JP6857472B2 - Steering assist ratio calculation device for power steering system and steering assist ratio calculation method for power steering system - Google Patents

Description

The present invention relates to a steering assist ratio calculation device for a power steering system and a steering assist ratio calculation method for a power steering system.

Conventionally, for example, in Patent Document 1 below, a myoelectric potential is detected from a deltoid muscle electrode, an integrated myoelectric potential is obtained, and whether it is an active state or a passive state is determined based on the integrated myoelectric potential. It is described that information from the pressure sensor, the vehicle steering state detection unit, and the sensory evaluation input unit is collected and stored together with some information, and the factors of the steering feeling are evaluated using these information. ..

Japanese Unexamined Patent Publication No. 2003-177079

When the driver drives the vehicle, steering is one of the main parts of the driving operation, and the driving feeling can be enhanced by improving the steering feeling. However, the technique described in the above patent document evaluates the factors of the steering feeling, and does not assume any improvement in the steering feeling.

Therefore, the present invention has been made in view of the above problems, and an object of the present invention is that it is possible to improve the steering feeling of the driver by optimally setting the steering assist ratio. It is an object of the present invention to provide a new and improved steering assist ratio calculation device and a steering assist ratio calculation device method.

In order to solve the above problems, according to a certain viewpoint of the present invention, the driver classifies the vehicle according to the difference in the average vehicle speed and the average yaw rate when driving on a curve based on the information of the current location and the destination of the vehicle. Among the driving states to be performed, the driving state acquisition unit that acquires the driving state according to the driving path from the current location to the destination, and the degree of dispersion of the arm acceleration and the amount of muscle activity of the arm related to steering are low. Assist for acquiring the assist ratio according to the driving state according to the driving path acquired by the driving state acquisition unit from a database defined for each driver for each driving state. A steering assist ratio calculation device for a power steering system including a ratio acquisition unit is provided.

Further, the driving state acquisition unit acquires the vehicle speed and the yaw rate for each curve section obtained from the map information, and averages the vehicle speed and the yaw rate in all the curve sections to obtain the average vehicle speed and the average yaw rate according to the driving route. It may be acquired as the operating state.

Further, in order to solve the above problem, according to another viewpoint of the present invention, the difference between the average vehicle speed and the average yaw rate when traveling on a curve is determined based on the information of the current location and the destination of the vehicle driven by the driver. Among the driving states classified according to the above, the step of acquiring the driving state according to the driving path from the current location to the destination, and the degree of dispersion of the arm acceleration and the amount of muscle activity of the arm related to steering are low. The assist ratio of the steering steering is obtained from the database defined for each driver for each driving state, and the assist ratio corresponding to the driving state according to the driving path acquired in the step of acquiring the driving state is acquired. A steering assist ratio calculation method for a power steering system is provided, comprising:

As described above, according to the present invention, the steering feeling of the driver can be improved by optimally setting the steering assist ratio.

It is a schematic diagram which shows the structure of the assist ratio calculation system 1000 of steering steering which concerns on one Embodiment of this invention. It is a schematic diagram which shows the outline of the processing performed by the assist ratio arithmetic unit 100. It is a flowchart which shows the detail of the process performed by the assist ratio arithmetic unit 100. It is a flowchart which shows the detail of the process performed by the assist ratio arithmetic unit 100. It is a schematic diagram which shows the example of a label table. It is a characteristic diagram which shows the example which plotted _{the Z score (ZA ijk} ) of the acceleration of an arm _{and the Z score (ZM ijk} ) of the muscle load of an arm on a two-dimensional plane. It is a characteristic figure which shows the relationship between _{the discretenesses D 111} , D ₂₁₁ , D ₃₁₁ and the assist ratio of i = 1, 2, 3 respectively when j = 1, k = 1. It is a schematic diagram which shows the approximate curve calculated from the formula (2). It is a flowchart which shows the process of the automatic change mode of the assist ratio which concerns on this embodiment. It is a schematic diagram which shows the state _{that the optimum C xjk} is stored in the database according to the traveling state. It is a schematic diagram which shows the route from the present place to the destination. It is a schematic diagram which shows the information stored in a database. It is a characteristic diagram for _{demonstrating} how the peak value C xjk of the assist ratio is different for each of different drivers A, B, and C. It is a characteristic diagram for _{demonstrating} how the peak value C xjk of the assist ratio is different for each of different drivers A, B, and C. It is a characteristic diagram for _{demonstrating} how the peak value C xjk of the assist ratio is different for each of different drivers A, B, and C.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the present specification and the drawings, components having substantially the same functional configuration are designated by the same reference numerals, so that duplicate description will be omitted.

