WO2018029914A1 - Vehicle state quantity estimation device - Google Patents

Vehicle state quantity estimation device Download PDF

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Publication number
WO2018029914A1
WO2018029914A1 PCT/JP2017/015973 JP2017015973W WO2018029914A1 WO 2018029914 A1 WO2018029914 A1 WO 2018029914A1 JP 2017015973 W JP2017015973 W JP 2017015973W WO 2018029914 A1 WO2018029914 A1 WO 2018029914A1
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WIPO (PCT)
Prior art keywords
vehicle
value
state quantity
parameter
updated
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PCT/JP2017/015973
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French (fr)
Japanese (ja)
Inventor
奈須 真吾
絢也 高橋
修之 一丸
隆介 平尾
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日立オートモティブシステムズ株式会社
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Publication of WO2018029914A1 publication Critical patent/WO2018029914A1/en

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    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/103Side slip angle of vehicle body
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/112Roll movement

Definitions

  • the present invention relates to a vehicle state quantity estimating device for estimating a state quantity of a vehicle.
  • the present invention is an invention for solving the above-mentioned problems, and accurately estimates state quantities such as a side slip angle, a lateral speed, and a roll angle of a vehicle body even under adverse conditions that are difficult to detect by an external recognition means.
  • An object of the present invention is to provide a vehicle state quantity estimating device.
  • the vehicle state quantity estimating device receives the detection value of the inertial sensor and the detection value of the external recognition means, and based on the detection value of the inertial sensor and a preset vehicle parameter.
  • the vehicle parameter is updated using a detection value of the external environment recognition means.
  • the vehicle state quantity can be estimated with high accuracy.
  • FIG. 5 is a flowchart showing an outline of processing by the vehicle state quantity estimation device 1 according to the first embodiment. 5 is a flowchart showing stability determination by the vehicle state quantity estimation device 1 according to the first embodiment.
  • FIG. 5 is a flowchart showing parameter update by the vehicle state quantity estimation device 1 according to the first embodiment.
  • FIG. 7 is a flowchart showing stability determination by the vehicle state quantity estimation device 1 according to the second embodiment.
  • FIG. 10 is a flowchart showing parameter update by the vehicle state quantity estimation device 1 according to the third embodiment.
  • FIG. 10 is a flowchart showing output value determination by a vehicle state quantity output device 30 according to the fourth embodiment.
  • FIG. The figure which shows the vehicle structure carrying the vehicle state quantity estimation apparatus 1 or the vehicle state quantity output device 30 which concerns on Embodiment 5.
  • FIG. 9 is a conceptual diagram of a suspension control unit 40 that performs riding comfort control, which is one function of a control suspension device 41 according to a fifth embodiment.
  • FIG. 10 is a conceptual diagram of a suspension control unit 40 that performs anti-roll control, which is one function of a control suspension device 41 according to a fifth embodiment.
  • FIG. 9 is a conceptual diagram of a suspension control unit 40 that performs anti-dive control and anti-squat control, which are one function of a control suspension device 41 according to a fifth embodiment.
  • FIG. 10 is a diagram showing an acceleration PSD as a processing result of a suspension control unit according to a fifth embodiment.
  • FIG. 10 is a view showing a change over time of a processing result of a suspension control unit according to a fifth embodiment.
  • FIG. 10 is a diagram illustrating acceleration acting on a gravity center point 11 of a vehicle traveling on a bank road having a cant angle ⁇ according to the seventh embodiment.
  • FIG. 1 is a conceptual diagram of a vehicle state quantity estimation device 1 that performs stability determination of a detection value of the external environment recognition unit 2, parameter update based on the detection value, and vehicle state quantity estimation using the parameter.
  • the vehicle state quantity estimation device 1 includes, for example, a first vehicle motion state quantity detection value 100 detected by an inertia sensor or a gyro sensor, a driver input quantity detection value detected by a steering angle sensor, a stroke sensor, or the like, or external environment recognition.
  • the second vehicle motion state detection value 200 detected by the means is input.
  • the vehicle momentum state quantity estimated value 300 is output.
  • the first vehicle motion state quantity detection value 100 is a value such as a wheel speed, a longitudinal acceleration of the vehicle body, a lateral acceleration, or a yaw rate.
  • the driver input amount detection value is a value such as a steering angle, an accelerator opening degree, or a brake depression force.
  • the second vehicle motion state quantity detection value 200 is a value such as a side slip angle, a lateral speed, a yaw angle, a roll angle, or a pitch angle of the vehicle body detected by the external environment recognition unit 2.
  • the vehicle motion state quantity estimated value 300 is a side slip angle, a lateral speed, a roll angle, a pitch angle, or the like of the vehicle body.
  • the vehicle state quantity estimation device 1 includes a stability determination unit 21, a parameter update unit 22, and a state quantity estimation unit 23.
  • the vehicle state quantity estimation device 1 includes a stability determination unit 21, a parameter update unit 22, and a state quantity estimation unit 23.
  • the state quantity estimation unit 23 stores preset vehicle parameters, and calculates a vehicle state quantity estimation value 300 using the vehicle motion state quantity detection value 100 and the parameters.
  • the stability determination unit 21 determines whether the second vehicle motion state quantity detection value 200 that is an input value from the external environment recognition unit 2 is stable by using an input value from the external environment recognition unit 2 or the inertial sensor. The judgment result is output.
  • the parameter update unit 22 updates the parameters stored in the state quantity estimation unit 23 using the input values from the external environment recognition unit 2 and the inertial sensor and the determination result from the stability determination unit 21. Specifically, the vehicle parameter is updated so that the vehicle motion state quantity detection value 200 detected by the external environment recognition unit 2 is the same as the estimated value calculated by the state quantity estimation unit using an inertial sensor or parameter. . In other words, by updating the parameters, the estimated value obtained by the state quantity estimating unit 23 is identified by the actually measured value obtained by the external field recognition means.
  • the parameter update in the present invention means that parameters such as vehicle mass, position of the center of gravity, moment of inertia, cornering power, etc. that change due to people getting on and off, changing tires, etc. are calculated based on input values, and stored parameters Is replaced with the calculated parameter.
  • Vehicle parameters change depending on people getting on and off, changing tires, and tire deterioration. Therefore, a difference occurs between the vehicle parameter stored in advance and the actual vehicle parameter, and this difference becomes an error of the estimated value 300 of the vehicle motion state.
  • the parameter is updated so that the detected value of the external environment recognition unit 2 matches the estimated value in the state quantity estimating unit 23, and the vehicle motion state quantity estimated value 300 is based on the updated parameter and the value of the inertial sensor. Is calculated. Therefore, it becomes possible to cope with fluctuations in vehicle parameters, and state quantities that are difficult to detect directly with an inertial sensor such as a side slip angle, a lateral speed, and a roll angle can be estimated with high accuracy using the value of the inertial sensor.
  • the vehicle motion state quantity detection value 200 used for the update is stable, that is, only a highly reliable value is used. Therefore, the stability determination of the vehicle motion state quantity detection value 200 in the stability determination unit 21 is performed. Further, FIG. 1 shows a case where only the vehicle motion state quantity estimated value 300 is output, but a stability determination result and an update parameter may be output as necessary. The output value is not limited.
  • the vehicle state quantity is estimated using the detected value of the inertial sensor and the updated parameter.
  • the state quantity can be estimated.
  • FIG. 2 is a diagram showing a four-wheel vehicle model.
  • the center of gravity 11 of the vehicle is the origin
  • the longitudinal direction of the vehicle is x
  • the lateral direction of the vehicle is y
  • the vertical direction of the vehicle is z.
  • FIG. 2 shows the movement of a four-wheeled vehicle during a turn.
  • the actual steering angle is ⁇
  • the vehicle traveling speed is V
  • the vehicle longitudinal speed is V x
  • the vehicle lateral speed is V y
  • the slip angle ⁇ formed by the angle between the traveling direction and the longitudinal direction of the vehicle that occurs in a vehicle turning at a speed V
  • ⁇ fr , ⁇ fl , ⁇ rr , ⁇ rl be the tire side slip angles that are the angles formed by the front-rear direction
  • Y fr , Y fl , Y rr , Y rl be the cornering forces that act on these tires.
  • the yaw rate generated around the z-axis passing through the center of gravity 11 is r
  • the distance between the center of gravity 11 and the front wheel axis, and the distance between the rear wheel axis are l f and l r , respectively, and the distance between the front wheel axis and the rear wheel axis.
  • FIG. 3 is a diagram showing an equivalent two-wheeled vehicle model of a four-wheeled vehicle.
  • FIG. 3 shows that the left and right left and right wheels ignore the tread of the vehicle and the left and right front and rear wheels are in the range where the side slip angle of the left and right tires is smaller than that of FIG. It is replaced with a model that concentrates on the intersection with.
  • the cornering forces 2Y f and 2Y r are the resultant forces of the cornering forces acting on the left and right of the front and rear wheel tires shown in FIG.
  • FIG. 4 is a diagram illustrating a roll motion associated with the lateral acceleration acting on the barycentric point 11.
  • Figure 4 is a lateral acceleration G y is showing how vehicle roll angle ⁇ of the sprung mass m b occurs acting.
  • the roll stiffness which is the magnitude of the moment generated by the expansion and contraction of the front and rear suspensions per unit roll angle, is represented by K ⁇ f , K ⁇ r , and the height from the ground of the roll center, which is the geometric instantaneous rotation center of the vehicle body, is represented by h f.
  • the height of the center of gravity 11 is h
  • the distance between the roll shaft 12 connecting the front and rear roll centers and the center of gravity 11 is h ⁇
  • the spring constants of the front and rear suspensions including the tires are K sf , K sr
  • C sf and C sr be the damping coefficients of the front and rear suspensions including the tire.
  • FIG. 5 is a diagram illustrating pitch motion associated with longitudinal acceleration acting on the barycentric point 11.
  • Figure 5 is a graph showing how the pitch angle ⁇ occurs in the vehicle sprung mass m b acting longitudinal acceleration G x.
  • the pitch rigidity which is the magnitude of the moment generated by the expansion and contraction of the front and rear suspensions including the tire per unit pitch angle, is represented by K ⁇
  • the distance between the center of gravity 11 and the pitch axis 13 which is the geometrical instantaneous rotation center of the vehicle body. Let the distance be h ⁇ .
  • DV y / dt which is the time derivative of the lateral velocity V y
  • dr / dt which is the time derivative of the yaw rate r generated around the z-axis
  • (V y ⁇ , r ⁇ ) is an estimated value of (V y , r).
  • the observer input is corrected so that the deviation e decreases, and the state quantity estimation error is reduced.
  • An estimated value V y ⁇ of the lateral speed is obtained from the equations (3) and (4), and an estimated value ⁇ ⁇ of the side slip angle of the vehicle body can be calculated using the following equation (5).
  • the distance h ⁇ between the pitch axis 13 and the center of gravity 11 which is the center of instantaneous rotation is a parameter
  • the other longitudinal acceleration G x and yaw rate r are the detection values described in FIG.
  • these parameters have been defined based on driving tests and numerical analysis results at the time of vehicle design, taking into account robustness to cope with aging and environmental changes, etc., and used as constants for estimating state quantities, etc. It has been. However, since it is a constant considering the robustness, there is a problem that an estimation error always occurs. On the other hand, in the present invention, this parameter is treated as a variable, and the estimation error is reduced by updating to a value according to the actual vehicle based on the detection value of the external recognition means 2 described in FIG. .
  • the center of gravity by mass 16 mass m p is applied to the vehicle is a diagram showing a state that has moved to 14 from 15.
  • this is an example assuming that the mass point 16 is acting at a position where the roll angle ⁇ of the vehicle body does not occur.
  • a parameter based on a running test at the time of vehicle design, a result of numerical analysis, or the like is a design value
  • an update parameter is an update value
  • a symbol with an 'in the expression described below is an update value.
  • l f ′ and l r ′ are the distance between the center of gravity (update value) 15 and the front wheel axis, and the distance between the rear wheel axis, and x G is the center of gravity (design value) 14 and the center of gravity (update value) 15. the distance, x P is the distance between the mass point 16 and the center of gravity point (updated value) 15.
  • the vehicle mass and the sprung mass update value m ′, m b ′ can be calculated using an equation represented by the following equation (8).
  • m, m b is the mass and the sprung mass of the design value of the vehicle of the formula (8)
  • m P is the mass of mass point 16
  • z f, z r is a vehicle body front side, the rear vertical displacement, respectively, It can calculate using the equation represented by the following formula
  • the pitch angle ⁇ in the equation (9) is the vehicle motion state quantity detection value 200 detected by the external recognition means 2.
  • l f ′ and l r ′ in Expression (9) are updated values of the distance between the center of gravity (updated value) 15 and the front wheel shaft and the distance from the rear wheel shaft shown in FIG. It can be calculated using the equation represented by 10).
  • the equation (10) is established when the vehicle is turning at a low speed such that the vehicle speed V can be regarded as 0, and the side slip angle ⁇ of the vehicle body is the vehicle motion state amount detection value 200 detected by the external recognition means 2 and the actual steering angle ⁇ . Is a conversion value of the driver input amount detection value.
  • T in the equation (11) is a tire output torque
  • R is a tire radius.
  • the tire output torque T can be obtained by, for example, a method of calculating from a torque map of the engine or motor, a transmission gear ratio, efficiency, or the like, or a method of directly detecting the torque of the drive shaft using a torque sensor.
  • Equation (11) When Equation (11) is used in combination, two updated values m ′ and m b ′ of the vehicle mass and sprung mass are calculated, but when the pitch angle ⁇ changes, these values are equal. Therefore, when the pitch angle ⁇ does not change, the updated value according to the equation (11) becomes larger than the updated values according to the equations (8) to (10). The updated values m ′ and m b ′ of the sprung mass can be calculated.
  • the calculation method of the updated values m ′ and m b ′ of the vehicle mass and the sprung mass using the detection value of the external recognition means 2 is not limited to the method described above. Is stored in the vehicle state quantity estimating device 1 in advance, and the characteristic map representing the relationship between the detected value of the external recognition means 2 taking into account the updated value m ′ and m b ′ of the vehicle mass and the sprung mass is stored in advance.
  • the update values m ′ and m b ′ of the vehicle mass and sprung mass may be calculated by inputting the detection value of the external recognition means 2 into Furthermore, by adding an acceleration axis to the characteristic map, it is possible to calculate updated values m ′ and m b ′ of the vehicle mass and the sprung mass from which the influence of the gravitational acceleration due to the road surface inclination is removed.
  • the distance x G between the centroid point (design value) 14 and the centroid point (update value) 15 and the distance x P between the mass point 16 and the centroid point (update value) 15 shown in FIG. 6 are calculated.
  • the distance x G is the absolute value of the difference between the design value lr and the update value lr of the distance between the center of gravity and the rear wheel axle
  • the distance x P is the design value m of the vehicle mass centered on the center of gravity (update value) 15. From the balance of moments due to the mass m P of the mass point 16, it can be calculated using the equation represented by the following equation (13).
  • the calculation method of the center of gravity position such as the center of gravity height h using the detection value of the external environment recognition unit 2 is not limited to the method described above, and for example, the external environment recognition unit 2 considering the suspension geometry.
  • a characteristic map representing the relationship between the detected value and the center of gravity position may be stored in advance in the vehicle state quantity estimating device 1, and the center of gravity position may be calculated by inputting the detected value of the external field recognition means 2 to the characteristic map.
  • by adding an acceleration axis to the characteristic map it is possible to calculate the position of the center of gravity from which the influence of the gravitational acceleration due to the road surface inclination is removed.
  • the updated value I z ′ of the yaw moment of inertia can be calculated by an equation represented by the following formula (14) using the parallel axis theorem.
  • I z in equation (14) is the design value of the yaw moment of inertia.
  • the cornering power update values K f ′ and K r ′ can be calculated using an equation represented by the following equation (15).
  • FIG. 7 shows a configuration diagram of a vehicle 10 equipped with the vehicle state quantity estimating device 1 according to the embodiment of the present invention.
  • the vehicle state quantity estimation device 1 is mounted on a vehicle 10, and includes a state quantity related to vehicle motion and an operation angle sensor 5 from an external environment recognition means 2 such as a camera and GPS, an acceleration sensor 3, a gyro sensor 4, and a wheel speed sensor 6.
  • the detection value of the state quantity related to the driver operation is acquired from the above.
  • the vehicle state quantity estimation device 1 determines the stability of the external recognition means 2 using the detection value as described in FIG. 1, updates the parameter based on the determination result, and uses the updated parameter and detection value.
  • the side slip angle and lateral speed of the vehicle body are estimated, and the results are output to the drive control unit 8 and the brake control unit 9 that control the braking / driving force of the vehicle.
  • FIG. 8 is a flowchart showing a processing outline of the vehicle state quantity estimation device 1.
  • the vehicle state quantity estimation device 1 acquires the detected values of the vehicle motion state quantity and the driver operation quantity necessary for parameter update and state quantity estimation from the acceleration sensor 3 and the gyro sensor 4 (step S801).
  • the vehicle motion state quantity detection value 100 that is the detection value of the acceleration sensor 3 and the like acquired in step S801 is compared with the vehicle motion state quantity detection value 200 that is the detection value of the external recognition means 2, and based on the magnitude relationship. Then, it is determined whether or not the vehicle motion state quantity detection value 200 is stable, and the stability determination result is output (step S802).
  • step S802 the updated parameters such as the mass m and the yaw inertia moment I z of the vehicle based on the detection value using the above equations (8) to (18), the update A parameter is output (step S803).
  • step S803 the vehicle motion state detection value 100, and the driver operation amount detection value, the side slip angle ⁇ and the lateral speed of the vehicle body are calculated using the above formulas (1) to (7).
  • V y , roll angle ⁇ , and pitch angle ⁇ are estimated, and the vehicle motion state estimated value 300 is output to drive control unit 8 and brake control unit 9, and the process ends.
  • the output cycle of the detection value of the external recognition means 2 is slower than the output cycle of the detection value of an inertial sensor such as an acceleration sensor. Therefore, in order to prevent erroneous processing due to the difference in output cycle in stability determination and parameter update, which will be described later, the processing cycle is adjusted to the output cycle of the sensor with the slowest output cycle, or the detection value of the sensor with the slowest output cycle It is desirable to use a value obtained by performing time differentiation on the basis of the time derivative and predicting based on the time derivative value.
  • the process of evaluating the reliability of the update parameter based on the information acquired from the fuel gauge, the seating sensor, the seat belt sensor, etc., and determining whether to update the parameter based on the evaluation result is performed in steps S803 and S803. It may be added during S804.
  • FIG. 9 is a flowchart showing a processing outline of the stability determination unit 21 of the vehicle state quantity estimation device 1.
  • the yaw angle ⁇ which is the time integral value of the yaw rate r detected by the gyro sensor 4
  • the stability is based on the error of the yaw angle ⁇ ⁇ detected by the external field recognition means 2 with respect to the true value.
  • a method of determining the will be described.
  • the stability determination unit 21 acquires the yaw rate r from the gyro sensor 4 and the yaw angle ⁇ ⁇ from the external environment recognition unit 2 (step S901).
  • step S902 the yaw rate r acquired in step S901 is time-integrated to calculate the yaw angle ⁇ (step S902).
  • step S903 a yaw angle error that is the difference between the true value and the yaw angle ⁇ ⁇ acquired in step S901 is calculated (step S903).
  • step S904 it is determined whether or not the yaw angle error calculated in step S903 is small with respect to a predetermined threshold. If small (step S904, YES), the process proceeds to step S905 and the count value is set to a predetermined value.
  • step S906 a count reset process for setting the count value to 0 is performed.
  • step S907 it is determined whether or not the count value obtained by the count-up process in step S905 or the count reset process in step S906 is larger than a predetermined threshold. If the count value is larger (YES in step S907).
  • step S908 If the vehicle motion state quantity detection value 200 detected by the external environment recognition means 2 continues to be stable during a predetermined period and is determined to be reliable, the process proceeds to step S908 to output a stability determination, and if small (step S907, NO), the vehicle motion state quantity detection value 200 detected by the external environment recognition means 2 is determined to be unreliable and unreliable for a predetermined period, and the process proceeds to step S909 to output an instability determination and processing. Exit.
  • the method of determining the stability in steps S901 to S909 is not limited to the above-described processing by count-up, and for example, a determination method by count-down that subtracts a predetermined value from the count value, or external recognition A determination method based on information self-diagnosed by the means 2 may be used.
  • the stability determination target is not limited to the yaw angle error described above.
  • the yaw rate r detected by the gyro sensor 4 is set to the true value, and the true value and the yaw angle detected by the external recognition unit 2 are determined.
  • the yaw rate error which is the difference from the yaw rate r ⁇ obtained by time differentiation of, may be determined.
  • FIG. 10 is a flowchart showing an outline of processing of the parameter update unit 22 of the vehicle state quantity estimation device 1.
  • the parameter update unit 22 acquires the stability determination result that is the output of the stability determination unit 21 (step S1001).
  • step S1002 it is determined whether the stability determination result acquired in step S1001 is a stability determination or an instability determination (step S1002). If it is a stability determination (step S1002, YES), detection of the external recognition means 2 is performed. If it is determined that the value has reliability for updating the parameter, the process proceeds to step S1003. If the value is unstable (NO in step S1002), the reliability for updating the parameter to the detected value of the external recognition unit 2 is determined. The process is terminated without determining that the parameter is not updated.
  • step S1002 If it is determined in step S1002 that the detected value of the external environment recognizing means 2 is reliable for updating the parameter, the detected values of the vehicle motion state amount and the driver operation amount are acquired from the acceleration sensor 3 or the gyro sensor 4 or the like. (Step S1003), the process proceeds to step S1004. In steps S1004 to S1007, based on the detection value acquired in step S1003, the mass, the position of the center of gravity, the moment of inertia, and the tire characteristics are calculated using the above formulas (8) to (17), and the parameters are updated. The updated parameter is output and the process ends.
  • the parameter updating method is not limited to the above-described method.
  • the parameter may be updated from time-series data acquired by a sensor during traveling using an optimization method or a system identification method.
  • the parameter value range to be updated in the parameter updating unit 22 is set such that, for example, in the case of mass, the mass when empty is the lower limit value and the mass when maximum loading is the upper limit in order to prevent divergence of the updated value due to erroneous sensor detection. It is desirable to predefine a range that may change depending on each parameter, such as a value, and update within that range.
  • the storage form of the update parameter is not limited to a numerical value. For example, the tire characteristics based on the cornering forces Y f and Y r calculated using the equations (16) and (17) and the tire side slip angles ⁇ f and ⁇ r are used. You may save it as a map.
  • FIG. 11 is a diagram showing a detected value of the vehicle state quantity estimating apparatus 1, an error of the detected value, a count value of a stable period, and a time change of the estimated value and the detected value.
  • the detection value, the error of the detection value, and the count value of the stable period are examples of the processing result in the stability determination unit 21 described with reference to FIG.
  • the estimated value is an example of a result estimated by the state quantity estimating unit 23 using the update parameter output from the parameter updating unit 22 described in FIG.
  • the stability determination period shown in FIG. 11 is a period in which the detected value error (Q) is smaller than the threshold value (a) and the count value (J) is larger than the threshold value (b).
  • the parameter update unit 22 updates the parameters within the stability determination period, and stops the parameter update during other periods (unstable determination period).
  • the state quantity estimation unit 23 can output a highly accurate estimated value substantially equal to the true value in the stability determination period as in the conventional method.
  • the state quantity estimation unit 23 of the present embodiment performs estimation using the latest parameters updated in the stability determination period, the estimated value cannot be corrected by the detected value of the external field recognition unit 2 in the instability determination period. An estimated value closer to the true value than the method can be output.
  • a parameter that has been treated as a constant in the conventional method is treated as a variable, the parameter is updated to a value that matches the actual vehicle based on the detection value of the external recognition means 2, and estimation is performed using the updated parameter.
  • the estimation error can be reduced compared to the conventional method.
  • skid prevention device for example, when replacing a tire with a lower performance than at the time of design, or using a tire whose tire air pressure is reduced or worn, the skid force command of the skid prevention device is insufficient in the conventional method, and skidding increases. There was a risk that running stability would be reduced.
  • the vehicle state quantity estimation device 1 of the present embodiment when the vehicle state quantity estimation device 1 of the present embodiment is applied, the braking force command of the skid prevention device can be corrected based on the current tire characteristics, so the skid is reduced compared to the conventional method and the running stability is improved. Can be made.
  • the vehicle state quantity estimation device 1 of the present embodiment when the vehicle state quantity estimation device 1 of the present embodiment is applied, a highly accurate estimated value approximately equal to the true value is obtained, that is, a predicted value of the state quantity with respect to the driver's operation input is obtained. Accordingly, the braking force command of the skid prevention device can be corrected, and the side slip can be reduced as compared with the conventional method, and the running stability can be improved.
  • the assist of the power steering device does not change in the conventional method. Vehicle behavior was occurring.
  • the vehicle state quantity estimating device 1 of the present embodiment when the vehicle state quantity estimating device 1 of the present embodiment is applied, the assist force and the steering angle of the power steering device can be increased / decreased according to the current tire characteristics. The vehicle behavior equivalent to the time can be realized.
  • a decrease in tire air pressure or the like can be detected by comparing the estimated values of the nominal model and the latest parameter estimation model, and can be transmitted to the driver.
  • the braking force command of the electric booster device does not change in the conventional method. A braking force corresponding to the characteristics is generated.
  • the vehicle state quantity estimation device 1 of the present embodiment is applied, the braking force command of the electric booster device can be corrected according to the current tire characteristics. Equivalent braking force can be generated.
  • an appropriate braking force command can be generated based on the latest mass, so that power consumption can be reduced as compared with the conventional method.
  • FIG. 12 is a diagram showing the positional relationship of the center of gravity 11 of the vehicle with respect to the coordinate system fixed to the ground.
  • the trajectory of the center of gravity 11 of the vehicle is expressed by the following equation (18), where (X, Y) is the position of the center of gravity 11 of the vehicle with respect to the coordinate system fixed to the ground, and the yaw angle with respect to the X axis of the vehicle is ⁇ . Is done.
  • This equation (18) uses the detected value of the external recognition means 2 such as the speed V in the traveling direction of the vehicle and the side slip angle ⁇ of the vehicle body, or an estimated value calculated by equation (1) using a vehicle model. Further, in order to calculate the trajectory of the center of gravity 11 of the vehicle without using the detected value of the external recognition means 2 or the estimated value calculated using the vehicle model, for example, there is a method using the following equation (19). .
  • FIG. 13 is a diagram showing the trajectory of the center of gravity 11 during a steady circle turn of a vehicle whose cornering power has decreased due to replacement with a low-performance tire or a decrease in tire air pressure.
  • the true value (e) is the actual locus of the center of gravity point 11 of the vehicle calculated using the vehicle motion state quantity detection value 200 that is the detection value of the external environment recognition means 2 and the equation (18).
  • No vehicle model (f) is an estimated trajectory of the center of gravity 11 of the vehicle calculated using Expression (19).
  • “With vehicle model and without heel parameter update (g)” is an estimated value calculated using a vehicle model whose parameters are not updated and an estimated locus of the center of gravity 11 of the vehicle calculated using equation (18).
  • the vehicle model present and the saddle parameter not updated (h) are an estimated value calculated using the vehicle model with updated parameters and an estimated locus of the center-of-gravity point 11 of the vehicle calculated using Equation (18).
  • the estimated trajectory with the vehicle model and with the heel parameter updated (h) is approximately equal to the true value (e) that is the actual trajectory of the center of gravity point 11 of the vehicle, but with no vehicle model (f).
  • the estimation trajectory with the vehicle model and without the saddle parameter update (g) has a large estimation error.
  • the conventional method performs self-position estimation based on the detection value of the external recognition means, and there is a risk that automatic detection cannot be continued due to poor detection accuracy due to bad conditions such as rain or lens contamination. It was.
  • a method of continuing automatic driving in response to a decrease in the detection accuracy of the external recognition means a method of performing map matching based on information such as the vehicle trajectory estimated using Equation (18) or Equation (19) is considered.
  • a method with a large estimation error of the trajectory such as no vehicle model (f) or a vehicle model and no saddle parameter update (g).
  • the vehicle state quantity estimation apparatus 1 of the present embodiment is applied, the vehicle trajectory and the like can be estimated with high accuracy, so that automatic driving can be continued to at least a place where the vehicle can be safely stopped.
  • the main difference between the second embodiment and the first embodiment is the stability determination method in the stability determination unit 21.
  • the processing outline of the vehicle state quantity estimation device 1 in the second embodiment is described with reference to FIGS. Will be explained.
  • FIG. 14 is a flowchart showing a processing outline of the stability determination unit 21 of the vehicle state quantity estimation device 1 according to the second embodiment.
  • the processing in steps S1401 to S1403 in FIG. 14 is the same as that in steps S901 to S903 in FIG.
  • step S1404 a value obtained by time-integrating the yaw angle error calculated in step S1403 over a predetermined period is calculated.
  • step S1405 it is determined whether or not the integrated value of the yaw angle error calculated in step S1404 is smaller than a predetermined threshold (step S1405). If it is smaller (YES in step S1405), the vehicle detected by the external environment recognition unit 2 is determined.
  • step S1406 It is determined that the motion state quantity detection value 200 is continuously stable and reliable in a predetermined period, and the process proceeds to step S1406 to output a stability determination. If it is large (NO in step S1405), it is detected by the external recognition means 2. It is determined that the detected vehicle motion state quantity value 200 is not stable for a predetermined period and is not reliable, and the process proceeds to step S1407 to output an instability determination, and the process ends.
  • the stability determination method in steps S1404 and S1405 is not limited to the determination method based on the integral value described above, and may be a determination method based on an average value, for example.
  • FIG. 15 is a diagram illustrating a detected value of the vehicle state quantity estimation device 1 in the second embodiment, an error of the detected value, an integrated value of the detected value error, and a time change of the estimated value and the detected value.
  • the stability determination period shown in FIG. 15 is a period in which the integrated value (N) obtained by integrating the error (Q) of the detected value in a predetermined period is smaller than the threshold value (c), and the vehicle motion state quantity detected by the external recognition means 2 This is a period in which the stability determination unit 21 outputs a stability determination that the detection value 200 is continuously stable and reliable in a predetermined period.
  • the count value is reset in the vehicle state quantity estimation device 1 in the first embodiment, and the parameter update is stopped.
  • the actual vehicle use is determined by setting the period during which the magnitude of noise is small and the integral value (N) is smaller than the threshold value (c) as the stability determination period. Under the environment, it is possible to increase the parameter update frequency while ensuring a certain accuracy in the parameter update.
  • the main difference between the third embodiment, the first embodiment, and the second embodiment is a method for determining whether the parameter update unit 22 has updated or not updated parameters.
  • the vehicle in the third embodiment is described with reference to FIGS. 16 and 17. A processing outline of the state quantity estimation device 1 will be described.
  • FIG. 16 is a flowchart showing an outline of processing of the parameter update unit 22 of the vehicle state quantity estimation device 1 in the third embodiment.
  • the parameter update unit 22 according to the third embodiment acquires the vehicle motion state quantity detection value 200 detected by the external field recognition unit 2 before one processing cycle and the estimation value estimated by the state quantity estimation unit 23 (step S1601). .
  • one process is defined from START to END in the flowchart showing the outline of the process of the vehicle state quantity estimating apparatus 1 described in FIG.
  • an estimation error that is a difference between the detected value and the estimated value acquired in step S1601 is calculated (step S1602).
  • step S1603 If it is larger (step S1603, YES), it is determined that the accuracy of the estimated value is insufficient. In S1604, if small (NO in Step S1603), the accuracy of the estimated value is necessary and sufficient, and it is determined that the parameter update is unnecessary, and the process ends. In step S1604, the parameters are updated according to the flow described in FIG. 10, and the process ends.
  • FIG. 17 is a diagram illustrating a detected value of the vehicle state quantity estimating apparatus 1 in the third embodiment, an error in the detected value, a count value in a stable period, an estimated error, and a time change of the estimated value and the detected value.
  • the parameter update is stopped if the estimation error is smaller than the predetermined threshold (d) even during the stability determination period.
  • the main difference between the fourth embodiment and the first to third embodiments is that the vehicle state quantity output device 30 is configured by adding an output value determining unit 24 to the vehicle state quantity estimating device 1 of the first to third embodiments. Therefore, an outline of the process of the vehicle state quantity output device 30 according to the fourth embodiment will be described with reference to FIGS.
  • FIG. 18 is a conceptual diagram of the vehicle state quantity output device 30 according to the fourth embodiment.
  • the vehicle state quantity output device 30 has a configuration in which an output value determination unit 24 is added to the vehicle state quantity estimation device 1 of the first to third embodiments.
  • the output value determination unit 24 includes a vehicle motion state quantity estimated value 300 that is an output of the state amount estimation unit 23, a stability determination result that is an output of the stability determination unit 21, and a vehicle motion state detected by the external environment recognition unit 2. Based on the amount detection value 200, it is determined which of the vehicle motion state amount estimated value 300 and the vehicle motion state amount detection value 200 is output as the vehicle motion state amount output value 400.
  • FIG. 18 shows a case where only the vehicle motion state quantity output value 400 is output. However, if necessary, the stability determination result, the update parameter, and the vehicle motion state quantity estimated value 300 may be output.
  • the output value of the vehicle state quantity output device 30 is not limited.
  • FIG. 19 is a flowchart showing a processing outline of the output value determination unit 24 of the vehicle state quantity output device 30 according to the fourth embodiment.
  • the output value determination unit 24 acquires the stability determination result that is the output of the stability determination unit 21 (step S1901).
  • step S1902 it is determined whether the stability determination result acquired in step S1901 is a stability determination or an instability determination (step S1902). If it is a stability determination (step S1902, YES), detection of the external recognition means 2 is performed. It is determined that the value has reliability for updating the parameter, and the process proceeds to step S1903. If the value is unstable (NO in step S1902), the reliability for updating the parameter to the detected value of the external recognition unit 2 is determined.
  • step S1902 If it is determined that there is no possibility, the process advances to step S1907 to output an estimated value, and the process ends. If it is determined in step S1902 that the detected value of the external environment recognizing means 2 is reliable for updating the parameter, the vehicle motion state quantity detected value 200 detected by the external environment recognizing means 2 one processing cycle before, and the state quantity The estimated value estimated by the estimating unit 23 is acquired (step S1903).
  • one process is defined from START to END in the flowchart showing the outline of the process of the vehicle state quantity estimation device 1 described in FIG. 8 of the first embodiment.
  • an estimation error that is a difference between the detected value and the estimated value acquired in step S1903 is calculated (step S1904).
  • step S1905 it is determined whether or not the estimation error calculated in step S1904 is larger than a predetermined threshold (step S1905). If it is larger (step S1905, YES), it is determined that the accuracy of the estimated value is insufficient. The process proceeds to S1906 to output the detected value. If the detected value is small (NO in step S1905), it is determined that the accuracy of the estimated value is necessary and sufficient, and the process proceeds to step S1907 to output the estimated value and the process is terminated.
  • FIG. 20 is a diagram illustrating a detected value of the vehicle state quantity output device 30 in the fourth embodiment, an error in the detected value, a count value in a stable period, an estimation error, and a time change in the estimated value, the detected value, and the output value.
  • the vehicle state quantity output device 30 outputs the detected value as an output value when the estimation error is greater than the threshold (d) during the stability determination period, and the estimated value is output as the output value otherwise.
  • Output as.
  • the value closest to the true value in the vehicle state quantity output device 30 is obtained. Can be output.
  • the main difference between the fifth embodiment and the first to fourth embodiments is that a vehicle 10 ′ in which a suspension control unit 40 and a control suspension device 41 are added to the vehicle 10 of the first to fourth embodiments is configured.
  • a processing outline of the suspension control unit 40 in the fifth embodiment will be mainly described with reference to FIGS.
  • the vehicle state quantity estimation device 1 according to the fifth embodiment may be the vehicle state quantity output device 30.
  • FIG. 21 shows a configuration diagram of a vehicle 10 ′ equipped with the vehicle state quantity estimation device 1 or the vehicle state quantity output device 30 in the fifth embodiment.
  • FIG. 21 shows a configuration in which a suspension control unit 40 and a control suspension device 41 are added to FIG.
  • the control suspension device 41 is a damping force adjustment type shock absorber capable of adjusting a damping characteristic or an active suspension capable of adjusting a vertical force between a vehicle body and a wheel.
  • the suspension control unit 40 is a vehicle state required for riding comfort control, anti-roll control, and the like based on detection values of inertial sensors, gyro sensors, and the like, and updated parameters such as mass and center of gravity updated by the vehicle state quantity estimation device 1. The amount is estimated, and a control signal for controlling the damping characteristic or the vertical force of the control suspension device 41 is generated.
  • FIG. 22 is a conceptual diagram of a suspension control unit 40 that performs riding comfort control, which is one function of the control suspension device 41 in the fifth embodiment.
  • the suspension control unit 40 includes update parameters such as mass and center of gravity updated by the vehicle state quantity estimation device 1 and vehicle motion state quantity detection values 100 such as sprung vertical acceleration and unsprung vertical acceleration detected by an inertial sensor. Is entered.
  • the suspension control unit 40 includes a vertical speed estimation unit 43, a target damping force calculation unit 44, and a damping force map 45.
  • the vertical speed estimation unit 43 receives the update parameter of the vehicle state quantity estimation device 1 and the vehicle motion state quantity detected value 100 as input, and estimates the vertical speed of the spring and unsprung.
  • the target damping force calculation unit 44 calculates the target damping force of the control suspension device 41 based on the vertical speed estimated by the vertical speed estimation unit 42 and the vehicle motion state quantity detection value 100.
  • the damping force map 45 is map information of the characteristics of the control suspension device 41 stored in advance.
  • the control suspension device 41 receives the target damping force calculated by the target damping force calculation unit 44 and the vehicle motion state quantity detection value 100 as inputs.
  • the command current for controlling the is derived and output.
  • FIG. 23 is a conceptual diagram of a suspension control unit 40 that performs anti-roll control, which is one function of the control suspension device 41 in the fifth embodiment.
  • the suspension control unit 40 includes update parameters such as mass and center of gravity updated by the vehicle state quantity estimation device 1 and vehicle motion state quantity detection values 100 such as sprung vertical acceleration and unsprung vertical acceleration detected by an inertial sensor.
  • the driver input amount detection value such as the steering angle detected by the steering angle sensor is input.
  • the suspension control unit 40 mainly includes a vehicle motion model 46 and roll control gain and pitch control gain stored in advance.
  • the vehicle motion model 46 estimates the lateral acceleration of the vehicle using the update parameter of the vehicle state quantity estimation device 1, the vehicle motion state quantity detection value 100, and the driver input quantity detection value as inputs.
  • the suspension control unit 40 controls the control suspension device 41 based on the lateral jerk and roll control gain obtained by differentiating the lateral acceleration estimated by the vehicle motion model 46, and the absolute value and pitch control gain of the lateral acceleration estimated by the vehicle motion model 46. Calculate and output the command current to be controlled.
  • FIG. 24 is a conceptual diagram of a suspension control unit 40 that performs anti-dive control and anti-squat control, which are one function of the control suspension device 41 in the fifth embodiment.
  • the suspension control unit 40 is input with updated parameters such as mass and center of gravity updated by the vehicle state quantity estimation device 1 and vehicle motion state quantity detection values 100 such as brake master cylinder pressure, engine torque, and gear position. .
  • the suspension control unit 40 mainly includes a vehicle motion model 46 and a pitch control gain stored in advance.
  • the vehicle motion model 46 estimates the longitudinal acceleration of the vehicle using the update parameter of the vehicle state quantity estimation device 1 and the vehicle motion state quantity detection value 100 as inputs.
  • the suspension control unit 40 calculates and outputs a command current for controlling the control suspension device 41 based on the longitudinal jerk obtained by differentiating the longitudinal acceleration estimated by the vehicle motion model 46 and the pitch control gain.
  • FIG. 25 and FIG. 26 are examples of simulation results showing the effect of using the update parameter in the fifth embodiment as an input.
  • FIG. 25 and FIG. 26 both show the results of comparing the influence of mass update, which is an update parameter, on the riding comfort on a high-speed wavy road.
  • FIG. 25 shows the vertical acceleration PSD of the floor, Fr tower, and Rr tower. It is a figure which shows the time change of a floor up-down displacement, a pitch angle, and a roll angle.
  • the parameter update is particularly effective in the vertical acceleration of the Rr tower, compared with the parameter update using the parameters stored in the design in consideration of the robustness stored in advance (conventional method). PSD and pitch angle are small, and higher performance riding comfort control can be realized.
  • a command current for controlling the suspension can be generated based on vehicle state quantities such as the sprung vertical speed and lateral acceleration estimated with high accuracy using parameters such as the latest mass and center of gravity.
  • vehicle state quantities such as the sprung vertical speed and lateral acceleration estimated with high accuracy using parameters such as the latest mass and center of gravity.
  • Higher-performance suspension control can be realized than when design parameters are used in consideration of robustness stored in advance.
  • the main difference between the sixth embodiment and the fifth embodiment is that a vehicle 10 ′′ in which a vehicle height sensor 42 is added to the vehicle 10 ′ of the fifth embodiment is configured, and mainly using FIGS. 27 to 28.
  • a processing outline of the suspension control unit 40 in the sixth embodiment will be described.
  • FIG. 27 shows a configuration diagram of a vehicle 10 ′′ equipped with the vehicle state quantity estimating device 1 or the vehicle state quantity output device 30 according to the sixth embodiment.
  • FIG. 27 shows a vehicle height sensor 42 with respect to FIG. Is added.
  • the vehicle height sensor 42 detects the relative distance between the road surface and the vehicle body in the z-axis direction or the displacement of the vehicle suspension.
  • the suspension control unit 40 determines the damping characteristics or the vertical force of the control suspension device 41 based on the detection values of various sensors such as the vehicle height sensor 42 and the update parameters of the vehicle state quantity estimation device 1 or the vehicle state quantity output device 30. Control.
  • the vehicle height sensor 42 can directly detect the vertical displacements z fr , z fl , z rr , z rl on the right front side, left front side, right rear side, and left rear side of the vehicle body.
  • the mass update values m ′ and m b ′ can be calculated using an equation represented by the following equation (20).
  • the vehicle height sensor 42 can directly detect the vertical displacement compared to the vertical displacement calculated based on the pitch angle ⁇ detected by the external environment recognition means 2, and the vehicle state quantity estimating device 1 or the vehicle is highly accurate.
  • the state quantity output device 30 can implement highly accurate parameter updating and high-performance suspension control using the parameter updating. Further, since the mass, the position of the center of gravity, and the moment of inertia can be calculated from the detected values of the external recognition means 2 and the vehicle height sensor 42, even if one of these parameters fails, the vehicle state quantity estimating device 1 or The parameter update by the vehicle state quantity output device 30 can be continued.
  • FIG. 28 is a diagram illustrating the vertical displacement generated in the vehicle on which the mass point of mass m P acts.
  • FIG. 28 shows a state in which the vertical displacements z fr , z fl , z rr , and z rl of the right front side, the left front side, the right rear side, and the left rear side of the vehicle body on which the mass point of mass m P acts are generated.
  • the roll angle ⁇ and the pitch angle ⁇ of the vehicle body can be calculated using an equation expressed by the following equation (21). In this embodiment, in order to facilitate understanding, it is assumed that the tread widths of the vehicle front and rear wheels are equal d.
  • the presence / absence of a sensor failure and the reliability of the detected value can be determined by comparing the roll angle ⁇ and the pitch angle ⁇ calculated using the equation (21) with the detected value of the external recognition means 2.
  • Example 7 differences from Example 5 and Example 6 will be described, and the same description as Example 5 and Example 6 will be omitted.
  • the main difference between the seventh embodiment, the fifth embodiment, and the sixth embodiment is that the vehicle state quantity estimating device 1 determines whether the parameter can be updated based on the tire ground contact load calculated by the suspension control unit 40.
  • a processing outline of the suspension control unit 40 and the vehicle state quantity control device 1 or the vehicle state quantity output device 30 in the seventh embodiment will be mainly described with reference to FIGS. 29 to 33.
  • FIG. 29 is a conceptual diagram of the vehicle state quantity estimation device 1 ′ in the seventh embodiment.
  • the vehicle state quantity estimation device 1 ′ inputs the tire ground contact load calculation value calculated by the suspension control unit 40 to the parameter update unit 22 of the vehicle state quantity estimation device 1 of the first to third embodiments. It is configured to do.
  • the cornering powers K f and K r described in the equation (15) and the like change in magnitude depending on the ground load. Therefore, in order to improve the estimation accuracy of the vehicle motion state quantity estimated value 300, the ground load calculated value is the state. It is desirable that the input is input to the quantity estimation unit 23.
  • the ground load calculation value may be input to the parameter updating unit 22 of the vehicle state quantity output device 30 of the fourth embodiment.
  • FIG. 30 is a diagram showing a 1 ⁇ 4 vehicle model with one degree of freedom.
  • Figure 30 is one in which the sprung mass m b showed how the vertical displacement by the unsprung vertical displacement z t of the mass m t.
  • the suspension spring constant is K s
  • the suspension damping coefficient is C s
  • the vertical displacement on the spring is z b
  • the vertical displacement on the spring is z t
  • the tire ground load is W 0 .
  • the unsprung and unsprung movements are expressed by the following equations (22) and (23), respectively.
  • the command current of the suspension control unit 40 is used in the case of a damping force adjustment type shock absorber in which the control suspension device 41 can adjust the damping characteristics. Enter the damping factor based. Further, in the case of an active suspension capable of adjusting the vertical force between the vehicle body and the wheel, the right side of the equations (22) and (23) is replaced with the vertical force derived by the suspension control unit 40.
  • d 2 z b / dt 2 and d 2 z t / dt 2 are not limited to the detected values of the vertical acceleration sensor installed on the spring and under the spring and the estimated value based on them, for example, the acceleration sensor 3 or an estimated value based on a detected value of the vehicle height sensor 42 or the like.
  • ⁇ W 0 is the amount of change in the contact load
  • W 0 is expressed by the following equation (24) in which this change is added to the contact load at rest.
  • the ground contact load of the tire can be calculated by the above method.
  • FIG. 31 is a flowchart showing a processing outline of the parameter update unit 22 of the vehicle state quantity estimation device 1 ′ in the seventh embodiment.
  • the ground load variation frequency and the ground load variation difference are calculated in correcting the parameters.
  • the ground load variation frequency and the difference in ground load variation are calculated in order to derive a correction gain for correcting parameters such as ground load vibration caused by a rough road surface and missing ground load caused by road surface depression.
  • FIG. 32 is a diagram illustrating load movement accompanying acceleration acting on the barycentric point 11.
  • Figure 32 shows how the variation amount [Delta] W fl ground load on each tire of the vehicle sprung mass m b acting longitudinal acceleration G x and the lateral acceleration G y, ⁇ W fr, ⁇ W rl , the [Delta] W rr occur It is.
  • FIG. 32 is an example assuming that the roll angle ⁇ and the pitch angle ⁇ of the vehicle body do not occur so as to facilitate understanding.
  • the roll angle ⁇ and the pitch of the vehicle body are illustrated. It is desirable to consider the load movement associated with the angle ⁇ .
  • FIG. 33 is a diagram showing acceleration acting on the gravity center point 11 of the vehicle traveling on the bank road having the cant angle ⁇ . Note that FIG. 33 is an example on the assumption that the roll angle ⁇ of the vehicle body and the side slip angle ⁇ of the vehicle do not occur so that the understanding can be easily understood. It is desirable to calculate load transfer.
  • Equation (25) the first term on the right side of Equation (25) is the amount of ground load variation associated with the longitudinal acceleration Gx
  • the second term on the right side is the amount of ground load variation associated with the lateral acceleration Gy
  • the third term on the right side is the vertical acceleration caused by the cant angle ⁇ . This is the amount of change in grounding load due to change.
  • the cant angle ⁇ used in the equation (25) can be calculated by an equation represented by the following equation (26).
  • the ground load fluctuation differences ⁇ W FY , ⁇ W RY , ⁇ W LX , ⁇ W RX are the latest ground loads inputted from the suspension control unit 40, W fl , W fr , W rl , W rr , the latest ground contact at rest. Assuming that the loads are W fl0 , W fr0 , W rl0 , W rr0 , and using the estimated values ⁇ W fl , ⁇ W fr , ⁇ W rl , ⁇ W rr of ground contact load fluctuation amounts calculated from the equations (25), (26), (27)
  • the first expression of Expression (27) is the difference between the calculated value and the estimated value of the contact load fluctuation amount on the left and right front wheels
  • the second expression is the difference between the calculated value and the estimated value of the contact load fluctuation amount on the left and right of the rear wheel
  • the formula is the difference between the calculated value and the estimated value of the ground load variation before and after the left wheel
  • the fourth formula is the difference between the calculated value and the estimated value of the ground load variation before and after the right wheel.
  • the parameter updating unit 22 acquires the contact load calculation value calculated by the suspension control unit 40 (step S3101).
  • one process is defined from START to END in the flowchart showing the outline of the process of the vehicle state quantity estimating apparatus 1 described in FIG.
  • the variation frequency of the ground load is calculated by performing FFT processing on the ground load calculated value acquired in step S3001 (step S3102).
  • a ground load variation estimation value is calculated based on detected values such as longitudinal acceleration G x , lateral acceleration G y , and yaw rate r, and equations (25) and (26) (step S3103).
  • a ground load variation difference is calculated based on the estimated value of the ground load variation calculated in step S3103, the calculated ground load obtained in step S3101, and equation (27) (step S3104).
  • a correction gain is calculated based on the ground load fluctuation frequency calculated in step S3102 and the ground load fluctuation difference calculated in step S3104.
  • a method of calculating the correction gain a method of deriving based on a correction gain map stored in advance with the ground load fluctuation frequency and the ground load fluctuation difference as inputs can be considered. There is no limitation on the method to be performed.
  • step S3105 the parameter is corrected using the correction gain calculated in step S3105.
  • a correction method a method of multiplying a parameter by a correction gain is conceivable, but a method of division may be used, and the method of correcting the parameter by the correction gain is not limited.
  • 1 vehicle state quantity estimation device
  • 2 external recognition means
  • 3 acceleration sensor
  • 4 gyro sensor
  • 5 steering angle sensor
  • 6 wheel speed sensor
  • 7 tire
  • 8 drive control unit
  • 9 brake control Unit: 10
  • 10 ′ Vehicle
  • 11 Center of gravity
  • 12 Roll axis
  • 13 Pitch axis
  • 14 Center of gravity (design value)
  • 15 Center of gravity (updated value)
  • 16 Mass
  • 21 Stability Determination unit
  • 22 parameter update unit
  • 23 state quantity estimation unit
  • 24 output value determination unit
  • 30 vehicle state quantity output device
  • 40 suspension control unit
  • 41 control suspension device
  • 42 vehicle height sensor
  • 43 Vertical velocity estimation unit
  • 44 target damping force calculation unit
  • 45 damping force map
  • 46 vehicle motion model
  • 100 vehicle state quantity detection value
  • 200 vehicle state quantity detection value detection value

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Abstract

The purpose of the present invention is to provide a vehicle state quantity estimation device capable of estimating with high accuracy a vehicle state quantity using the detection values of an external recognition means, such as a camera or GPS. This vehicle state quantity estimation device (1) comprises: inertial sensors (3), (4) which detect acceleration, angular velocity or the like; a state quantity estimation unit (23) which estimates the state quantity of the vehicle on the basis of the detection values of the inertial sensors and preset parameters, such as the mass or the position of the center of gravity; and an external recognition means (2) for detecting the relative displacement, angle or the like between structures around the vehicle and the vehicle. The parameters are updated while the detection values of the external recognition means are stable.

Description

車両状態量推定装置Vehicle state quantity estimation device
 本発明は、車両の状態量を推定する車両状態量推定装置に関する。 The present invention relates to a vehicle state quantity estimating device for estimating a state quantity of a vehicle.
 センサでの直接検出が困難な車体の横すべり角や横方向速度、ロール角などの状態量は、慣性センサなどの検出値と車両運動モデルを用いて推定しているが、モデルパラメータの誤差などによって推定誤差が生じる。この推定誤差を小さくするため、従来はオブザーバやカルマンフィルタを用いた推定値の補正の他、例えば特許文献1に記載されているようなカメラやGPSなどの外界認識手段を用いて直接検出した相対ヨー角を用いて推定横すべり角を補正する車両状態量の推定方法が知られている。 State quantities such as side slip angle, lateral speed, and roll angle, which are difficult to detect directly with a sensor, are estimated using detected values from an inertial sensor and a vehicle motion model. An estimation error occurs. In order to reduce this estimation error, conventionally, in addition to correction of an estimated value using an observer or a Kalman filter, for example, a relative yaw detected directly using an external recognition means such as a camera or GPS as described in Patent Document 1 is used. A vehicle state quantity estimation method that corrects an estimated side slip angle using a corner is known.
特許第5402244号Japanese Patent No. 5402244
 しかしながら、特許文献1に記載された車両状態量の推定方法では、良好な環境条件であれば高精度な推定値が得られるものの、雨天などの外界認識手段が検出困難な悪条件では推定値を補正することができず、精度が大幅に低下する可能性がある。 However, in the estimation method of the vehicle state quantity described in Patent Document 1, a high-precision estimated value can be obtained under good environmental conditions, but the estimated value is obtained under bad conditions that are difficult for the outside recognition means such as rain to be detected. There is a possibility that the correction cannot be made and the accuracy is greatly lowered.
 本発明は、前記の課題を解決するための発明であって、外界認識手段が検出困難な悪条件であっても車体の横すべり角や横方向速度、ロール角などの状態量を高精度に推定できる車両状態量推定装置を提供することを目的とする。 The present invention is an invention for solving the above-mentioned problems, and accurately estimates state quantities such as a side slip angle, a lateral speed, and a roll angle of a vehicle body even under adverse conditions that are difficult to detect by an external recognition means. An object of the present invention is to provide a vehicle state quantity estimating device.
 前記目的を達成するため、本発明の車両状態量推定装置は、慣性センサの検出値と、外界認識手段の検出値が入力され、該慣性センサの検出値と予め設定されている車両パラメータに基づいて車両の状態量を出力する車両状態量推定装置において、前記外界認識手段の検出値を用いて、前記車両パラメータを更新することを特徴する。 In order to achieve the above object, the vehicle state quantity estimating device according to the present invention receives the detection value of the inertial sensor and the detection value of the external recognition means, and based on the detection value of the inertial sensor and a preset vehicle parameter. In the vehicle state quantity estimation device that outputs the vehicle state quantity, the vehicle parameter is updated using a detection value of the external environment recognition means.
 本発明によれば、車両状態量を高精度に推定することができる。 According to the present invention, the vehicle state quantity can be estimated with high accuracy.
