JP2017128191A - Vehicle control device and vehicle control method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To surely stabilize a behavior of a vehicle by controlling driving torque on the basis of information on a coming environment for the vehicle and a current travelling situation of the vehicle.SOLUTION: A vehicle control device 200 comprises: a first driving torque calculating portion 282 that calculates first driving torque to be imparted in a turning direction of a vehicle 1000 on the basis of information on a coming environment for the vehicle 1000; a second driving torque calculating portion 284 that calculates second driving torque to be imparted in the turning direction of the vehicle 1000 on the basis of a current travelling situation of the vehicle 1000; and a required driving torque calculating portion 288 that calculates required driving torque to be imparted in the turning direction of the vehicle by weighting the first driving torque and the second driving torque on the basis of the current travelling situation of the vehicle 1000.SELECTED DRAWING: Figure 3

Description

  The present invention relates to a vehicle control device and a vehicle control method.

  Conventionally, for example, in Patent Document 1 below, when bad weather is predicted from weather information detected by another vehicle traveling ahead of a predetermined distance from the host vehicle, the vehicle is decelerated by downshift control to stabilize the vehicle. A control device for ensuring the safety is described.

JP 2005-285135 A

  However, in the technique described in Patent Document 1, when it is determined that the other vehicle traveling ahead is raining, the automatic transmission is downshifted to promote deceleration. When the road surface condition is worsening, it is not assumed that optimum control is performed in consideration of the road surface condition that is currently running.

  In particular, when the current road surface condition is a rainy road, a snowy road, etc., if the vehicle is controlled based on the road surface to be driven in the future, it becomes difficult to control the current road surface condition. It is assumed that the vehicle behavior becomes unstable such as slipping.

  Accordingly, the present invention has been made in view of the above problems, and an object of the present invention is to control the driving torque based on the future environmental information of the vehicle and the current traveling state of the vehicle. Accordingly, it is an object of the present invention to provide a new and improved vehicle control apparatus and vehicle control method capable of reliably stabilizing vehicle behavior.

  In order to solve the above-described problem, according to an aspect of the present invention, a first drive torque calculation unit that calculates a first drive torque to be applied in a turning direction of a vehicle based on future environmental information of the vehicle; A second driving torque calculator for calculating a second driving torque to be applied in the turning direction of the vehicle based on the current traveling state of the vehicle; and the first driving torque and the second based on the current traveling state of the vehicle. There is provided a vehicle control device including a drive request torque calculation unit that calculates a drive request torque to be applied in a turning direction of the vehicle by weighting the drive torque.

  The first drive torque calculation unit may calculate the first drive torque based on a road surface condition at a predicted future arrival point.

  The first driving torque is applied to the turning direction of the vehicle as a couple of forces to the left and right wheels, and the first driving torque calculating unit reduces the driving force difference between the left and right wheels by reducing the first driving torque. It may be made to do.

  Further, the first drive torque calculation unit may calculate the first drive torque based on an elapsed time after detecting a sign of bad weather.

  In addition, the first drive torque calculation unit may reduce the first drive torque as the elapsed time is longer.

  The second driving torque is applied to the turning direction of the vehicle as a couple of forces to the left and right wheels, and the second driving torque calculation unit reduces the second driving torque to reduce the difference in driving force between the left and right wheels. It is also possible to reduce the value.

  Further, the second driving torque calculation unit may reduce the second driving torque based on image information obtained by imaging a road surface with a camera as the current road surface condition is worse.

  In addition, the second drive torque calculation unit decreases the second drive torque as the difference increases based on the difference between the yaw rate model value calculated from the vehicle model and the actual yaw rate detected by the yaw rate sensor. There may be.

  Further, the drive request torque calculation unit increases the weight of the second drive torque with respect to the first drive torque based on image information obtained by imaging the road surface with a camera as the current road surface condition is worse. It may be.

  The drive request torque calculation unit may increase the weight of the second drive torque with respect to the first drive torque as the vehicle speed increases.

  Further, the drive request torque calculation unit is configured to calculate the second drive torque with respect to the first drive torque as the difference increases based on a difference between a yaw rate model value calculated from a vehicle model and an actual yaw rate detected by a yaw rate sensor. It is also possible to increase the weight.

  In order to solve the above problem, according to another aspect of the present invention, a step of calculating a first driving torque to be applied in a turning direction of the vehicle based on future environmental information of the vehicle, Calculating a second driving torque to be applied in the turning direction of the vehicle based on the current traveling state, and weighting the first driving torque and the second driving torque based on the current traveling state of the vehicle. And a step of calculating a drive request torque to be applied in the turning direction of the vehicle.

  As described above, according to the present invention, it is possible to reliably stabilize the vehicle behavior by controlling the driving torque based on the future environmental information of the vehicle and the current traveling state of the vehicle.

It is a mimetic diagram showing a vehicle concerning one embodiment of the present invention. It is a schematic diagram which shows turning control by steering. It is a schematic diagram which shows the structure of a control apparatus. It is a flowchart which shows the process performed with a control apparatus. It is a schematic diagram which shows a mode that the friction coefficient (mu) of a road surface changes with time at the time of rain. It is a characteristic view showing a gain map 270 for the first drive torque correction coefficient calculation unit to calculate the gain ζ. It is a flowchart which shows the process in which the 1st drive torque correction coefficient calculating part calculates the variation | change_quantity (DELTA) ζ of the gain (zeta) per unit time. FIG. 8 is a characteristic diagram showing a change in gain ζ adjusted by a change amount Δζ based on the processing in FIG. 7. It is a schematic diagram which shows the some monitoring area | region of the matrix form set in the rectangular image which the stereo camera imaged. It is a characteristic view showing a gain map for the second drive torque correction coefficient computing unit to calculate the gain τ. It is a characteristic view showing a gain map 274 for the second drive torque correction coefficient calculation unit to calculate the gain κ. FIG. 10 is a characteristic diagram showing a gain map for a second drive torque correction coefficient calculation unit to calculate a gain (GainV) according to the vehicle speed V. FIG. 10 is a characteristic diagram illustrating a gain map for a second drive torque correction coefficient calculation unit to calculate a gain (Gain γ_diff) corresponding to the yaw rate deviation γ_diff. It is a characteristic view which shows the gain map which calculates the gain (GainLoadCondition) according to the road surface condition which the external field recognition part detected. It is a schematic diagram which shows each parameter controlled by the process of this embodiment. It is a schematic diagram which shows each parameter controlled by the process of this embodiment. It is a schematic diagram which shows each parameter controlled by the process of this embodiment.