FIG. 1 is a schematic view showing a configuration of a steering assist ratio calculation system 1000 according to an embodiment of the present invention. The system 1000 shown in FIG. 1 can be mounted on a vehicle. In the present embodiment, body information related to steering is acquired from each driver (driver), and the optimum steering assist ratio for each driver is calculated based on the acquired body information. Steering As body information related to steering, arm acceleration A and arm muscle activity M (myoelectricity (also referred to as muscle load)) are used.

When the driver drives the vehicle, steering is one of the main parts of the driving operation, and the driving feeling can be enhanced by improving the steering feeling. In the present embodiment, the steering feeling is improved by setting the optimum steering control according to the individual characteristics by measuring the steering operation of the occupant. Specifically, the acceleration A and the muscle activity amount M of the hand during steering are measured by biometric measurement. These measurements are performed with three or more different control settings. The control setting is changed by changing the assist ratio C of the electric power steering system (EPS). Acceleration A and muscle activity M acquired with different control settings are scored as Z, and the average of the degree of discreteness D as a steering feeling index is calculated for each control setting on the two-axis phase plane of acceleration A and muscle activity M. calculate. Then, an approximate curve is obtained so that the obtained discrete degree D and the assist ratio C are non-linear (D = f (C)), and the assist ratio at which the discrete degree D is the optimum value (vertex that becomes convex downward) is obtained. .. According to such a method, since the assist ratio having the lowest degree of discreteness can be calculated for each driver, the assist ratio can be optimally set according to the characteristics of each driver. As the acceleration A, the average value of the three-axis composite vector may be used.

In the present embodiment, the acceleration A of the arm and the muscle activity amount M of the arm are exemplified as the biological information, but the biological information may be other information. Further, although two pieces of information, the acceleration A of the arm and the muscle activity amount M of the arm, are used as the biological information, one or three or more biological information may be used.

As shown in FIG. 1, the assist ratio calculation system 1000 includes a control device (assist ratio calculation device) 100, a biological sensor 200 (accelerometer 210, muscle load sensor 220 for measuring muscle load), a vehicle sensor 400, and a mode switching SW500. , Communication unit 510, and electric power steering system (EPS) 600. The vehicle sensor 400 includes a vehicle speed sensor 402, a steering angle sensor 404, a yaw rate sensor 406, a steering torque sensor 408, an outside sensor 410, and a position sensor (GPS) 412. The vehicle exterior sensor 410 detects the road shape, the white line shape of the road surface, and the like by image processing of an image obtained from a stereo camera or the like. Instead of the stereo camera, another imaging device such as a monocular camera may be used. Further, as the vehicle exterior sensor 410, a device that detects the situation outside the vehicle by using a millimeter wave radar, an infrared laser, or the like may be used. The position sensor 412 acquires the current position. The communication unit 510 communicates with the outside by wireless communication, and acquires the traveling state, traveling history, etc. of another vehicle.

The acceleration of the arm is measured by attaching the acceleration sensor 210 to the driver's arm. Further, the acceleration of the arm may be calculated from the locus of the hand by analyzing the image captured by the camera and measuring the locus of the hand. In the case of image analysis, marker measurement by motion capture and markerless measurement by a stereo camera, an infrared camera, or the like can be used. The method of acquiring the acceleration of the arm is not limited to these, and other methods may be used.

The muscle burden sensor 220 includes an electromyogram (electrode), and measures the electromyography by, for example, attaching the electromyogram to the deltoid muscle of the driver. Specifically, the value obtained by calculating% MVC from the myoelectric data can be used from the following formula. The RMS value represents the effective value of myoelectricity. The RMS value at the time of maximum voluntary contraction is measured in advance in a stopped state or the like.
% MVC = RMS value at the analysis site ÷ RMS value at the time of maximum voluntary contraction Note that the muscle load may be measured by a substitute measurement using a load sensor instead of the electromyogram. In this case, a load sensor is attached to the steering wheel or the seat, the load is measured as a point of action when the driver applies force, and the muscle load is estimated from the load. In addition, the muscle load may be estimated using a musculoskeletal model or the like.

The assist ratio arithmetic unit 100 includes a first control unit 110, a second control unit 120, a motor control unit 130, and databases 140 and 150. The first control unit 110 obtains a control value for controlling the motor included in the electric power steering system (EPS) 600 based on the vehicle speed and steering torque, and sends the control value to the motor control unit 130. The motor control unit 130 controls the motor included in the electric power steering system (EPS) 600 based on the control value.

The second control unit 120 calculates the assist ratio of the electric power steering system (EPS) 600 based on the information of the biosensor 200. The motor control unit 130 controls the motor included in the electric power steering system (EPS) 600 based on the assist ratio calculated by the second control unit 120.

In a normal driving state, the motor included in the electric power steering system (EPS) 600 is controlled based on the vehicle speed and steering torque. At this time, the electric power steering system (EPS) 600 controls the electric power steering system (EPS) 600 with a predetermined assist ratio. On the other hand, when the driver performs a predetermined operation by the mode switching SW500, the assist ratio by the electric power steering system (EPS) 600 is changed to the assist ratio calculated by the second control unit 120, and the electric power is changed by the changed assist ratio. The motor included in the steering system (EPS) 600 is controlled.

The database 140 stores map information of the navigation system, travel history information of the own vehicle or another company, and the like. Further, the assist ratio C _xjk, which will be described later, is stored in the database 150.

The second control unit 120 includes a biological information acquisition unit 122 that acquires biological information detected by the biological sensor 200, an assist ratio calculation unit 124 that calculates an assist ratio based on the biological information, and an operating state that acquires the driving state of the vehicle. It includes an acquisition unit 126, an assist ratio acquisition unit 127 that acquires the assist ratio stored in the database 150 based on the driving state of the vehicle, and a route search unit 128.

More specifically, the biometric information acquisition unit 122 acquires biometric information related to steering steering according to a plurality of driving states for each of the plurality of assist ratios of steering steering set by the power steering system 600. In addition, the assist ratio calculation unit 124 calculates the assist ratio at which the degree of dispersal of biometric information is low in a plurality of operating states. The driving state acquisition unit 126 acquires the driving state according to the driving route based on the information of the current location and the destination of the vehicle. The assist ratio acquisition unit 127 obtains the assist ratio according to the driving state acquired by the driving state acquisition unit 126 from the database 150 that defines the assist ratio at which the degree of dispersion of biometric information related to steering is low for each driving state. get.

Each component of the assist ratio arithmetic unit 100 of FIG. 1 can be composed of a central processing unit such as a circuit (hardware) or a CPU and a program (software) for functioning the central processing unit. Further, the program can be stored in a recording medium such as a memory.

FIG. 2 is a schematic diagram showing an outline of processing performed by the assist ratio arithmetic unit 100. First, the vehicle is driven with the acceleration sensor 210 and the muscle load sensor 220 attached to the driver (step S10). Then, the acceleration and myoelectricity of the hand are measured during driving (steps S12 and S14).

Next, the measured acceleration and the degree of discreteness of myoelectricity are calculated (step S16). Next, the discreteness data is accumulated, and the optimum value of the steering assist ratio is calculated based on the discreteness (step S18). Next, the steering assist ratio in the electric power steering system (EPS) 600 is changed (step S18), the process returns to step S10, and the processes of steps S10 to S20 are repeated. Then, after repeating the process three or more times, the process proceeds from step S16 to step S22 to determine the optimum value and finally determine the assist ratio (step S24). The control condition is determined by the assist ratio determined in step S24, and the vehicle is driven (step S26). Various vehicle information such as vehicle speed and steering angle are appropriately used to determine control conditions.

3 and 4 are flowcharts showing the details of the processing performed by the assist ratio arithmetic unit 100. The process of FIG. 3 is mainly performed by the biological information acquisition unit 122. Further, the processing of FIG. 4 is mainly performed by the assist ratio calculation unit 124 and the assist ratio determination unit 127. First, in step S100, the mode switching SW500 is operated to turn on the initial setting mode. Although the initial setting mode is premised on driving the vehicle, various information may be acquired in the simulation setting mode in which the vehicle is steered while the vehicle is stationary. In the next step S102, the assist ratio Ci of the electric power steering system (EPS) 600 at each control setting is set. i is set to 3 or more, and 3 or more assist ratios are set. As an example, i = 1, 2, 3 and C1 = 1, C2 = 2, C3 = 0.4. In addition, the assist ratio is set in a straight section or while the vehicle is stopped. The straight line section can be estimated by using the map information of the navigation system, the information obtained from the external sensor 410, and the like.

In the next step S104, recording of various data is started. The data recorded here are various data such as vehicle speed Vi, steering angle αi, yaw rate γi, actual steering torque Tsi, steering torque Thi, arm acceleration Ai, arm muscle load Mi, and the like.

In the next step S106, it is estimated whether or not the vehicle is traveling in the steady circle section, and the average value of each of the above data is calculated for each travel in the steady circle section. For the estimation of the steady circular running section, as an example, the moving average peak value of the yaw rate is calculated, and 4 seconds before and 2 seconds after the peak value are estimated as the steady circular running section, and each data is calculated.

In the next step S108, each data is labeled with j and k according to the average value of the vehicle speed and the yaw rate for each steady circle section according to the label table. FIG. 5 is a schematic diagram showing an example of a label table. When i = 1, the average vehicle speed V1 in a certain steady circle section is 0 to 10 [km / h], and when the average yaw rate γ1 is 0.1 to 0.2 [rad / s], after labeling. The acceleration of the arm is A ₁₁₅ .

In the next step S110, the operation is continued until N data in which j and k are combined are recorded. In the next step S112, 1 is added to the value of i, in the next step S114, it is determined whether or not the value of i is 3 or less, and if the value of i is 3 or less, the processing after step S102 is repeated. Do.

If the value of i exceeds 3 in step S114, the process proceeds to step S116 of FIG. In step S116, j and k are changed from 1, and the calculation is started with each combination. The value of j is from 1 to J (j = 1: J), and the value of k is from 1 to K (k = 1: K). In the next step S118, the data (arm acceleration, arm muscle load) with matching labels j and k are converted into Z scores, respectively. As a result, (ZA _ijk , ZM _ijk ) is obtained. The Z score is expressed by replacing the position of a certain value pi of the element i constituting the population in the distribution with a standard normal distribution having a mean value of 0 and a standard deviation of 1 (Z). Score = (pi-μ) / σ, where μ is the population mean, σ is the standard deviation), and if pi is equal to the mean, the Z score is 0. It will be a negative value. The above formula for obtaining the Z score is obtained by replacing the distribution of the standard deviation of an arbitrary value with the standard normal distribution of the standard deviation 1.

