controlled using the risk upper limit as a target value.

In step S203, following the determination that risk sense index correction value <risk sense upper limit value in step S201, the risk sense index correction value (RP _f-2 , RP _r-2 ) calculated in step S106 is the risk. It is determined whether or not it is less than the lower limit of risk feeling, which is the lower limit of the feeling control value. If YES (risk feeling index correction value <risk feeling lower limit value), the process proceeds to step S204. If NO (risk feeling index correction value ≧ risk feeling lower limit value), the process proceeds to step S205.
Here, the risk feeling lower limit value is read from the risk feeling control value storage unit 43 of the control information database 4. The risk feeling lower limit value is smaller than the risk feeling upper limit value, and there is a difference between the risk feeling upper limit value and the risk feeling lower limit value.

In step S204, following the determination that risk sense index correction value <risk sense lower limit value in step S203, the vehicle that increases the actual risk sense index value so that the actual risk sense index value exceeds the risk sense lower limit value. A motion is generated and the process proceeds to step S110.
As for the control step when increasing the actual risk index value, the actual risk index value is feedback-controlled using the lower limit of risk as the target value, as in the case of reducing the risk index value.

  In step S205, following the determination that risk sense index correction value ≧ risk sense lower limit value in step S203, the actual risk sense index value is continuously changed so that the actual risk sense index value becomes the risk sense index correction. A vehicle motion to be performed (not limited) is generated, and the process proceeds to step S110.

As described above, in the vehicle motion control apparatus of the second embodiment, the risk feeling control value has the risk feeling upper limit value that is the upper limit value and the risk feeling lower limit value that is the lower limit value. When the risk feeling index correction value (RP _f-2 , RP _r-2 ) is equal to or greater than the risk feeling upper limit value, the vehicle motion of the host vehicle is generated according to the risk feeling upper limit value. Further, when the risk feeling index correction value (RP _f-2 , RP _r-2 ) is less than the risk feeling lower limit value, the vehicle motion of the host vehicle is generated according to the risk feeling lower limit value. Further, when the risk feeling index correction value (RP _f-2 , RP _r-2 ) is not less than the risk feeling lower limit value and less than the risk feeling upper limit value, the risk feeling index correction value (RP _f-2 , RP _{r− 2} ) The vehicle motion of the own vehicle is generated according to ₂ ).

As a result, the actual risk index value (actual risk index value) calculated from the physical relative relationship between the host vehicle and the obstacles existing around the host vehicle is indicated by a solid line in FIG. It converges between the lower limit of risk feeling and the upper limit of risk feeling.
As a result, the fluctuation range of the risk feeling felt by the driver during driving can be reduced, the fluctuation of the risk feeling can be further suppressed, and the driver can feel a more stable driving feeling. In addition, it is possible to improve the driver's confidence in the system.

  As described above, the vehicle motion control method and the vehicle motion control device of the present disclosure have been described based on the first embodiment and the second embodiment, but the specific configuration is not limited to these embodiments. Design changes and additions are allowed without departing from the spirit of the invention according to each claim of the claims.

In the first embodiment, the first risk potential correction values (RP _f−1 , RP) in which the risk index values are corrected using the weighting coefficients (Af, Bf, Ar, Br) corresponding to the risk sensitivities that differ for each driving scene. _{An example is shown in which r-1} ) is further corrected by using a risk feeling correction coefficient (Cf, Cr) that is proportional to the driver's sense scale to obtain a risk feeling index correction value (RP _f-2 , RP _r-2 ). It was. However, the present invention is not limited to this. Since the “risk sense index correction value (= risk potential correction value)” may be a value obtained by correcting the risk sense index for each driving scene, the first risk potential correction values (RP _f−1 , RP _r−1). ) May be a “risk index correction value”.
Even in this case, the vehicle motions correspond to the risk index values that reflect different risk sensitivities for each driving scene, and the driver can estimate the movement of the vehicle by grasping the driving scene. Thus, the movement of the own vehicle can be grasped sensuously.

  Further, in the first embodiment, when risk index values are corrected according to different risk sensitivities for each driving scene, the weighting coefficients (Af, Bf, Ar, Br) for each driving scene are corrected based on differences in individual risk sensitivities. Although an example in which (correction by individual sensitivity difference) is added is shown, the present invention is not limited to this. For example, when the first risk potential correction value is corrected so as to be proportional to the driver's sense scale, correction based on the individual sensitivity difference may be added. That is, the individual sensitivity difference may be reflected in the risk feeling correction coefficient (Cf, Cr).