車両状態量推定装置1の概念図。The conceptual diagram of the vehicle state quantity estimation apparatus 1. FIG. 4輪車モデルを示す図。The figure which shows a four-wheeled vehicle model. 4輪車の等価的な2輪車モデルを示す図。The figure which shows the equivalent two-wheeled vehicle model of a four-wheeled vehicle. 重心点11に作用する横加速度に伴うロール運動を示す図。The figure which shows the roll motion accompanying the lateral acceleration which acts on the gravity center point 11. FIG. 重心点11に作用する前後加速度に伴うピッチ運動を示す図。The figure which shows the pitch motion accompanying the longitudinal acceleration which acts on the gravity center point. 重心点などの位置関係を示す図。The figure which shows positional relationships, such as a gravity center point. 実施形態1に係る車両状態量推定装置1を搭載した車両構成を示す図。The figure which shows the vehicle structure carrying the vehicle state quantity estimation apparatus 1 which concerns on Embodiment 1. FIG. 実施形態1に係る車両状態量推定装置1による処理概要を示すフローチャート。5 is a flowchart showing an outline of processing by the vehicle state quantity estimation device 1 according to the first embodiment. 実施形態1に係る車両状態量推定装置1による安定度判断を示すフローチャート。5 is a flowchart showing stability determination by the vehicle state quantity estimation device 1 according to the first embodiment. 実施形態1に係る車両状態量推定装置1によるパラメータ更新を示すフローチャート。5 is a flowchart showing parameter update by the vehicle state quantity estimation device 1 according to the first embodiment. 実施形態1に係る車両状態量推定装置1による処理結果の時間変化を示す図。The figure which shows the time change of the process result by the vehicle state quantity estimation apparatus 1 which concerns on Embodiment 1. FIG. 実施形態1に係る地面に固定した座標系に対する重心点11の位置関係を示す図。The figure which shows the positional relationship of the gravity center point 11 with respect to the coordinate system fixed to the ground concerning Embodiment 1. FIG. 実施形態1に係る定常円旋回時の重心点11の軌跡を示す図。The figure which shows the locus | trajectory of the gravity center point 11 at the time of the steady circle turning which concerns on Embodiment 1. FIG. 実施形態2に係る車両状態量推定装置1による安定度判断を示すフローチャート。7 is a flowchart showing stability determination by the vehicle state quantity estimation device 1 according to the second embodiment. 実施形態2に係る車両状態量推定装置1による処理結果の時間変化を示す図。The figure which shows the time change of the processing result by the vehicle state quantity estimation apparatus 1 which concerns on Embodiment 2. FIG. 実施形態3に係る車両状態量推定装置1によるパラメータ更新を示すフローチャート。10 is a flowchart showing parameter update by the vehicle state quantity estimation device 1 according to the third embodiment. 実施形態3に係る車両状態量推定装置1による処理結果の時間変化を示す図。The figure which shows the time change of the processing result by the vehicle state quantity estimation apparatus 1 which concerns on Embodiment 3. FIG. 実施形態4に係る車両状態量出力装置30の概念図。The conceptual diagram of the vehicle state quantity output device 30 which concerns on Embodiment 4. FIG. 実施形態4に係る車両状態量出力装置30による出力値判断を示すフローチャート。10 is a flowchart showing output value determination by a vehicle state quantity output device 30 according to the fourth embodiment. 実施形態4に係る車両状態量出力装置30による処理結果の時間変化を示す図。The figure which shows the time change of the process result by the vehicle state quantity output device 30 which concerns on Embodiment 4. FIG. 実施形態5に係る車両状態量推定装置1あるいは車両状態量出力装置30を搭載した車両構成を示す図。The figure which shows the vehicle structure carrying the vehicle state quantity estimation apparatus 1 or the vehicle state quantity output device 30 which concerns on Embodiment 5. FIG. 実施形態5に係る制御サスペンション装置41の1機能である乗心地制御を行うサスペンション制御ユニット40の概念図。FIG. 9 is a conceptual diagram of a suspension control unit 40 that performs riding comfort control, which is one function of a control suspension device 41 according to a fifth embodiment. 実施形態5に係る制御サスペンション装置41の1機能であるアンチロール制御を行うサスペンション制御ユニット40の概念図。FIG. 10 is a conceptual diagram of a suspension control unit 40 that performs anti-roll control, which is one function of a control suspension device 41 according to a fifth embodiment. 実施形態5に係る制御サスペンション装置41の1機能であるアンチダイブ制御およびアンチスクワット制御を行うサスペンション制御ユニット40の概念図。FIG. 9 is a conceptual diagram of a suspension control unit 40 that performs anti-dive control and anti-squat control, which are one function of a control suspension device 41 according to a fifth embodiment. 実施形態5に係るサスペンション制御ユニットの処理結果の加速度PSDを示す図。FIG. 10 is a diagram showing an acceleration PSD as a processing result of a suspension control unit according to a fifth embodiment. 実施形態5に係るサスペンション制御ユニットの処理結果の時間変化を示す図。FIG. 10 is a view showing a change over time of a processing result of a suspension control unit according to a fifth embodiment. 実施形態6に係る車両状態量推定装置1あるいは車両状態量出力装置30を搭載した車両構成を示す図。The figure which shows the vehicle structure carrying the vehicle state quantity estimation apparatus 1 or the vehicle state quantity output device 30 which concerns on Embodiment 6. FIG. 実施形態6に係る質量mの質点が作用した車体の上下変位を示す図。It shows a vertical displacement of the vehicle body mass point of mass m p according to the sixth embodiment is applied. 実施形態7に係る車両状態量推定装置1’の概念図。The conceptual diagram of the vehicle state quantity estimation apparatus 1 'which concerns on Embodiment 7. FIG. 実施形態7に係る2自由度の1/4車両モデルを示す図。The figure which shows the 1/4 vehicle model of 2 freedom degree which concerns on Embodiment 7. FIG. 実施形態7に係る車両状態量推定装置1’によるパラメータ更新を示すフローチャート。18 is a flowchart showing parameter update by the vehicle state quantity estimation device 1 ′ according to the seventh embodiment. 実施形態7に係る重心点11に作用する加速度に伴う荷重移動を示す図。The figure which shows the load movement accompanying the acceleration which acts on the gravity center point 11 which concerns on Embodiment 7. FIG. 実施形態7に係るカント角σのバンク路を走行する車両の重心点11に作用する加速度を示す図。FIG. 10 is a diagram illustrating acceleration acting on a gravity center point 11 of a vehicle traveling on a bank road having a cant angle σ according to the seventh embodiment.
 本発明を実施するための実施形態について、適宜図面を参照しながら詳細に説明する。 Embodiments for carrying out the present invention will be described in detail with reference to the drawings as appropriate.
 図1~図5を用いて、本発明における外界認識手段の検出値の安定度判断、前記検出値に基づくパラメータ更新や、前記パラメータを用いた車両状態量推定の方法について説明する。 1 to 5, a method for determining the stability of the detected value of the external recognition means, updating the parameter based on the detected value, and estimating the vehicle state quantity using the parameter according to the present invention will be described.
 図1は、外界認識手段2の検出値の安定度判断や、前記検出値に基づくパラメータ更新や、前記パラメータを用いた車両状態量推定を行う車両状態量推定装置1の概念図である。 FIG. 1 is a conceptual diagram of a vehicle state quantity estimation device 1 that performs stability determination of a detection value of the external environment recognition unit 2, parameter update based on the detection value, and vehicle state quantity estimation using the parameter.
 車両状態量推定装置1には、例えば、慣性センサやジャイロセンサで検出した第一の車両運動状態量検出値100や、舵角センサやストロークセンサなどで検出したドライバ入力量検出値や、外界認識手段で検出した第二の車両運動状態量検出値200が入力される。そして、入力された信号に基づき、車両運動量状態量推定値300を出力する。 The vehicle state quantity estimation device 1 includes, for example, a first vehicle motion state quantity detection value 100 detected by an inertia sensor or a gyro sensor, a driver input quantity detection value detected by a steering angle sensor, a stroke sensor, or the like, or external environment recognition. The second vehicle motion state detection value 200 detected by the means is input. And based on the input signal, the vehicle momentum state quantity estimated value 300 is output.
 ここで、第一の車両運動状態量検出値100は、車輪速や車体の前後加速度、横加速度、ヨーレートなどの値である。ドライバ入力量検出値は、操舵角やアクセル開度、ブレーキ踏力などの値である。また、第二の車両運動状態量検出値200は、外界認識手段2で検出した車体の横すべり角や横方向速度、ヨー角、ロール角、ピッチ角などの値である。また、車両運動状態量推定値300は、車体の横すべり角や横方向速度、ロール角、ピッチ角などである。 Here, the first vehicle motion state quantity detection value 100 is a value such as a wheel speed, a longitudinal acceleration of the vehicle body, a lateral acceleration, or a yaw rate. The driver input amount detection value is a value such as a steering angle, an accelerator opening degree, or a brake depression force. The second vehicle motion state quantity detection value 200 is a value such as a side slip angle, a lateral speed, a yaw angle, a roll angle, or a pitch angle of the vehicle body detected by the external environment recognition unit 2. Further, the vehicle motion state quantity estimated value 300 is a side slip angle, a lateral speed, a roll angle, a pitch angle, or the like of the vehicle body.
 車両状態量推定装置1は、安定度判断部21と、パラメータ更新部22と、状態量推定部23を備える。車両状態量推定装置1は、安定度判断部21と、パラメータ更新部22と、状態量推定部23を備える。 The vehicle state quantity estimation device 1 includes a stability determination unit 21, a parameter update unit 22, and a state quantity estimation unit 23. The vehicle state quantity estimation device 1 includes a stability determination unit 21, a parameter update unit 22, and a state quantity estimation unit 23.
 状態量推定部23には、予め設定された車両のパラメータが記憶されており、車両運動状態量検出値100とパラメータを用いて、車両状態量推定値300を算出する。 The state quantity estimation unit 23 stores preset vehicle parameters, and calculates a vehicle state quantity estimation value 300 using the vehicle motion state quantity detection value 100 and the parameters.
 安定度判断部21は、外界認識手段2や、慣性センサからの入力値を用いて外界認識手段2からの入力値である第二の車両運動状態量検出値200が安定しているかを判断し、その判断結果を出力する。 The stability determination unit 21 determines whether the second vehicle motion state quantity detection value 200 that is an input value from the external environment recognition unit 2 is stable by using an input value from the external environment recognition unit 2 or the inertial sensor. The judgment result is output.
 パラメータ更新部22は、外界認識手段2や慣性センサからの入力値および安定度判断部21からの判断結果を用いて、状態量推定部23に記憶されているパラメータの更新を行う。具体的には、外界認識手段2で検出した車両運動状態量検出値200と、慣性センサやパラメータを用いて状態量推定部で算出した推定値の値が同じとなるように車両パラメータを更新する。言い換えると、パラメータを更新することにより、状態量推定部23で求めた推定値を外界認識手段で求めた実測値により同定している。 The parameter update unit 22 updates the parameters stored in the state quantity estimation unit 23 using the input values from the external environment recognition unit 2 and the inertial sensor and the determination result from the stability determination unit 21. Specifically, the vehicle parameter is updated so that the vehicle motion state quantity detection value 200 detected by the external environment recognition unit 2 is the same as the estimated value calculated by the state quantity estimation unit using an inertial sensor or parameter. . In other words, by updating the parameters, the estimated value obtained by the state quantity estimating unit 23 is identified by the actually measured value obtained by the external field recognition means.