_{FIG. 6 is a characteristic diagram showing an example in which the Z score (ZA ijk} ) of the acceleration of the arm and the Z score (ZM _ijk ) of the muscle load of the arm are plotted on a two-dimensional plane. In FIG. 6, the horizontal axis _{shows the value of the Z score (ZA ijk} ) of the acceleration of the arm, and the vertical axis shows the value of the Z score (ZM _ijk ) of the muscle burden of the arm. The characteristics of the plot shown in FIG. 6 are obtained for each specific combination of j and k. In a specific combination of i and j, plotting is performed for each of the cases where i indicating the assist ratio is 1, 2, and 3. As an example, for convenience of explanation, it is assumed that the characteristics shown in FIG. 6 are j = 1 and k = 1. In FIG. 6, the plot when i = 1 (assist ratio C1 = 1, steering torque “intermediate”) is shown by ◇, and the plot when i = 2 (assist ratio C2 = 2, steering torque “light”) is shown. It is shown by □, and the plot when i = 3 (assist ratio C3 = 0.4, steering torque “heavy”) is shown by Δ.

_{In the next step S120, the degree of discreteness Dijk} for each control setting of each assist ratio is obtained from the Z-score data. The degree of discreteness _Dijk is calculated from the following equation (1). Discrete degree D _ijk is an index created based on the experimental result that the movement during steering (acceleration A, muscle activity amount M) is highly reproducible in a control setting that is easy to drive. If the steering torque is too heavy or too light, the variation in muscle activity and acceleration tends to be large, and the degree of discreteness tends to be high, and the degree of discreteness has a high correlation with the steering feeling. When an appropriate steering torque is set with a good steering feeling, muscle activity and acceleration do not increase or decrease excessively with respect to a setting where the steering torque is heavy or light, variation is reduced, and the degree of discreteness is reduced.

As described above, for example, when j = 1 and k = 1, the degree of discreteness _{Di 11} can be obtained. FIG. 7 is a characteristic diagram showing the relationship between _{the discrete degrees D 111} , D ₂₁₁ , and D _{311 of} i = 1, 2, and 3 and the assist ratio when j = 1 and k = 1. As shown in FIG. 7, it can be seen that the degree of discreteness D changes according to the assist ratio.

In the next step S122, _{it is determined whether or not the conditions of D 1jk} <D _2jk and D _1jk <D _3jk are satisfied, and if this condition is satisfied, the process proceeds to step S124. On the other hand, if this condition is not satisfied, the process returns to step S116 and recalculation is performed. Here, when the assist ratio is C3, the steering torque is heavy, when the assist ratio is C2, the steering torque is light, and C3 and C2 are set to control settings in which the assist ratios are extremely different. On the other hand, in the case of the assist ratio C1, the steering torque is intermediate, the design neutral value, and the control setting is assumed to be close to an appropriate value. Therefore, by performing the determination in step S122, only the condition that the degree of discreteness at the assist ratio C1 becomes the minimum value smaller than the degree of discreteness at the time of the assist ratio C2 and the assist ratio C3 is extracted.

In step S124, a non-linear approximation curve showing the relationship between the degree of discreteness D and the assist ratio C in FIG. 7 is calculated. The approximate curve can be calculated from the following equation (2), for example. However, in equation (2), e is a constant term.
D _jk = (C-C _xjk ) ² + e ... (2)

FIG. 8 is a schematic diagram showing an approximate curve calculated from the equation (2). In step S124, the peak value C _xjk is calculated based on the approximate curve. In the examples of FIGS. 7 and 8, the minimum value Ci ₁₁ is calculated.