  In Example 2, the risk feeling control value has a risk feeling upper limit value that is an upper limit value and a risk feeling lower limit value that is a lower limit value, and a difference between the risk feeling upper limit value and the risk feeling lower limit value is obtained. An example with However, the present invention is not limited to this, and the risk feeling upper limit value and the risk feeling lower limit value may be the same value. In this case, a vehicle motion that matches (converges) the actual risk index value with the predetermined risk control value is generated.

  In the first and second embodiments, the vehicle motion control method and the motion control device according to the present disclosure generate a vehicle motion, and then drive the host vehicle by the vehicle driving device 5 according to the generated vehicle motion. That is, although the example applied to an automatic driving vehicle was shown, it is not restricted to this. For example, you may apply to the driving assistance vehicle which does not implement vehicle drive control, while showing the produced | generated vehicle motion on a display inside a vehicle. Furthermore, the generated vehicle motion may be presented on the in-vehicle display, and may be applied to a semi-automatic driving vehicle that performs vehicle drive control only when the actual vehicle motion greatly deviates from the generated vehicle motion. In short, the present invention can be applied to any driving assistance vehicle having a driving assistance function that assists vehicle movement based on recognition of the outside world and recognition of the host vehicle state.

DESCRIPTION OF SYMBOLS 1 Outside world recognition apparatus 2 Own vehicle state recognition apparatus 3 Vehicle control arithmetic unit (controller)
31 Driving environment determination unit 32 Risk index value calculation unit (risk potential correction value calculation unit)
33 Vehicle travel planning unit (vehicle motion generation unit)
4 Control Information Database 41 Scene-Specific Risk Sense Index Expression Storage Unit 42 Inter-Scene Risk Sense Correction Coefficient Storage Unit 43 Risk Sense Control Value Storage Unit 5 Vehicle Drive Device

Claims (7)

In a vehicle motion control method for controlling vehicle motion based on external environment recognition and own vehicle state recognition,
Risk potential correction value is calculated by correcting the risk potential, which is a risk index calculated from the physical relative relationship between the host vehicle and obstacles around the host vehicle, for each driving scene based on the driving environment judgment. And
A vehicle motion control method for generating vehicle motion of the host vehicle according to the risk potential correction value. The vehicle motion control method according to claim 1,
When calculating the risk potential correction value, the risk potential is corrected using a weighting coefficient corresponding to a different risk sensitivity for each driving scene to calculate a first risk potential correction value, and the first risk potential correction A vehicle motion control method, wherein the risk potential correction value is calculated by correcting the value using a risk feeling correction coefficient that is proportional to a driver's sense scale. In the vehicle motion control method according to claim 1 or 2,
When generating the vehicle motion, the vehicle motion of the host vehicle is generated according to a risk potential correction value that does not exceed a preset risk feeling control value. The vehicle motion control method according to claim 3,
When generating the vehicle motion, if the risk potential correction value is greater than or equal to the risk feeling control value, the vehicle motion of the host vehicle is generated according to the risk feeling control value, and the risk potential correction value is the risk feeling correction value. When the value is less than the control value, the vehicle motion of the host vehicle is generated according to the risk potential correction value. The vehicle motion control method according to claim 3,
The risk feeling control value has a risk feeling upper limit value that is an upper limit value, and a risk feeling lower limit value that is a lower limit value,
When generating the vehicle motion, if the risk potential correction value is greater than or equal to the risk feeling upper limit value, the vehicle motion of the host vehicle is generated according to the risk feeling upper limit value, and the risk potential correction value is the risk feeling correction value. When less than the lower limit value, the vehicle motion of the host vehicle is generated according to the risk feeling lower limit value, and when the risk potential correction value is not less than the risk feeling lower limit value and less than the risk feeling upper limit value, the risk A vehicle motion control method for generating vehicle motion of the host vehicle according to a potential correction value. The vehicle motion control method according to any one of claims 1 to 5,
When calculating the risk potential correction value, a correction based on a difference in individual sensitivity is added. In a vehicle motion control device equipped with a controller that controls vehicle motion based on external environment recognition and vehicle state recognition,
The controller is
Risk potential correction value is calculated by correcting the risk potential, which is a risk index calculated from the physical relative relationship between the host vehicle and obstacles around the host vehicle, for each driving scene based on the driving environment judgment. A risk potential correction value calculation unit that performs
A vehicle motion generation unit that generates a vehicle motion of the host vehicle according to the risk potential correction value;
A vehicle motion control device comprising: