JP2005247098A - Driving operation auxiliary unit for vehicle, and vehicle having the same - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a driving operation auxiliary unit for a vehicle capable of controlling the operational reaction force without sense of incongruity by learning the individual difference of a driver. <P>SOLUTION: The driving operation auxiliary unit for the vehicle calculates the risk potential around a self vehicle, and controls the operational reaction force of an accelerator pedal according to the risk potential. Since individual difference is include in the risk feeling, the reaction of a driver is measured in a series of operations in which a self vehicle approaches a preceding vehicle, the accelerator pedal is stepped off, and a brake pedal is stepped on, and the risk potential is learned corresponding to the reaction of the driver. The relationship between the risk potential and the operational reaction force of the accelerator pedal is corrected based on the learned risk potential data. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a driving operation assisting device for a vehicle that assists a driver's operation.

  Conventional vehicle driving assist devices change the operation reaction force of an accelerator pedal according to the risk potential based on the driving environment around the host vehicle (see, for example, Patent Document 1). This device calculates the risk potential based on the relative relationship between the host vehicle and the preceding vehicle, that is, the inter-vehicle distance, the relative speed, and the like.

Prior art documents related to the present invention include the following.
JP 2003-246225 A

  The conventional device can transmit the risk potential based on the approaching state to the preceding vehicle to the driver as an accelerator pedal reaction force. However, since the risk sensation for a certain approach state varies from person to person, it is desired to perform the accelerator pedal reaction force control that matches the individual driver's risk sensation by learning individual differences among drivers.

  The vehicle driving operation assistance device according to the present invention includes a situation recognition unit that detects a traveling situation around the host vehicle, a risk potential calculation unit that calculates a risk potential around the host vehicle based on a detection result of the situation recognition unit, Based on the risk potential calculated by the risk potential calculation means, an operation reaction force control means for controlling an operation reaction force generated in the driving operation device, and a reaction learning means for learning a vehicle state during a predetermined reaction action of the driver; And a correcting means for correcting the risk potential formula in the risk potential calculating means based on the vehicle state learned by the reaction learning means.

  By learning the driver's individual differences and correcting the risk potential equation, it is possible to perform an operation reaction force control that matches the individual's risk sense.

<< First Embodiment >>
FIG. 1 is a system diagram showing a configuration of a vehicle driving assistance device 1 according to the first embodiment of the present invention, and FIG. 2 is a configuration diagram of a vehicle on which the vehicle driving assistance device 1 is mounted. .

  First, the configuration of the vehicle driving assistance device 1 will be described. The laser radar 10 is attached to a front grill part or a bumper part of the vehicle, and scans the front area of the host vehicle by irradiating infrared light pulses in the horizontal direction. The laser radar 10 measures the reflected wave of the infrared light pulse reflected by a plurality of reflectors in front (usually the rear end of the preceding vehicle), and determines the inter-vehicle distance to the preceding vehicle from the arrival time of the reflected wave. Detect relative speed. The detected inter-vehicle distance and relative speed are output to the controller 50. The forward area scanned by the laser radar 10 is about ± 6 deg with respect to the front of the host vehicle, and a forward object existing within this range is detected.

  The vehicle speed sensor 30 detects the vehicle speed of the host vehicle by measuring the number of rotations of the wheels and the number of rotations on the output side of the transmission, and outputs the detected host vehicle speed to the controller 50.

  The controller 50 also receives a signal from an accelerator off switch 84 that detects the operating state of the accelerator pedal 82 and a signal from the brake on switch 94 that detects the operating state of the brake pedal 92.

  The controller 50 includes a CPU and CPU peripheral components such as a ROM and a RAM, and performs overall control of the vehicle driving assistance device 1. The controller 50 calculates the risk potential around the host vehicle from signals such as the host vehicle speed, the inter-vehicle distance, and the relative speed input from the vehicle speed sensor 30 and the laser radar 10. Then, the reaction force control of the accelerator pedal 82 is performed based on the calculated risk potential.