_{For example, in FIG. 8, D jk} in each case of the assist ratios C1 = 1, C2 = 2, C3 = 0.4 _{is substituted into the equation (2), and C x 11 is performed} by the following calculation based on the equation (2). = 0.81. Specifically, the degree of discreteness D _{111 = 0.9 when C1 = 1, the degree of discreteness D 211} = 2.6 when C2 = 2, and the degree _{of discreteness D 311} = 1 when C3 = 0.4. Substitute 4 into equation (2) _{to obtain C x11} = 0.81. Therefore, in this case, the assist ratio having the lowest degree of discreteness is 0.81.
D _i11 = 3.93c ² -6.36c + 3.29
= 3.93 (c-0.81) ² + 0.71

In the next step S126, it is determined whether or not the processing of steps S116 to S124 is completed for all the combinations of j and k, and if the processing is completed for all the combinations of j and k, the process proceeds to step S128. All C _xjk are stored in database 150. On the other hand, if the processing is not completed for all the combinations of j and k, the process returns to step S116. After step S128, the process proceeds to step S130, and the initial setting mode is set to OFF.

_{FIG. 10 is a schematic view showing how the optimum C xjk} according to the traveling state is stored in the database 150. As shown in FIG. 10, the database stores the _{optimum assist ratio C xjk} for any combination of the average speed vj and the average yaw rate γl.

Next, the automatic change mode 1 of the assist ratio according to the present embodiment will be described with reference to FIG. The processing of FIG. 9 is mainly performed by the operating state acquisition unit 126, the assist ratio acquisition unit 127, and the route search unit 128. First, in step S200, the driver turns on the automatic change mode 1. In the next step S202, the route search is started. Specifically, the route search unit 128 starts searching for a route from the current location to the destination based on the navigation map information stored in the database 140.

In the next step S204, the average vehicle speed vr when traveling on the curve section on the travel route is acquired for each curve section. Here, l is the number of curves. In the next step S206, the yaw rate average value γl or the average curve radius ρl when traveling on the curve section on the travel route is acquired for each curve section.

FIG. 11 is a schematic diagram showing a route from the current location to the destination. In the example of FIG. 11, the number of curves is 2 (l = 2), and in the section from the current location Q1 to the intermediate location Q2, the average vehicle speed is v1, the yaw rate average value is γ1, and the average curve radius is ρl. Further, in the section from the intermediate place Q2 to the destination Q3, the average vehicle speed is v2, the yaw rate average value is γ2, and the average curve radius is ρ2.

The average vehicle speed lv, the yaw rate average value rl, or the average curve radius ρl is acquired from the navigation map information data stored in the database 140. Further, the travel history information of the own vehicle or another vehicle may be acquired and acquired from the travel history information. FIG. 12 is a schematic diagram showing information stored in the database 140. As shown in FIG. 12, the database 140 stores information such as the average vehicle speed vr, the yaw rate average value γl, and the average curve radius ρl in each section in association with the map information. In addition, the database 140 stores information on the travel history of the own vehicle and other vehicles, and includes the curve section in the map information, the average vehicle speed wl when the vehicle and other vehicles travel on the curve section, and the average yaw rate. It is recorded in association with rl or the average curve radius ρl. The average vehicle speed lv, the mean yaw rate value γl, or the average radius of curvature ρl in each section shown in FIG. 12 is obtained by averaging the vehicle speed, yaw rate, and radius of curvature when traveling in the estimated steady circular section. For example, it can be obtained by starting to accumulate each data when it is determined to be the beginning of the stationary circle interval, and calculating the average value of each data when it is determined to be the end of the stationary circle interval.

If the data acquired in step S206 is ρl, the process proceeds to step S208, and the average yaw rate γl is calculated from the following formula. When the yaw rate average value rl is directly acquired in step S206, the process proceeds to step S210 without going through step S208.
γl = vl / ρl

In step S210, the average vehicle speed vy of the traveling route to the destination is obtained by averaging the average vehicle speed vr acquired in all the curve sections. Further, the average yaw rate γy of the traveling route to the destination is obtained by averaging the average yaw rate γl acquired in all the curve sections. As described above, the driving state acquisition unit 126 acquires the average vehicle speed by and the average yaw rate γy as the driving state according to the driving route. In the next step S212, the assist ratio acquisition unit 127 selects a combination of vehicle speed vj and yaw rate γk that is the closest value to the combination of average vehicle speed by and average yaw rate γy from the database 150 storing _{C xjk shown in FIG. The search is performed, and the control condition C xjk} corresponding to the combination of the searched vehicle speed vj and the yaw rate γk is selected.