  Exemplary embodiments of the present invention will be described below in detail with reference to the accompanying drawings. In addition, in this specification and drawing, about the component which has the substantially same function structure, duplication description is abbreviate | omitted by attaching | subjecting the same code | symbol.

1. Configuration Example of Vehicle According to One Embodiment of the Present Invention First, a configuration of a vehicle 1000 according to one embodiment of the present invention will be described with reference to FIG. FIG. 1 is a schematic diagram showing a vehicle 1000 according to the present embodiment. As shown in FIG. 1, a vehicle 1000 includes driving force generators (motors) 108, 110, 112, driving front wheels 100 and 102, rear wheels 104 and 106, front wheels 100 and 102, and rear wheels 104 and 106. 114, wheel speed sensors 116, 118, 120, 122 for detecting the respective wheel speeds of the front wheels 100, 102 and the rear wheels 104, 106, a steering wheel 124, a steering angle sensor 130, a power steering mechanism 140, a yaw rate sensor 150, a lateral The apparatus includes an acceleration sensor 160, a control device (controller) 200, and an external recognition unit 220.

  The vehicle 1000 according to the present embodiment is provided with motors 108, 110, 112, and 114 for driving the front wheels 100 and 102 and the rear wheels 104 and 106, respectively. Therefore, the driving torque can be controlled by each of the front wheels 100 and 102 and the rear wheels 104 and 106. Accordingly, the yaw rate can be generated by the torque vectoring control by driving each of the front wheels 100 and 102 and the rear wheels 104 and 106 independently of the yaw rate generation by the steering of the front wheels 100 and 102. In particular, by controlling the torque of the rear wheels 104 and 106 individually, the yaw rate can be generated independently of the steering system. The driving torque of the rear wheels 104 and 106 is controlled by controlling the motors 112 and 114 corresponding to the rear wheels 104 and 106 based on a command from the control device 200.

The power steering mechanism 140 controls the steering angle of the front wheels 100 and 102 by torque control or angle control according to the operation of the steering wheel 124 by the driver. Steering angle sensor 130, the driver for detecting a steering angle theta _H entered by operating the steering wheel 124. The yaw rate sensor 150 detects the actual yaw rate γ of the vehicle 1000. Wheel speed sensors 116, 118, 120 and 122 detect vehicle speed V of vehicle 1000.

  Note that the present embodiment is not limited to this embodiment, and the motors 108 and 102 for driving the front wheels 100 and 102 are not provided, and only the rear wheels 104 and 106 are independently driven by the motors 112 and 114. The generated vehicle may be used. Further, the motors 112 and 114 that drive the rear wheels 104 and 106 may not be provided, and only the front wheels 100 and 102 may be vehicles that generate driving force independently of the motors 108 and 102.

  FIG. 2 is a schematic diagram showing turning control (steering control) by steering performed by the vehicle 1000 according to the present embodiment. In the turning control by the steering, the turning of the vehicle 1000 is supported by generating a driving force difference between the rear wheels 104 and 106 according to the operation of the steering wheel 130 by the driver. In the example shown in FIG. 2, the vehicle 1000 is turning to the left by the steering of the driver (driver). Further, due to the difference in driving force between the rear wheels 104 and 106, a forward driving force is generated on the right rear wheel 106, and the driving force on the left rear wheel 104 is suppressed with respect to the right rear wheel 106 or backwards. By generating a driving force, a driving force difference is generated on the left and right, and a moment is generated in a direction that supports a counterclockwise turn.

  The vehicle 1000 according to the present embodiment includes a driving force associated with turning support from sensors that measure the running state of the vehicle, such as the steering angle sensor 130, the wheel speed sensors 116, 118, 120, and 122, the yaw rate sensor 150, and the lateral acceleration sensor 160. The first driving torque designated based on the future information when the vehicle 1000 detects a sign that the vehicle 1000 becomes unstable from the future environmental information acquired from the external recognition unit 220. The distribution of left and right driving force is gradually reduced.

  On the other hand, when an environment in which the vehicle becomes unstable is detected based on the surrounding environment information acquired from the outside recognition unit 220 or the current vehicle behavior, the second drive torque specified based on the current situation By specifying the required driving force for each wheel in a situation where the distribution of the vehicle is increased, the difference in driving force between the left and right sides can be suppressed more quickly than in the situation where a sign of instability of the vehicle 1000 is detected from future environmental information. . Details will be described below.

2. Configuration of Control Device FIG. 3 is a schematic diagram showing the configuration of the control device 200. The control device 200 includes an in-vehicle sensor 210, a GPS 212, an external recognition unit 220, a drive target torque calculation unit 230, a first drive torque correction coefficient calculation unit 240, a second drive torque correction coefficient calculation unit 250, a vehicle model calculation unit 260, a subtraction. Unit 262, gain map processing units 270, 272, 274, 276, 278, 279, and drive target torque correction processing unit 280. The drive target torque correction processing unit 280 includes a first drive torque calculation unit 282, a second drive torque calculation unit 284, a torque correction coefficient calculation unit 286, and a drive request torque calculation unit 288.