  The accelerator pedal reaction force control device 80 controls the accelerator pedal operation reaction force according to a command value from the controller 50. As shown in FIG. 3, the accelerator pedal 80 is connected to a servo motor 81 and an accelerator pedal stroke sensor 83 via a link mechanism. The servo motor 81 controls the torque and the rotation angle according to a command from the accelerator pedal reaction force control device 80, and arbitrarily controls the operation reaction force generated when the driver operates the accelerator pedal 82. The accelerator pedal stroke sensor 83 detects the operation amount of the accelerator pedal 82 converted into the rotation angle of the servo motor 81 through the link mechanism.

  Note that the normal accelerator pedal reaction force characteristic when the accelerator pedal reaction force control is not performed is set, for example, such that the accelerator pedal reaction force increases linearly as the accelerator pedal operation amount increases. The normal accelerator pedal reaction force characteristic can be realized by the spring force of a torsion spring (not shown) provided at the center of rotation of the accelerator pedal 82, for example.

Next, the operation of the vehicle driving assistance device 1 according to the first embodiment of the present invention will be described. First, the outline will be described below.
By performing the accelerator pedal reaction force control according to the risk potential around the host vehicle, the approaching state to the preceding vehicle can be transmitted to the driver as the operation reaction force of the accelerator pedal 82. However, since the risk sensation for the same risk potential, that is, the approaching state to the preceding vehicle, varies depending on the person, even if the same accelerator pedal reaction force is generated for the same risk potential, depending on the driver, There may be a sense of incongruity with the timing of occurrence.

  Therefore, in the first embodiment, the driver's individual difference (fake) is learned to correct the control parameter for the accelerator pedal operation reaction force. Specifically, a driver's predetermined reaction and a predetermined vehicle state are measured, and risk potential data used for learning is selected using these measurement results. Then, based on the selected learning data, the characteristics of the accelerator pedal reaction force with respect to the risk potential are corrected to realize the accelerator pedal reaction force control without any sense of incongruity.

  Below, operation | movement of the driving operation assistance apparatus 1 for vehicles by 1st Embodiment is demonstrated in detail using FIG. FIG. 4 is a flowchart showing a processing procedure of the driving operation assistance control program in the controller 50. This processing content is continuously performed at regular intervals (for example, 50 msec).

  In step S101, the host vehicle and the running conditions around the vehicle are read. Specifically, the host vehicle speed v1, the preceding vehicle speed v2, the inter-vehicle distance d between the host vehicle and the preceding vehicle, and the relative speed vr (vr = v2-v1) detected by the laser radar 10 and the vehicle speed sensor 30 are read. Further, signals from the accelerator off switch 84 and the brake on switch 94 are also read.

In step S102, a margin time TTC and an inter-vehicle time THW for the preceding vehicle are calculated. The margin time TTC is a physical quantity indicating the current degree of proximity of the host vehicle with respect to the preceding vehicle. In the allowance time TTC, when the current traveling state continues, that is, when the own vehicle speed v1, the preceding vehicle speed v2, and the relative vehicle speed vr are constant, the inter-vehicle distance d becomes zero and the own vehicle and the preceding vehicle come into contact with each other. It is a value indicating The margin time TTC is obtained by the following (Equation 1).
TTC = −d / vr (Formula 1)

  The smaller the margin time TTC value, the closer the contact with the preceding vehicle, and the greater the degree of approach to the preceding vehicle. For example, when approaching a preceding vehicle, it is known that most drivers start a deceleration action before the margin time TTC becomes 4 seconds or less.

The inter-vehicle time THW is an effect when it is assumed that the degree of influence on the margin time TTC due to a change in the vehicle speed of the assumed vehicle ahead, that is, the relative vehicle speed vr changes when the host vehicle follows the preceding vehicle. It is a physical quantity indicating the degree. The inter-vehicle time THW is expressed by the following (Formula 2).
THW = d / v1 (Formula 2)

  The inter-vehicle time THW is obtained by dividing the inter-vehicle distance d by the own vehicle speed v1, and represents the time until the own vehicle reaches the current position of the preceding vehicle. The greater the inter-vehicle time THW, the smaller the predicted influence level with respect to the surrounding environmental changes. That is, when the inter-vehicle time THW is large, even if the vehicle speed of the preceding vehicle changes in the future, the degree of approach to the preceding vehicle is not greatly affected, and the margin time TTC does not change so much. When the own vehicle follows the preceding vehicle and the own vehicle speed v1 = the preceding vehicle speed v2, the inter-vehicle time THW can be calculated using the preceding vehicle speed v2 instead of the own vehicle speed v1 in (Equation 2).