In the next step S214, the control condition (assist ratio is C _xjk ) is set. When the vehicle reaches the destination in the next step S216, the automatic change mode 1 is turned off (OFF) in the next step S218.

13A to 13C are characteristic diagrams showing how _{the peak value C xjk} of the assist ratio is different for each of the different drivers A, B, and C. In the characteristics of the driver A shown in FIG. 13A, the degree of discreteness peaks when the steering torque is in the middle (assist ratio ≈ 1). Further, in the characteristics of the driver B shown in FIG. 13B, the degree of discreteness peaks when the steering torque is heavy (assist ratio 1.0 or less). Further, in the characteristics of the driver C shown in FIG. 13C, the degree of discreteness peaks when the steering torque is light (assist ratio 1.0 or more). As described above, the optimum assist ratio differs for each driver, but in the present embodiment, the peak value C _xjk at which the discreteness D is the smallest is obtained for each of the labels j and k according to the driving state, and further. Since C _xjk is selected according to the driving state, it is possible to set the optimum assist ratio Cx according to the driving state for each driver.

As described above, according to the present embodiment, it is possible to calculate the assist ratio at which the degree of dispersal of biometric information is the lowest for each driver. Therefore, the assist ratio should be optimally set according to the characteristics of each driver. Can be done. In addition, the driving state from the current location to the destination can be acquired based on the map information, and the assist ratio that minimizes the degree of dispersal of biological information can be automatically set according to the driving state, so the optimum steering feeling according to the driving state can be set. Can be obtained.

Although the preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, the present invention is not limited to such examples. It is clear that a person having ordinary knowledge in the field of technology to which the present invention belongs can come up with various modifications or modifications within the scope of the technical idea described in the claims. , These are also naturally understood to belong to the technical scope of the present invention.

100 Assist ratio arithmetic unit 124 Assist ratio acquisition unit 126 Operating status acquisition unit 150 Database

Claims (3)

Of the driving states classified according to the difference in average vehicle speed and average yaw rate when traveling on a curve based on the information on the current location and destination of the vehicle driven by the driver, the driving route from the current location to the destination. The operation state acquisition unit that acquires the operation state according to
The driving state acquisition unit acquires the steering assist ratio, which reduces the degree of dispersion of the arm acceleration and arm muscle activity related to steering, from the database defined for each driver for each driving state. An assist ratio acquisition unit that acquires an assist ratio according to the operation state according to the operation path, and an assist ratio acquisition unit.
A steering assist ratio calculation device for a power steering system, which is characterized by being equipped with. The driving state acquisition unit acquires the vehicle speed and the yaw rate for each curve section obtained from the map information, averages the vehicle speed and the yaw rate in all the curve sections, and obtains the average vehicle speed and the average yaw rate according to the driving route. The steering assist ratio calculation device of the power steering system according to claim 1, wherein the state is acquired. Of the driving states classified according to the difference in average vehicle speed and average yaw rate when traveling on a curve based on the information on the current location and destination of the vehicle driven by the driver, the driving route from the current location to the destination. Steps to get the operating status according to
A step of acquiring the driving state from a database in which the assist ratio of the steering steering, which reduces the degree of dispersion of the arm acceleration and the muscle activity of the arm related to the steering steering, is defined for each driver for each driving state. The step of acquiring the assist ratio according to the driving state according to the driving route acquired in step 1 and
A steering assist ratio calculation method for a power steering system, which comprises.

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