  The external environment recognition unit 220 is a component for recognizing an external environment. The external recognition unit 220 can acquire weather information together with position information (GPS information). The external environment recognition unit 220 includes sensors such as a stereo camera, a raindrop sensor, and a gradient sensor, and acquires environmental information such as weather around the vehicle and road surface inclination. The outside world recognition unit 220 includes a wireless communication unit, and acquires weather information by wireless communication. Further, the external environment recognition unit 220 can acquire position information and precipitable water from the GPS 212, use the precipitable water as an indicator of weather information, and recognize that it is bad weather when a certain amount of precipitable water can be expected. be able to. The stereo camera included in the external recognition unit 220 captures the outside of the vehicle and acquires image information outside the vehicle, in particular, image information on the road surface in front of the vehicle, a lane indicating a traveling lane, a preceding vehicle, a traffic light, and various signs. . The stereo camera includes a pair of left and right cameras having image sensors such as a CCD sensor and a CMOS sensor, and acquires image information by imaging an external environment outside the vehicle.

3. Overview of Processing Performed by Control Device FIG. 4 is a flowchart showing processing performed by the control device 200. In FIG. 4, first, in step S100, the drive target torque calculator 230 calculates the drive target torque MotTrqTgt of each wheel. In the next step S110, the vehicle model calculation unit 260 calculates a vehicle model. In the next step S120, the first drive torque correction coefficient calculation unit 240 calculates the first drive target torque correction coefficient Coef1. In the next step S130, the first drive torque calculator 282 calculates the first drive torque MotTrq1. In the next step S140, the second drive torque correction coefficient calculation unit 250 calculates a correction coefficient Coef2 for the second drive target torque. In the next step S150, the second drive torque calculator 284 calculates the second drive torque MotTrq2. In the next step S160, the torque correction coefficient calculator 286 calculates the torque correction coefficient TrqCoef. In the next step S170, the drive request torque calculation unit 288 calculates the drive torque. In addition, the process of each step may be performed for each wheel.

4). Processing Performed by Each Component of Control Device Hereinafter, processing performed by each component of the control device 200 shown in FIG. 3 will be described in detail. The drive target torque calculation unit 230 calculates the control target torque (left and right torque distribution) to be applied to the wheels from the steering angle, the vehicle speed, and the yaw rate separately from the front and rear drive torques determined by the vehicle speed V, the accelerator opening, and the like. To do. The control target torque (left and right torque distribution) calculated here does not include the front and rear driving torque determined by the accelerator opening or the like. Specifically, the drive target torque calculating unit 230 controls the control target torques MotTrqTgtFL (FL wheel), MotTrqTgtFR (FR wheel), and MotTrqTgtRL (F wheel) to be applied to each wheel based on the vehicle speed V, the steering angle θ _H , and the yaw rate γ. RL wheel), MotTrqTgtRR (RR wheel). MotTrqTgtFL is the control target torque of the left front wheel, MotTrqTgtFR is the control target torque of the right front wheel, MotTrqTgtRL is the control target torque of the left rear wheel, and MotTrqTgtRR is the control target torque of the right rear wheel. Further, the control target torques calculated here are MotTrqTgtFL, MotTrqTgtFR, MotTrqTgtRL, and MotTrqTgtRR, which are torques serving as a basic amount of the left / right driving force distribution. The control target torque MotTrqTgt calculated by the drive target torque calculation unit 230 may be calculated by other methods, for example, by steering control by steering or vehicle control by lane detection by the external recognition unit 220. It may be determined by a state quantity, an instruction value given by a map, or the like.

  The first drive torque correction coefficient calculation unit 240 calculates a gain ζ for correcting the control target torque MotTrqTgt according to the elapsed time since the sign of the future bad weather is detected by the map processing using the gain map 270.

  FIG. 5 is a schematic diagram showing how the road friction coefficient μ changes with time during rain. As shown in FIG. 5, at time 0, the road surface is dry and the coefficient of friction μ is high. However, when it starts to rain at time t0, the coefficient of friction μ starts to decrease, and when about 20 minutes have passed since the start of the fall. The coefficient of friction μ is the lowest due to the influence of mud and dust. Thereafter, even if it continues to rain, mud and dust are washed away from the road surface, so the friction coefficient μ increases. When the rain stops at time t1, the friction coefficient μ increases, and when the road surface dries, the friction coefficient μ returns to the original high state.

  As described above, according to FIG. 5, it can be seen that the most slippery road surface state when about 20 minutes have passed since the rain began. Therefore, the first drive torque correction coefficient calculation unit 240 reduces the control target torque MotTrqTgt most at the time when about 20 minutes have elapsed from the start of rain according to the elapsed time since the sign of bad weather was detected. The gain ζ is calculated. For example, a sign of a future bad weather can be detected when a raindrop is first detected by a stereo camera or a raindrop sensor included in the external recognition unit 220. Thereby, as future environmental information, it can be estimated that the road surface condition deteriorates at the predicted arrival point 20 minutes after the first detection.

  FIG. 6 is a characteristic diagram showing a gain map 270 for the first drive torque correction coefficient calculation unit 240 to calculate the gain ζ. As shown in FIG. 6, the longer the elapsed time since the sign of bad weather is detected, the smaller the value of the gain ζ, and the gain ζ becomes 0 when the elapsed time TH2_P is reached. As an example, if a sign of bad weather is detected at time t = 0, TH2_P is set 20 minutes later. As a result, the gain ζ becomes zero when about 20 minutes have passed since the rain began. The first drive torque correction coefficient calculation unit 240 designates the gain ζ as the first drive torque correction coefficient Coef1 (Coef1 = ζ). Here, since the gain ζ is a gain according to the road surface condition of the future predicted arrival point obtained from the elapsed time since the sign of bad weather is detected, the second drive torque correction coefficient Coef2 is a future vehicle. The correction coefficient is based on the environmental information.