In step S103, the risk potential RP around the host vehicle is calculated using the inter-vehicle time THW and the margin time TTC calculated in step S102. The risk potential RP around the host vehicle can be calculated by the following (Formula 3).
RP = a / THW + b / TTC (Formula 3)
Here, a and b are constants for appropriately weighting the inter-vehicle time THW and the margin time TTC, and appropriate values are set in advance. The constants a and b are set to, for example, a = 1 and b = 8 (a <b).

  In step S104, it is determined whether or not a learning condition for learning the risk potential RP is satisfied. Specifically, based on the relative speed vr between the host vehicle and the preceding vehicle detected in step S101 and signals from the accelerator pedal off switch 84 and the brake pedal on switch 94, “approach to the preceding vehicle → accelerator off → It is determined whether or not a series of operations “brake on” has been completed. Here, for example, it is assumed that the vehicle is approaching the preceding vehicle when a relative speed vr greater than or equal to a predetermined value is generated.

  If it is determined in step S104 that a series of operations as learning conditions has been completed, the process proceeds to the processing in step S105 and subsequent steps, and the risk potential is based on the running state recorded in the past several tens of seconds until the series of operations is completed. Learn RP. Here, we learn the driver's individual difference (decision) such as how much risk the driver feels in the above series of operations, that is, how much risk potential RP occurs when driving operation such as accelerator off / brake on is performed. This is called learning of the risk potential RP.

  As shown in FIG. 5, when the host vehicle approaches the preceding vehicle, the risk potential RP calculated when the accelerator pedal 82 is turned off (released) and then the brake pedal 92 is turned on (depressed). The risk potential RP calculated sometimes is taken as data to be learned. FIG. 5 is a risk potential map in which the horizontal axis represents the inter-vehicle time THW, and the vertical axis represents the reciprocal of the margin time TTC. The upper right portion of the map indicates a region where the risk potential RP is high. The solid line in the map is an equirisk potential line indicating that the risk potential RP is equal.

  First, in order to learn the risk potential RP when the accelerator pedal is turned off, the driver's reaction speed in a predetermined driving action is calculated in step S105. Here, the step change time RT from when the driver turns off the accelerator pedal 82 to when the brake pedal 92 is turned on is detected, and the reciprocal of the step change time RT is determined as the emergency response of the driver in the driving action of pedal change. It is calculated as degree (Response Urgency) RU1. The degree of urgency RU1 increases as the stepping time RT is short, that is, as the brake pedal 92 is turned on sooner after the accelerator pedal 82 is turned off, and becomes shorter as the stepping time RT is longer, that is, as the pedal is slowly changed.

  In the risk potential map shown in FIG. 6A, the size of ○ indicates the level of urgency RU1 based on the pedal switching time RT, and the position of ○ indicates the risk potential when the driver releases the accelerator pedal 82. RP is shown.

  In a succeeding step S106, it is determined whether or not the urgency level RU1 calculated in the step S105 is larger than a predetermined value RU10. When RU1> RU10, the process proceeds to step S107, and the risk potential RP when the accelerator pedal is off, which is recorded in the memory of the controller 50, is learned. When RU1 ≦ RU10, the risk potential RP is not learned. As shown in FIG. 6B, the controller 50 learns the risk potential RP using a plurality of data when the urgency level RU1 exceeds the predetermined value RU10.

  FIG. 8 shows the frequency distribution of the risk potential RP when the accelerator pedal is off when RU1> RU10. As shown in FIG. 8, since the risk potential RP is used only when the urgency RU1 exceeds the predetermined value RU10, the peak (mode) of the risk potential RP is compared to the case where the learning target data indicated by the dotted line is not selected. ) Is shifting upward. The mode value of the risk potential RP when the accelerator pedal is off shown in FIG. 8 is the learned risk potential RPa when the accelerator pedal is off. The risk potential learning value RPa represents a driver's risk sensation or a habit of driving operation that, when the risk potential RP increases to this value, the rate at which the driver turns off the accelerator pedal 82 is high.

  Further, in order to learn the risk potential RP when the brake pedal is on, in step S108, the driver's reaction speed in a predetermined driving action is calculated. Here, the deceleration rate after the driver turns on the brake pedal 92 is calculated as the driver's response Urgency RU2 in the driving action of deceleration after the pedal is switched. The degree of urgency RU2 indicates the maximum value of the deceleration of the host vehicle within a predetermined time (for example, 5 seconds) or the change in speed within the predetermined time (for example, 5 seconds) after the brake pedal 92 is turned on after the accelerator pedal is turned off. Dimensionless and calculated. When a speed change within a predetermined time is used as the urgency level RU2, the urgency level RU2 is increased as the amount of decrease in the vehicle speed within the predetermined time increases, and the urgency level RU2 is maximized when the amount of decrease in the vehicle speed exceeds a predetermined value. Fixed to a value. The degree of urgency RU2 increases as the degree of deceleration after the brake pedal 92 is turned on increases.

  In the risk potential map shown in FIG. 7, the size of ○ indicates the magnitude of the urgency RU2 based on the degree of deceleration after the brake is turned on, and the position of ○ indicates the risk potential RP when the driver turns on the brake pedal 92. ing.

  In a succeeding step S109, it is determined whether or not the urgency level RU2 calculated in the step S108 is larger than a predetermined value RU20. When RU2> RU20, the process proceeds to step S110, and the risk potential RP when the brake pedal is turned on, which is recorded in the memory of the controller 50, is learned. When RU2 ≦ RU20, the risk potential RP is not learned. As shown in FIG. 7, the controller 50 learns the risk potential RP using a plurality of data when the urgency level RU2 exceeds a predetermined value RU20.

  FIG. 8 further shows the frequency distribution of the risk potential RP when the brake pedal is off when RU2> RU20. As shown in FIG. 8, since the risk potential RP is used only when the urgency RU2 exceeds the predetermined value RU20, the peak of the risk potential RP (mode value) is compared with the case where the learning target data indicated by the dotted line is not selected. ) Is shifting upward. The mode value of the risk potential RP when the brake pedal is off shown in FIG. 8 is the learned risk potential RPb when the brake pedal is on. The risk potential learning value RPb represents a driver's risk sensation or a habit of driving action that when the risk potential RP increases to this value, the driver switches to the brake pedal 92 at a high rate.

  In step S111, the risk potential RP and accelerator when performing the operation reaction force control using the risk potential RPa when the accelerator pedal is turned off learned in step S107 and the risk potential RPb when the brake pedal is turned on learned in step S110. The relationship (RP-ΔF characteristic) with the pedal reaction force increase amount ΔF is corrected. Hereinafter, correction of RP-ΔF will be described.

  FIG. 9 shows an initial RP-ΔF characteristic before correction using learning data. In FIG. 9, the reaction force ΔFa corresponding to the risk potential RPa when the accelerator pedal is off and the reaction force ΔFb corresponding to the risk potential RPb when the brake pedal is on are predetermined values that are set appropriately in advance. The RP-ΔF characteristic is set so that the reaction force increase amount ΔF increases as the risk potential RP increases as it passes through the reaction forces ΔFa and ΔFb.

  Since there is an individual difference between the risk potential RPa when the accelerator pedal is off and the risk potential RPb when the brake pedal is on, the RP-ΔF characteristic is corrected as shown in FIG. 10 based on the learned data. As a result, a constant reaction force ΔFa is generated with respect to the learned risk potential RPa when the accelerator pedal is turned off, and a constant reaction force ΔFb is generated with respect to the learned risk potential RPb when the brake pedal is turned on. That is, although the risk potentials RPa and RPb when the accelerator is turned on / brake off differ depending on the individual driver, the reaction forces ΔFa and ΔFb generated for these values do not change by correcting the RP-ΔF characteristics. .