The first drive torque calculation unit 282 uses the first drive torque correction coefficient Coef1 (for each wheel) and the drive target as the first drive torque MotTrq1 obtained from future information out of the left and right drive torque distribution obtained from the turning situation of the vehicle. It is calculated by the product of torque MotTrqTgt (for each wheel). That is, the first drive torque MotTrq1 is calculated from the following equation (1). Note that (#) indicates that the same wheel is an object, and # indicates FL (left front wheel), FR (right front wheel), RL (left rear wheel), and RR (right rear wheel). .
MotTrq1 (#) = MotTrqTgt (#) × Coef1 (#) (1)

  Since the drive target torque MotTrqTgt is obtained separately (in parallel) from the front / rear drive torque determined by the vehicle speed, the accelerator opening, etc., the drive target torque MotTrqTgt does not include the front / rear drive torque. Therefore, by multiplying the target driving torque MotTrqTgt of the left and right wheels by Coef1, especially when the elapsed time is long and the value of Coef1 is decreasing, the driving force difference between the left and right wheels is suppressed.

  As described above, when the first drive torque calculation unit 282 acquires signs of bad weather from the future information in the vehicle that acquires the environmental information of the outside world, the first drive torque calculation unit 282 determines the predetermined operation amount and the future regarding the turning of the vehicle 1000. By calculating the first driving torque MotTrq1 to be applied to the vehicle 1000 based on such information, the future difference in driving force between the left and right is suppressed, and the stable performance of the vehicle is ensured. Further, when signs of bad weather are detected from future information, the signs are gradually changed in a direction that suppresses the difference between the left and right driving forces in accordance with the most slippery and dangerous time zone. Thereby, a vehicle behavior can be stabilized reliably.

  In addition, when detecting signs of bad weather and gradually changing the left and right driving force difference according to the most slippery time zone, the driving force distribution corresponding to the left and right equal torque (for example, four-wheel equal torque mode) is set to the target level. It is also good. For example, in the most dangerous time zone, the target value of the left and right torque distribution may be gradually changed in a decreasing direction so that the distribution is finally performed in the torque mode of the four wheels.

  From the above viewpoint, the first drive torque calculation unit 282 calculates the first time calculated according to the transition of parameters indicating the future state, such as the elapsed time after detecting the sign of bad weather, and GPS position information. The target value for the left-right torque distribution is gradually shifted to 0 with a 1-drive torque correction coefficient Coef1. As a result, the drive target torque MotTrqTgt gradually changes in the direction in which the left and right torque difference becomes 0, and the first drive torque MotTrq1 is calculated.

  Next, the calculation of the gain ζ will be described in detail. The first drive torque correction coefficient calculation unit 240 calculates the change amount Δζ of the gain ζ per unit time based on the vehicle speed V and the weather information (GPS). Specifically, the first drive torque correction coefficient calculation unit 240 calculates the gain ζ per unit time from the elapsed time after acquiring the sign of bad weather and the travel distance traveled by the vehicle after detecting the sign of bad weather. Is calculated.

FIG. 7 is a flowchart illustrating a process in which the first drive torque correction coefficient calculation unit 240 calculates the change amount Δζ of the gain ζ per unit time. The process of FIG. 7 is performed every predetermined control cycle. First, in step S10, the distance L _EST from the vehicle speed V and the predicted arrival time (for example, 20 minutes) T _EST to the predicted arrival point is calculated. The distance L _EST is calculated from the following equation (2).
L _EST = V × T _EST (2)

As the elapsed time T _EST , the most dangerous time (20 minutes) after the rain starts is designated. As the vehicle speed V, the current vehicle speed at the time of detection is used. However, the vehicle speed V is not limited to this, and an average value V _MEAN of the vehicle speed V calculated from a certain predetermined time zone (several to several hundreds before sampling) may be substituted.

As described above, according to FIG. 5, it can be seen that the most slippery road surface state when about 20 minutes have passed since the beginning of raining. Therefore, in step S10 of FIG. 7, the distance L _EST to the predicted arrival point is calculated with the elapsed time T _EST being 20 minutes. Thus, the predicted arrival point corresponds to a point that is the most slippery road surface state, and the distance L _EST corresponds to the distance from the current position of the vehicle 1000 to the predicted arrival point.

  In the next step S12, it is determined whether or not a sign of bad weather has been acquired. If a sign of bad weather has been acquired, the process proceeds to step S14. The sign of bad weather can be acquired from the stereo camera and raindrop sensor of the external environment recognition unit 220. It is also possible to acquire signs of bad weather by acquiring weather information (GPS) from the outside.

In step S14, the travel distance _LNOW is calculated from the vehicle speed V and the elapsed time since the bad weather was detected. Specifically, based on the following equation (3), the travel time L _NOW after the sign of bad weather is acquired in step S12 is calculated by integrating the sampling time Δt and the vehicle speed Vk at each sampling. .

In the next step S16, ΔL is obtained by subtracting L _NOW from L _EST based on the following equation (4).
ΔL = L _EST −L _NOW (4)

  Since ΔL calculated here represents the remaining distance to the predicted arrival point, the smaller ΔL, the closer to the predicted arrival point (the point that is the most slippery road surface state).

On the other hand, when no sign of bad weather is acquired in step S12, the process proceeds to step S18. That is, when it is assumed that the weather is good, the process proceeds to step S18. In step S18, the travel distance (L _NOW ) that has started to be accumulated after acquiring the sign of bad weather is reset to 0 (L _NOW = 0). In the next step S20, ΔL = ΔL _MAX 1 is set. Here, as ΔL _MAX 1, a value that is not reached in bad weather is designated. ΔL _MAX 1 is, for example, a positive value, and is a guard value that is handled as the maximum value in the microcomputer processing.