  In subsequent step S112, the accelerator pedal reaction force increase amount ΔF is calculated based on the risk potential RP calculated in step S103. If the learning condition is satisfied in step S104, the RP-ΔF characteristic corrected in step S111 is used. If the learning condition is not satisfied, the RP-ΔF characteristic set in the previous cycle or as shown in FIG. The reaction force increase amount ΔF is calculated using the initial characteristics.

  In step S113, the reaction force increase amount ΔF calculated in step S112 is output to the accelerator pedal reaction force control device 80. In response to a command from the controller 50, the accelerator pedal reaction force control device 80 generates an accelerator pedal reaction force by adding a reaction force increase amount ΔF to a normal reaction force characteristic corresponding to an accelerator pedal operation amount. 81 is controlled. Thus, the current process is terminated.

Thus, in the first embodiment described above, the following operational effects can be achieved.
(1) The controller 50 calculates the risk potential RP based on the traveling situation around the host vehicle, and performs the operation reaction force control according to the risk potential RP. Further, when the driver performs a predetermined driving operation in response to his / her own risk sense, that is, the vehicle state at the time of the predetermined reaction operation is learned, and the risk potential equation is corrected based on the learned vehicle state. Specifically, the driver's individual difference is learned by learning the vehicle state at the time of the driver's predetermined reaction operation, and the RP-ΔF characteristic as shown in FIG. 9 is corrected. Thereby, it is possible to perform the operation reaction force control that matches the individual's risk sense.
(2) The controller 50 learns the risk potential PR as the vehicle state at the time of the driver's predetermined reaction operation. Then, the risk potential equation is corrected so that the risk potential RP calculated based on the traveling situation becomes smaller as the learned risk potential RP at the time of the reaction operation is larger. That is, for a driver whose risk potential RP during a predetermined reaction operation always tends to be large, the risk potential RP is corrected so as to decrease. Specifically, as the risk potentials RPa and RPb during the reaction operation are larger, the RP-ΔF characteristic is corrected in a direction in which the reaction force increase amount ΔF with respect to the risk potential RP decreases as shown in FIG. Thereby, the same operation reaction force can be generated at the time of a predetermined reaction operation according to an individual's risk sense.
(3) The controller 50 selects the vehicle state as the learning target data, that is, the risk potential RP, based on the reaction of the driver regarding the reaction operation or the behavior of the host vehicle. Even when the same reaction operation is performed, the response of the driver or the behavior of the host vehicle differs depending on each case. Therefore, by selecting the learning target data based on the driver's reaction or the behavior of the host vehicle, it is possible to accurately learn the individual differences of the driver.
(4) The controller 50 learns the risk potential RPa when the accelerator pedal is released as the vehicle state during a predetermined reaction operation. At this time, a change-over time RT from when the driver releases the accelerator pedal 82 until the driver depresses the brake pedal 92 is used as the driver's reaction or the behavior of the host vehicle. The step change time RT is one of the factors representing the driver's risk sensation, and by selecting the learning target data using the step change time RT, it is possible to learn individual differences of the driver with high accuracy.
(5) Since the risk potential RP when the stepping time RT is smaller than the predetermined value, that is, when the urgency RU1 (RU1 = 1 / RT) is larger than the predetermined value RU10 is selected as the learning target data, Variations can be reduced and individual differences among drivers can be learned accurately.
(6) The controller 50 learns the risk potential RPb when the brake pedal 92 is depressed after the accelerator pedal 82 is released as the vehicle state during a predetermined reaction operation. At this time, as the driver's reaction or the behavior of the host vehicle, the deceleration of the host vehicle caused by the depression of the brake pedal 92 is used. The degree of deceleration of the host vehicle is one of the factors representing the driver's risk sensation. By selecting data to be learned using the degree of deceleration, individual differences among drivers can be learned with high accuracy.
(5) Since the risk potential RP when the urgency level RU2 based on the deceleration rate is larger than the predetermined value RU20 is selected as the data to be learned, it is possible to reduce the variation in the learning data and accurately learn the individual differences of the drivers. Can do.