After steps S16 and S20, the process proceeds to step S22. The value of ΔL obtained in steps S16 and S20 represents the distance from the current position of the vehicle 1000 to the predicted arrival point (the point that is the most slippery road surface state), and it is assumed that the smaller the ΔL, the more slippery the road surface is. Is done. In step S22, it is determined whether the ΔL <ΔL _MAX 2, in the case of ΔL <ΔL _MAX 2 proceeds to step S24. On the other hand, if ΔL <ΔL _MAX 2 is not satisfied, the process proceeds to step S26. Note that ΔL _MAX 1 and ΔL _MAX 2 have a magnitude relationship of ΔL _MAX 1> ΔL _MAX 2. A time corresponding to ΔL _MAX 2 corresponds to TH1_P in FIG.

  When the process proceeds to step S24, it is considered to be close to the predicted arrival point (the point that is the most slippery road surface state), and therefore, the change amount Δζ of the gain ζ per unit time is set to DELTA_ZETA_P. Here, DELTA_ZETA_P is a positive value (DELTA_ZETA_P> 0).

  When the process proceeds to step S26, it is fine weather, so the amount of change Δζ of gain ζ per unit time is set to DELTA_ZETA_M. Here, DELTA_ZETA_M is a negative value (DELTA_ZETA_M <0).

  As described above, when there is a sign of bad weather, the first drive torque correction coefficient calculation unit 240 corrects the gain ζ change amount Δζ per unit time according to the distance from the current position to the predicted arrival point. The value of is output. On the other hand, when there is no sign of bad weather, the first drive torque correction coefficient calculation unit 240 enters the normal control return mode and outputs a negative value as the change amount Δζ of the gain ζ per unit time. Then, the first drive torque correction coefficient calculation unit 240 repeats the process of FIG. 7 to calculate the gain ζ by integrating the amount of change Δζ, and calculates the first drive torque correction coefficient based on the gain ζ. .

  FIG. 8 is a characteristic diagram showing the gain ζ calculated by the first drive torque correction coefficient calculation unit 240 based on the change amount Δζ based on the processing of FIG. As shown in FIG. 8, by performing the process of FIG. 7, ζ changes according to the change of ΔL, and the value of ζ increases as ΔL decreases. Therefore, the gain ζ can be calculated with higher accuracy with respect to the map in FIG. 6 according to the elapsed time since the sign of bad weather was detected.

As described above, in the present embodiment, environmental information at a future predicted arrival point is predicted, and when there is a sign of bad weather and the slippery road surface state is assumed at the predicted arrival point, the predicted arrival point is obtained. L _MAX 1 is designated as the point, the distance at which the torque is gradually changed is designated as L _MAX 2, and the first driving torque MotTrq1 is adjusted by the first driving torque correction coefficient Coef1, thereby driving torque according to future environmental information. Realize control. As a result, the driving torque is optimally controlled when the vehicle arrives at the predicted arrival point, so that the vehicle behavior at the predicted arrival point can be reliably determined. In the above-mentioned example, since the time zone in which about 20 minutes have passed since the beginning of raining is most slippery, the environmental information at the point where the vehicle is traveling when 20 minutes have passed is used as the future environmental information. However, the present invention is not limited to this. For example, if local weather information is acquired together with location information, and the route to the destination of the vehicle obtained from the navigation system passes through a rainy area, the rainy area is set as the predicted arrival point. The drive torque can be optimally controlled when it arrives.

  The second driving torque correction coefficient calculation unit 250 calculates the gain τ according to the road surface condition detected by the outside recognition unit 220 by the map process using the gain map 272. Further, the second drive torque correction coefficient calculation unit 250 calculates the gain κ according to the deviation γ_diff between the yaw rate model value γ_mdl and the actual yaw rate γ by the map processing using the gain map 274. Then, the second drive torque correction coefficient calculation unit 250 calculates a second drive torque correction coefficient Coef2 for correcting the control target torque MotTrqTgt from the product of the gain τ and the gain κ.

  First, calculation of the gain τ will be described. FIG. 9 is a schematic diagram showing a plurality of matrix monitoring areas 300 set in a rectangular image 240 captured by the stereo camera. In the example shown in FIG. 9, a scene in front of the vehicle is captured as a subject by the stereo camera, and a part of the preceding vehicle 350 and a part of the lane 360 are shown in the image 340.

  As shown in FIG. 9, the image captured by the stereo camera 130 is divided into a plurality of matrix-like areas, and each is used as a monitoring area 300, and the wet data rate RT, average luminance A, and number of luminance edges N in each monitoring area. Is calculated. Whether or not it is a snowy road is determined by calculating the number N of luminance edges in the horizontal direction of each monitoring area 300 and the overall average luminance A of each monitoring area 300, and the number N of luminance edges is smaller than a threshold value, and When the average brightness A is greater than the threshold, it can be determined that the road is a snowy road. Such a method is described in, for example, Japanese Patent Application Laid-Open No. 2001-43352.

  Whether or not the road is a wet road is determined by obtaining the height in the three-dimensional space with respect to the distance data to each predetermined area of the monitoring area 300 obtained from the stereo camera, and at a position lower than the road surface of the traveling road. Is counted as the number of wet data due to the fact that has moved to the road surface. A result obtained by dividing the number of wet data by the number of distance data corresponding to the road surface of the traveling road (the number of dry data) is calculated as a wet data rate RT, and when the wet data rate RT is larger than a threshold value, it is determined that the road is a wet road. Such a technique is described in, for example, JP-A-2001-41741.

  FIG. 10 is a characteristic diagram showing a gain map 272 for the second drive torque correction coefficient calculation unit 250 to calculate the gain τ. As shown in FIG. 10, the better the road surface condition (Load Condition) detected by the external recognition unit 220, that is, the dry road surface, the value of the gain τ increases, and the road surface condition (Load Condition) becomes TH2. When it reaches, the gain τ becomes MAX_GAIN. In addition, when the road surface condition (Load Condition) is smaller than TH1, the gain τ is MIN_GAIN (= 0).