<< Second Embodiment >>
Below, the driving operation assistance device for a vehicle according to the second embodiment of the present invention will be described. The configuration of the vehicular driving assistance device according to the second embodiment is the same as that of the first embodiment shown in FIGS. Here, differences from the above-described first embodiment will be mainly described.

  In the second embodiment, when the learning condition is satisfied, the risk potential RPa when the accelerator pedal is off and the risk potential RPb when the brake pedal is on are learned without selecting data to be learned. Specifically, the risk potential RP calculated when the accelerator pedal is off and the risk potential RP calculated when the brake pedal is on are stored in association with the urgency levels RU1 and RU2, respectively, and the stored risk potential data is corrected. Risk potential learning values RPa and RPb are calculated.

  Below, operation | movement of the driving assistance device for vehicles by 2nd Embodiment is demonstrated in detail using FIG. FIG. 11 is a flowchart showing the processing procedure of the driving operation assistance control program in the controller 50. This processing content is continuously performed at regular intervals (for example, 50 msec). The processing in steps S201 to S204 is the same as the processing in steps S101 to S104 in the flowchart shown in FIG.

  In step S205, as in the first embodiment, the driver response urgency RU1 based on the pedal change time RT and the driver response urgency RU2 based on the deceleration after the pedal change are calculated.

  In step S206, the risk potential RPa when the accelerator pedal is off and the brake pedal on based on the emergency level RU1 and the risk potential RP when the accelerator pedal is off and the risk potential RP when the emergency level RU2 and the brake pedal are on. Learn the time risk potential RPb. Hereinafter, learning of the risk potential RP will be described.

  FIG. 12 shows that the risk potential RP (Δ) when the accelerator pedal is off with respect to the emergency level RU1 and the risk potential RP (◯) when the brake pedal is on with respect to the emergency level RU2 when the series of operations described above are performed a plurality of times. The distribution of. As shown in FIG. 12, the risk potential RP when the accelerator pedal is turned off and the risk potential RP when the brake pedal is turned on are higher as the urgency levels RU1 and RU2 are higher.

  As shown in FIG. 12, the urgency levels RU1 and RU2 increase as the risk potential RP increases. The driver depresses the brake pedal 92 earlier as the timing of turning off the accelerator pedal 82 becomes slower, and the timing at which the brake pedal 92 is turned on. This is because there is a tendency to decelerate more strongly as the speed gets slower. Therefore, it can be estimated that the driver intends to turn off the accelerator pedal 82 when the urgency level RU1 is 0 and to turn on the brake pedal 92 when the urgency level RU2 is 0.

  Therefore, the risk potential RP when the urgency level RU1 is 0 is set as the risk potential learning value RPa when the accelerator pedal is off, and the risk potential RP when the urgency level RU2 is 0 is set as the risk potential learning value RPb when the brake pedal is turned on. Estimate as Specifically, the approximate expression of the relationship between the risk potential RP when the accelerator pedal is off and the urgency RU1 and the approximate expression of the relationship between the risk potential RP and the urgency RU2 when the brake pedal is on are calculated by a method such as the least square method. To do. And the intersection of each approximate expression and the horizontal axis of FIG. 12 is calculated as the risk potential learning values RPa and RPb.

  In step S207, as in the first embodiment, a predetermined reaction force ΔFa is generated with respect to the learned risk potential RPa when the accelerator pedal is off, and a predetermined reaction force ΔFa is generated with respect to the learned risk potential RPb when the brake pedal is turned on. The RP-ΔF characteristic is corrected so that the reaction force ΔFb is generated.

  Henceforth, since the process in step S208 and S209 is the same as the process in step S112 and S113 of FIG. 4, description is abbreviate | omitted.

Thus, in the second embodiment described above, the following operational effects can be obtained in addition to the effects of the first embodiment described above.
(1) The controller 50 corrects the vehicle state to be learned based on the driver's reaction for a predetermined reaction operation or the behavior of the host vehicle. Specifically, the risk potential learning values RPa and RPb are calculated (estimated) by correcting the risk potential data obtained during a predetermined reaction operation as shown in FIG. Thereby, the individual difference of a driver can be learned accurately.
(2) The controller 50 corrects the risk potential data obtained when the accelerator pedal is released in accordance with the urgency RU1 based on the switching time RT from when the accelerator pedal 82 is released until the brake pedal 92 is depressed, Learns the risk potential RPa when the accelerator pedal is released. Thereby, the individual difference regarding the driver's risk sensation when releasing the accelerator pedal 82 can be learned with high accuracy.
(3) After releasing the accelerator pedal 82, the controller 50 corrects the risk potential data obtained when the brake pedal is depressed in accordance with the urgency RU2 based on the degree of deceleration of the host vehicle when the brake pedal 92 is depressed. Learn the risk potential RPb when the driver depresses the brake pedal. Thereby, the individual difference regarding the driver's sense of risk when the brake pedal 92 is depressed can be learned with high accuracy.

  In the first or second embodiment described above, the RP-ΔF characteristic is changed according to the learned individual difference. However, the present invention is not limited to this, and it is also possible to correct the relationship between the driving situation around the host vehicle and the risk potential RP as a correction of the risk potential formula based on the learned vehicle state.

  In the first and second embodiments, the deceleration or speed change of the host vehicle is used as the emergency degree RU2 regarding the brake pedal on. However, the brake pedal operation amount when the brake pedal 92 is switched to is used. You can also. In this case, it can be determined that the urgency level RU2 is higher as the brake pedal operation amount is larger.

  In the first and second embodiments described above, each time the driver performs a series of operations, the risk potential RP during the reaction operation is stored, and the risk potentials RPa and RPb are learned using the stored risk potential data. . That is, since individual differences between drivers in the system are gradually learned, the RP-ΔF characteristic is also gradually corrected. Therefore, it is possible to perform the operation reaction force control while correcting the RP-Δ characteristic without causing the driver to feel uncomfortable.

  In the first and second embodiments described above, the risk potential RP is learned as the vehicle state during the reaction operation. However, the data to be learned is not limited to the risk potential RP. That is, it is also possible to learn the traveling condition around the host vehicle detected by the laser radar 10 or the like, or the inter-vehicle time THW and the margin time TTC calculated based on the traveling condition. Therefore, in the first and second embodiments described above, the vehicle state at the time of the reaction operation to be learned is the driving situation around the host vehicle, the inter-vehicle time THW calculated based on the driving situation, the margin time TTC, and the inter-vehicle distance The risk potential RP calculated from the time THW and the margin time TTC is included.

  In the first and second embodiments described above, the example of the system that controls the accelerator pedal reaction force according to the risk potential RP has been described, but the brake pedal reaction force is controlled according to the risk potential RP. The present invention can be similarly applied to the above.

  Note that the vehicle driving assistance device according to the first and second embodiments uses the laser radar 10 and the vehicle speed sensor 30 as the situation recognition means, and uses a risk potential calculation means, a reaction learning means, a correction means, and learning data selection. A controller 50 was used as a means. Moreover, the controller 50 and the accelerator pedal reaction force control apparatus 80 were used as the operation reaction force control means. However, the present invention is not limited to these. For example, instead of the laser radar 10, another type of millimeter wave radar or the like, or a CCD camera or a CMOS camera can be used as the situation recognition means.

1 is a system diagram of a vehicle driving assistance device according to a first embodiment of the present invention. The block diagram of the vehicle carrying the driving operation assistance apparatus for vehicles shown in FIG. The block diagram around an accelerator pedal. The flowchart which shows the process sequence of the driving operation assistance control program by the controller of 1st Embodiment. The figure explaining the learning of risk potential. (A) (b) The figure explaining the relationship between the driver reaction at the time of an accelerator pedal off, and risk potential. The figure explaining the driver reaction at the time of brake pedal ON, and the risk potential. The figure which shows the frequency distribution of the risk potential at the time of an accelerator pedal off, and the risk potential at the time of a brake pedal on. The figure which shows the relationship between the risk potential before correction | amendment, and reaction force increase amount. The figure which shows the relationship between the risk potential corrected using learning data, and reaction force increase amount. The flowchart which shows the process sequence of the driving operation assistance control program by the controller of 2nd Embodiment. The figure explaining the driver reaction and risk potential when the accelerator pedal is off, and the relationship between the driver reaction and risk potential when the brake pedal is on.