Next, calculation of the gain κ will be described. The vehicle model calculation unit 260 refers to a vehicle model (planar two-wheel model) that simulates the behavior of the vehicle based on the vehicle speed V and the tire rudder angle δ, and γ in the following equations (5) and (6) Is calculated as the yaw rate model value γ_mdl. Also, β in the following equations (5) and (6) is calculated as a side slip angle model value (β_mdl). Incidentally, the tire steer angle δ is calculated by dividing the steering angle theta _H at the steering gear ratio.

In the above formula, constants and variables are as follows.
m: Vehicle inertia weight
I: Yaw inertia of vehicle δ: Steering angle (tire turning angle)
V: Vehicle speed β: Vehicle side slip angle γ: Yaw rate (= γ_mdl)
If: distance between the front axis position and the center of gravity position lr: distance between the rear axis position and the center of gravity position Kf, Kr: cornering force

The subtractor 262 calculates the yaw rate deviation (γ_diff) by taking the difference between the yaw rate sensor value (actual yaw rate γ) detected by the yaw rate sensor 150 mounted on the vehicle and the yaw rate model value γ_mdl. That is, the yaw rate deviation γ_diff is calculated from the following equation. The yaw rate deviation γ_diff is an index for discriminating the degree of deviation (model reliability) between the vehicle model and the actual vehicle behavior.
γ_diff = γ_mdl−γ

  FIG. 11 is a characteristic diagram showing a gain map 274 for the second drive torque correction coefficient calculation unit 250 to calculate the gain κ. In FIG. 11, TH1_P is a weighting gain switching threshold value (+ side), TH2_P is a weighting gain switching threshold value (+ side), and TH1_M is a weighting gain switching threshold value (−side). ), And TH2_M is a weighting gain switching threshold value (− side). The relationship between the threshold values is TH1_P <TH2_P and TH1_M> TH2_M. The second drive torque correction coefficient calculation unit 250 calculates the weighting gain κ using the gain map 274 that receives γ_diff. As shown in FIG. 11, if γ_diff is within a predetermined threshold range (TH1_P and TH1_M), it is determined that the reliability of the yaw rate model value γ_mdl calculated from the vehicle model is high, and κ = MAX_GAIN (for example, MAX_GAIN). = 1). If | γ_diff | is larger than a predetermined threshold (TH2_P or TH2_M), it is determined that the reliability of the yaw rate model value γ_mdl calculated from the vehicle model is low, and κ = 0. In the range of TH1_P <γ_diff <TH2_P or TH2_M <γ_diff <TH1_M, κ is linearly interpolated between 0 and 1 according to the degree of divergence between γ_mdl and γ (model reliability). As described above, it is estimated that the higher the gain (absolute value) of the yaw rate deviation γ_diff, the lower the reliability of the yaw rate model value γ_mdl and the worse the road surface condition.

  The second drive torque correction coefficient calculation unit 250 calculates the second drive torque correction coefficient Coef2 from the product of the gain τ and the gain κ (Coef2 = κ × τ). Here, since the gain τ and the gain κ are gains obtained from current information around the host vehicle, the second drive torque correction coefficient Coef2 is a correction coefficient based on the current traveling state of the vehicle.

As described above, the second drive torque calculation unit 284 determines the current around the vehicle in the left / right drive torque distribution determined by the turning situation of the vehicle separately from the front / rear drive torque determined by the vehicle speed V, the accelerator opening, and the like. The second drive torque MotTrq2 obtained from the specific information is calculated by the product of the second drive torque correction coefficient Coef2 (for each wheel) and the drive target torque MotTrqTgt (for each wheel). That is, the second drive torque MotTrq2 is calculated from the following equation (7).
MotTrq2 (#) = MotTrqTgt (#) × Coef2 (#) (7)

  Since the drive target torque MotTrqTgt is obtained separately from the front / rear drive torque determined by the vehicle speed, the accelerator opening, etc., the drive target torque MotTrqTgt does not include the front / rear drive torque. Therefore, by multiplying the left and right wheel drive target torque MotTrqTgt by the second drive torque correction coefficient Coef2, the second drive torque correction is performed particularly when the road surface condition is bad or the reliability of the yaw rate model value γ_mdl is low. When the value of the coefficient Coef2 is lowered, the difference in driving force between the left and right wheels is suppressed. Thereby, a vehicle behavior can be stabilized.

  The torque correction coefficient calculation unit 286 calculates a correction coefficient TrqCoef for calculating the drive request torque MotTrqReq by weighting the first drive torque MotTrq1 and the second drive torque MotTrq2. The torque correction coefficient calculation unit 286 calculates a correction coefficient TrqCoef for weighting the first drive torque MotTrq1 and the second drive torque MotTrq2 in accordance with the vehicle speed V, the yaw rate deviation γ_diff, and the road surface condition (Load Condition).

  FIG. 12 is a characteristic diagram showing a gain map 276 for the second drive torque correction coefficient calculation unit 250 to calculate a gain (GainV) corresponding to the vehicle speed V. As shown in FIG. 12, the correction gain (GainV) due to the vehicle speed V varies between the maximum value MAX_GAIN and the minimum value MIN_GAIN, and the value decreases as the vehicle speed V increases.

  FIG. 13 is a characteristic diagram showing a gain map 278 for the second drive torque correction coefficient calculation unit 250 to calculate a gain (Gain γ_diff) corresponding to the yaw rate deviation γ_diff. As shown in FIG. 13, the correction gain (Gain γ_diff) based on the yaw rate deviation γ_diff decreases as the absolute value of γ_diff increases.

  FIG. 14 is a characteristic diagram showing a gain map 279 for calculating a gain (GainLoad Condition) according to the road surface condition detected by the external field recognition unit 220. As shown in FIG. 14, the value of the correction gain (GainLoad Condition) corresponding to the road surface condition increases when the road surface condition becomes good.

The torque correction coefficient calculator 286 calculates a torque correction coefficient TrqCoef based on the product of GainV, Gainγ_diff, and GainLoadCondition. That is, the torque correction coefficient TrqCoef is calculated from the following equation (8).
TrqCoef = GainV × Gainγ_diff × GainLoadCondition
... (8)

The drive request torque correction processing unit 288 weights the first drive torque MotTrq1 and the second drive torque MotTrq2 based on the drive torque correction coefficient TrqCoef to calculate the drive request torque MotTrqReq. The drive request torque MotTrqReq is calculated from the following equation (9).
MotTrqReq (#) = TrqCoef × MotTrq1 (#) + (1-TrqCoef) × MotTrq2 (#) (9)

  When the vehicle speed V is high, the driving environment around the host vehicle, such as a situation where the yaw rate deviation γ_diff is large (a situation where the difference between the state quantity of the vehicle model and the actual vehicle behavior is large), a road surface condition detected by the stereo camera is bad When unstable, the value of the drive torque correction coefficient TrqCoef becomes small. In this case, the vehicle behavior can be stabilized by increasing the weight of the second drive torque MotTrq2 that is controlled based on the current information around the host vehicle according to the above equation. Further, when the traveling environment around the host vehicle is stable, the value of the drive torque correction coefficient TrqCoef is increased, and therefore the weight of the first drive torque MotTrq1 calculated based on future information can be increased.

  Accordingly, when the vehicle 1000 slides on a low μ road surface, the vehicle model may become unstable on the travel route when the difference between the state quantity of the vehicle model and the vehicle behavior is large, or by the external recognition unit 220. Second driving torque calculated based on current information, compared to situations in which signs of bad weather are detected from future information when the driving environment around the vehicle is unstable, such as when a certain road surface is detected By increasing the distribution of MotTrq2, it is possible to quickly suppress the left and right driving force difference with respect to a predetermined operation amount related to vehicle turning.

  In addition, when the process of the second drive torque MotTrq2 is performed with priority over the process of the second drive torque MotTrq2, in a situation where the current information around the host vehicle is prioritized, the situation determined by the yaw rate deviation γ_diff If the situation judged by environmental information around the vehicle such as a stereo camera or raindrop sensor is different and the vehicle enters an unstable road surface such as a puddle or snowy road surface, the road surface state detected by the external recognition unit 220 Based on the above, a drive torque correction coefficient TrqCoef for increasing the distribution of the second drive torque MotTrq2 is calculated.

  On the other hand, when escaping from an unstable road surface such as a puddle or a snowy road surface, a drive torque correction coefficient TrqCoef for increasing the distribution of the second drive torque MotTrq2 is calculated based on the vehicle behavior (yaw rate deviation γ_diff, etc.). Furthermore, in a situation where priority is given to future information, it also has a function of increasing the distribution of the first drive torque MotTrq1.

  If the stereo camera detects the amount of water that is expected to cause the hydroplaning phenomenon, the difference between the left and right torque distribution drive target torque is quickly shifted to 0 by the second drive torque correction coefficient Coef2 based on the current situation ( Gradually change in the direction of zero left / right difference). In the transition region until the occurrence of the hydroplaning phenomenon is detected, the left / right torque distribution (target value) is corrected by weighting the first drive torque MotTrq1 and the second drive torque MotTrq2.

  Note that the first correction coefficient and the second correction coefficient may be weighted according to the degree of deviation between the vehicle model and the actual vehicle behavior (for example, yaw rate deviation γ_diff). The weight of the second drive torque correction coefficient Coef2 that quickly corrects the target value) to 0 may be increased.

  As described above, in the present embodiment, the drive request torque MotTrqReq calculated by the drive request torque calculator 288 is the torque of the left and right drive force distribution in order to control the torque of the left and right drive force distribution (torque in the turning direction). On the other hand, based on the accelerator opening, the wheel speeds VwFL, VwFR, VwRL, VwRR, etc., the driving torque for the front-rear driving force distribution can be obtained separately. The driving target torque for the front / rear driving force distribution and the driving target torque for the left / right driving force distribution are added for each wheel to determine the driving force distribution for each wheel. As described above, the final driving request torque for the motor is calculated by adding the driving target torque in the front-rear direction determined by the accelerator operation and the driving torque determined according to the turning state of the vehicle 1000 for each wheel. The driving force distribution for each wheel is determined.

5. About each parameter controlled by processing of this embodiment Drawing 15-Drawing 17 are mimetic diagrams showing each parameter controlled by processing of this embodiment. FIG. 15 shows the vehicle speed V, the steering angle θ _H , the gain ζ, the left-right difference (ΔMotTrqTgt) of the drive target torque MotTrqTgt, and the yaw rate γ.

  In FIG. 15, the characteristic indicated by the broken line indicates a case where the left-right difference of the drive target torque is controlled based on the future environmental information by the process according to the present embodiment, and the characteristic indicated by the solid line performs the process according to the present embodiment. Shows no case. Specifically, in the processing according to the present embodiment, the gain ζ is reduced. As shown in FIG. 15, when the process according to the present embodiment is performed, it can be seen that the left-right difference in the drive target torque is reduced and the yaw rate is reduced. Therefore, by reducing the gain ζ based on the environmental information of the future predicted arrival point, it is possible to reliably prevent the vehicle 1000 from turning too much when the road surface condition deteriorates at the predicted arrival point.

FIG. 16 shows the vehicle speed V, the steering angle θ _H , the gain κ, the left / right difference (ΔMotTrqTgt) of the drive target torque MotTrqTgt, and the yaw rate γ.