Explanation of symbols

10: laser radar 30: vehicle speed sensor 50: controller 80: accelerator pedal reaction force control device 82: accelerator pedal 84: accelerator off switch 94: brake on switch

Claims (13)

A situation recognition means for detecting the driving situation around the vehicle,
Risk potential calculation means for calculating a risk potential around the host vehicle based on the detection result of the situation recognition means;
Based on the risk potential calculated by the risk potential calculation means, an operation reaction force control means for controlling an operation reaction force generated in the driving operation device;
Reaction learning means for learning a vehicle state at the time of a predetermined reaction operation of the driver;
A vehicle driving assisting device, comprising: a correcting unit that corrects a risk potential equation in the risk potential calculating unit based on the vehicle state learned by the reaction learning unit. The vehicle driving assistance device according to claim 1,
The reaction learning means learns the risk potential during the reaction operation as the vehicle state,
The correction means corrects the risk potential equation so that the risk potential calculated by the risk potential calculation means becomes smaller as the risk potential during the reaction operation learned by the reaction learning means is larger. A driving operation assisting device for a vehicle. In the driving assistance device for vehicles according to claim 1 or 2,
The vehicle further comprises learning data selection means for selecting the vehicle state as learning target data in the reaction learning means based on a response of the driver or the behavior of the host vehicle regarding the reaction operation. Driving assistance device. In the driving assistance device for vehicles according to claim 1 or 2,
The vehicle driving operation assisting device, wherein the reaction learning unit corrects the vehicle state based on a reaction of the driver with respect to the reaction operation or a behavior of the host vehicle. The vehicle driving assistance device according to claim 3,
The reaction learning means learns the vehicle state when the accelerator pedal is released as the vehicle state during the predetermined reaction operation,
The learning data selection means uses a changeover time from when the accelerator pedal is released until the brake pedal is depressed as the driver's reaction or the behavior of the host vehicle. The vehicle driving assistance device according to claim 5,
The driving data assisting device for a vehicle, wherein the learning data selecting means selects the vehicle state when the stepping time is smaller than a predetermined value as the learning target data. The vehicle driving operation assistance device according to claim 4,
The reaction learning means learns the vehicle state when the accelerator pedal is released as the vehicle state during the predetermined reaction operation, and corrects the vehicle state as a response of the driver or a behavior of the host vehicle. A vehicular driving operation assisting device that uses a step-change time from when the accelerator pedal is released to when the brake pedal is depressed. The vehicle driving assistance device according to claim 7,
The vehicle driving operation assistance device according to claim 1, wherein the reaction learning unit corrects the learning data of the vehicle state according to the stepping time and learns the vehicle state during the reaction operation. The vehicle driving assistance device according to claim 3,
The reaction learning means learns the vehicle state when the brake pedal is depressed after releasing the accelerator pedal as the vehicle state during the predetermined reaction operation,
The learning data selection means uses the degree of deceleration of the host vehicle by depressing a brake pedal as the driver's reaction or the behavior of the host vehicle. The driving operation assisting device for a vehicle according to claim 9,
The vehicle learning operation assisting device, wherein the learning data selection means selects the vehicle state when the deceleration rate is larger than a predetermined value as the learning target data. The vehicle driving operation assistance device according to claim 4,
The reaction learning means learns the vehicle state when the brake pedal is depressed after releasing the accelerator pedal as the vehicle state during the predetermined reaction operation, and corrects the vehicle state when the driver responds. Alternatively, the driving assisting device for a vehicle is characterized in that as the behavior of the host vehicle, the deceleration of the host vehicle caused by depression of a brake pedal is used. The vehicle driving operation assistance device according to claim 11,
The vehicle driving operation assistance device according to claim 1, wherein the reaction learning unit corrects the learning data of the vehicle state according to the deceleration rate and learns the vehicle state during the reaction operation.   A vehicle comprising the vehicular driving assist device according to any one of claims 1 to 12.

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