  In FIG. 16, the broken line characteristics indicate the case where the left-right difference of the drive target torque is controlled based on the current traveling state by the processing according to the present embodiment, and the solid line characteristics do not perform the processing according to the present embodiment. Shows the case. Specifically, in the process according to the present embodiment, the gain κ is reduced. As shown in FIG. 16, when the process according to the present embodiment is performed, it can be seen that the left-right difference in the drive target torque decreases and the yaw rate decreases. Therefore, by reducing the gain κ based on the current travel status report, it is possible to reliably prevent the vehicle 1000 from turning too much when the current road surface condition is bad.

In FIG. 17, future environmental information and the current traveling state are controlled in combination, and the vehicle speed V, the steering angle θ _H , the gain ζ, the gain κ, the drive torque correction coefficient TrqCoef, and the first drive torque are controlled. Left-right difference of MotTrq1 (ΔMotTrq1 (solid line)), left-right difference of second drive torque MotTrq2 (ΔMotTrq2 (thin broken line)), left-right difference of drive request torque MotTrqReq (ΔMotTrqReq (thick broken line)), left-right difference of drive target torque (ΔMotTrqTgt) (Solid line), the left-right difference (ΔMotTrqReq (broken line)) of the drive request torque MotTrqReq, and the yaw rate γ, respectively.The value of the yaw rate γ indicates that the control according to the present embodiment is not performed, and the broken line indicates The case where the control by this embodiment is performed is shown.

  As shown in FIG. 17, the drive torque correction coefficient TrqCoef decreases according to the gain ζ based on the future environmental information and the gain κ based on the current travel situation, so that the left-right difference (ΔMotTrqReq) of the drive request torque MotTrqReq is It turns out that it has fallen. It can also be seen that the yaw rate γ decreases when the control according to the present embodiment is performed. Therefore, it is possible to reliably prevent the vehicle 1000 from turning too much.

  As described above, according to the present embodiment, the first drive torque MotTrq1 applied in the turning direction of the vehicle is calculated based on the future environmental information of the vehicle, and the second drive is performed based on the current traveling state of the vehicle. The torque MotTrq2 is calculated, and the first driving torque and the second driving torque are weighted based on the current traveling state of the vehicle. This makes it possible to optimally control the drive torque based on future environmental information, and optimally control the drive torque based on the current driving situation when there is a situation such as poor road surface conditions in the current driving situation. It becomes possible to do.

  The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to such examples. It is obvious that a person having ordinary knowledge in the technical field to which the present invention pertains can come up with various changes or modifications within the scope of the technical idea described in the claims. Of course, it is understood that these also belong to the technical scope of the present invention.

200 Control Device 230 Drive Target Torque Calculation Unit 282 First Drive Torque Calculation Unit 284 Second Drive Torque Calculation Unit 288 Drive Required Torque Calculation Unit

Claims (12)

A first drive torque calculator that calculates a first drive torque to be applied in the turning direction of the vehicle based on future environmental information of the vehicle;
A second drive torque calculating unit that calculates a second drive torque to be applied in the turning direction of the vehicle based on the current traveling state of the vehicle;
A drive request torque calculator that calculates a drive request torque to be applied in the turning direction of the vehicle by weighting the first drive torque and the second drive torque based on the current traveling state of the vehicle;
A vehicle control device comprising:   2. The vehicle control device according to claim 1, wherein the first drive torque calculation unit calculates the first drive torque based on a road surface condition of a predicted arrival point in the future. The first drive torque is applied to the turning direction of the vehicle as a couple to the left and right wheels,
3. The vehicle control device according to claim 1, wherein the first driving torque calculation unit reduces a driving force difference between left and right wheels by decreasing the first driving torque. 4.   The vehicle according to any one of claims 1 to 3, wherein the first drive torque calculation unit calculates the first drive torque based on an elapsed time after detecting a sign of bad weather. Control device.   5. The vehicle control device according to claim 4, wherein the first drive torque calculation unit decreases the first drive torque as the elapsed time is longer. 6. The second driving torque is applied to the turning direction of the vehicle as a couple to the left and right wheels,
6. The vehicle control device according to claim 1, wherein the second driving torque calculation unit reduces a driving force difference between left and right wheels by reducing the second driving torque. 7. .   The said 2nd drive torque calculating part reduces the said 2nd drive torque, so that the present road surface condition is bad based on the image information obtained by imaging a road surface with a camera, The said 1st drive torque calculating part reduces the said 2nd drive torque. The vehicle control device according to claim 6.   The second driving torque calculation unit reduces the second driving torque as the difference increases based on a difference between a yaw rate model value calculated from a vehicle model and an actual yaw rate detected by a yaw rate sensor. The vehicle control device according to any one of claims 1 to 7.   The drive request torque calculation unit increases the weight of the second drive torque with respect to the first drive torque as the current road surface condition is worse, based on image information obtained by imaging the road surface with a camera. The vehicle control device according to claim 1.   10. The vehicle control device according to claim 1, wherein the drive request torque calculation unit increases the weight of the second drive torque with respect to the first drive torque as the vehicle speed increases. .   The drive request torque calculation unit weights the second drive torque with respect to the first drive torque as the difference is larger based on a difference between a yaw rate model value calculated from a vehicle model and an actual yaw rate detected by a yaw rate sensor. The vehicle control device according to claim 1, wherein the vehicle control device is made high. Calculating a first driving torque to be applied to the turning direction of the vehicle based on future environmental information of the vehicle;
Calculating a second drive torque to be applied in the turning direction of the vehicle based on the current traveling state of the vehicle;
Calculating a drive request torque to be applied in the turning direction of the vehicle by weighting the first drive torque and the second drive torque based on the current traveling state of the vehicle;
A vehicle control method comprising:

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