 ここで、本発明におけるパラメータ更新とは、人の乗り降りやタイヤ交換などによって変化する車両の質量や重心位置、慣性モーメント、コーナリングパワーなどのパラメータを入力値に基づいて算出し、記憶されているパラメータを、算出したパラメータに置き換えることである。 Here, the parameter update in the present invention means that parameters such as vehicle mass, position of the center of gravity, moment of inertia, cornering power, etc. that change due to people getting on and off, changing tires, etc. are calculated based on input values, and stored parameters Is replaced with the calculated parameter.
 車両パラメータは、人の乗り降りやタイヤ交換、タイヤの劣化などによって変化する。そのため、予め記憶されている車両パラメータと実際の車両パラメータに差異が生じ、この差異が車両運動状態量推定値300の誤差となる。本発明は、外界認識手段2の検出値と、状態量推定部23での推定値とが合うようにパラメータを更新し、更新したパラメータと慣性センサの値に基づいて車両運動状態量推定値300を算出している。そのため、車両パラメータの変動に対応可能となり、横すべり角や横方向速度、ロール角などの慣性センサでは直接検出が難しい状態量を、慣性センサの値を用いて高精度に推定できる。 Vehicle parameters change depending on people getting on and off, changing tires, and tire deterioration. Therefore, a difference occurs between the vehicle parameter stored in advance and the actual vehicle parameter, and this difference becomes an error of the estimated value 300 of the vehicle motion state. In the present invention, the parameter is updated so that the detected value of the external environment recognition unit 2 matches the estimated value in the state quantity estimating unit 23, and the vehicle motion state quantity estimated value 300 is based on the updated parameter and the value of the inertial sensor. Is calculated. Therefore, it becomes possible to cope with fluctuations in vehicle parameters, and state quantities that are difficult to detect directly with an inertial sensor such as a side slip angle, a lateral speed, and a roll angle can be estimated with high accuracy using the value of the inertial sensor.
 ここで、状態量推定部23における推定精度は、この更新パラメータの精度に左右されるため、更新に用いる車両運動状態量検出値200が安定している、つまり信頼性が高い値のみを用いることが好ましいので、安定度判断部21における車両運動状態量検出値200の安定度判断を行う。また、図1には車両運動状態量推定値300のみを出力する場合を記載しているが、必要に応じて安定度判断結果や更新パラメータを出力しても良く、車両状態量推定装置1の出力値は限定しない。 Here, since the estimation accuracy in the state quantity estimation unit 23 depends on the accuracy of the update parameter, the vehicle motion state quantity detection value 200 used for the update is stable, that is, only a highly reliable value is used. Therefore, the stability determination of the vehicle motion state quantity detection value 200 in the stability determination unit 21 is performed. Further, FIG. 1 shows a case where only the vehicle motion state quantity estimated value 300 is output, but a stability determination result and an update parameter may be output as necessary. The output value is not limited.
 外界認識手段2が検出困難な状況であっても、慣性センサの検出値と更新したパラメータを用いて車両の状態量を推定しているため、本発明によれば、従来よりも精度よく車両の状態量が推定可能である。 Even in a situation where the external recognition means 2 is difficult to detect, the vehicle state quantity is estimated using the detected value of the inertial sensor and the updated parameter. The state quantity can be estimated.
 図2~図5を用いて、状態量推定部23における車両運動状態量の推定方法、パラメータ更新部22におけるパラメータ算出方法の具体例を説明する。 A specific example of the vehicle motion state quantity estimation method in the state quantity estimation unit 23 and the parameter calculation method in the parameter update unit 22 will be described with reference to FIGS.
 最初に、状態量推定部23における車両運動状態量の推定方法の一例を説明する。 First, an example of a vehicle motion state quantity estimation method in the state quantity estimation unit 23 will be described.
 図2は、4輪車モデルを示す図である。本実施例では車両の重心点11を原点とし、車両の前後方向をx、車両の左右方向をy、車両の上下方向をzとする。図2は旋回中の4輪車の運動を示したものであり、実舵角をδ、車両の進行方向の速度をV、車両の前後方向の速度をV、車両の左右方向の速度をV、速度Vで旋回する車両に生じる進行方向と車体前後方向のなす角を横すべり角β、前輪右タイヤ、前輪左タイヤ、後輪右タイヤ、後輪左タイヤのそれぞれの移動速度方向とタイヤ前後方向が成す角であるタイヤの横すべり角をβfr、βfl、βrr、βrl、これらタイヤに働くコーナリングフォースをYfr、Yfl、Yrr、Yrlとする。また、重心点11を通るz軸周りに生じるヨーレートをr、重心点11と前輪軸との距離、および後輪軸との距離をそれぞれl、l、前輪軸と後輪軸との距離であるホイールベースをl、車両前後輪のトレッド幅をそれぞれd、dとする。 FIG. 2 is a diagram showing a four-wheel vehicle model. In this embodiment, the center of gravity 11 of the vehicle is the origin, the longitudinal direction of the vehicle is x, the lateral direction of the vehicle is y, and the vertical direction of the vehicle is z. FIG. 2 shows the movement of a four-wheeled vehicle during a turn. The actual steering angle is δ, the vehicle traveling speed is V, the vehicle longitudinal speed is V x , and the vehicle lateral speed is V y , the slip angle β formed by the angle between the traveling direction and the longitudinal direction of the vehicle that occurs in a vehicle turning at a speed V, the front wheel right tire, the front wheel left tire, the rear wheel right tire, the rear wheel left tire, and the respective moving speed directions and tires Let β fr , β fl , β rr , β rl be the tire side slip angles that are the angles formed by the front-rear direction, and let Y fr , Y fl , Y rr , Y rl be the cornering forces that act on these tires. Further, the yaw rate generated around the z-axis passing through the center of gravity 11 is r, the distance between the center of gravity 11 and the front wheel axis, and the distance between the rear wheel axis are l f and l r , respectively, and the distance between the front wheel axis and the rear wheel axis. wheelbase l, respectively d f a tread width of the vehicle front and rear wheels, and d r.
 図3は、4輪車の等価的な2輪車モデルを示す図である。図3は、図2に対して左右タイヤの横すべり角が小さく、かつその値が小さく、実舵角も小さいとみなした範囲において、車両のトレッドを無視して前後の左右輪が前後輪軸と車軸との交点に集中しているモデルに置き換えたものである。ここでコーナリングフォース2Y、2Yは、図2に示した前後輪タイヤの左右に働くコーナリングフォースの合力である。 FIG. 3 is a diagram showing an equivalent two-wheeled vehicle model of a four-wheeled vehicle. FIG. 3 shows that the left and right left and right wheels ignore the tread of the vehicle and the left and right front and rear wheels are in the range where the side slip angle of the left and right tires is smaller than that of FIG. It is replaced with a model that concentrates on the intersection with. Here, the cornering forces 2Y f and 2Y r are the resultant forces of the cornering forces acting on the left and right of the front and rear wheel tires shown in FIG.
 図4は、重心点11に作用する横加速度に伴うロール運動を示す図である。図4は、横加速度Gが作用するばね上質量mの車両にロール角φが生じる様子を示したものである。ここで単位ロール角あたりの前後サスペンションの伸縮で生じるモーメントの大きさであるロール剛性をKφf、Kφr、車体の幾何学的な瞬間回転中心であるロールセンタの地面からの高さをh、h、重心点11の高さをh、前後のロールセンタを結ぶロール軸12と重心点11の間の距離をhφ、タイヤを含む前後のサスペンションのばね定数をKsf、Ksr、タイヤを含む前後のサスペンションの減衰係数をCsf、Csrとす
る。
FIG. 4 is a diagram illustrating a roll motion associated with the lateral acceleration acting on the barycentric point 11. Figure 4 is a lateral acceleration G y is showing how vehicle roll angle φ of the sprung mass m b occurs acting. Here, the roll stiffness, which is the magnitude of the moment generated by the expansion and contraction of the front and rear suspensions per unit roll angle, is represented by K φf , K φr , and the height from the ground of the roll center, which is the geometric instantaneous rotation center of the vehicle body, is represented by h f. , H r , the height of the center of gravity 11 is h, the distance between the roll shaft 12 connecting the front and rear roll centers and the center of gravity 11 is h φ , and the spring constants of the front and rear suspensions including the tires are K sf , K sr , Let C sf and C sr be the damping coefficients of the front and rear suspensions including the tire.
 図5は、重心点11に作用する前後加速度に伴うピッチ運動を示す図である。図5は、前後加速度Gが作用するばね上質量mの車両にピッチ角ψが生じる様子を示したものである。ここで単位ピッチ角あたりのタイヤを含む前後のサスペンションの伸縮で生じるモーメントの大きさであるピッチ剛性をKψ、車体の幾何学的な瞬間回転中心であるピッチ軸13と重心点11の間の距離をhψとする。 FIG. 5 is a diagram illustrating pitch motion associated with longitudinal acceleration acting on the barycentric point 11. Figure 5 is a graph showing how the pitch angle ψ occurs in the vehicle sprung mass m b acting longitudinal acceleration G x. Here, the pitch rigidity, which is the magnitude of the moment generated by the expansion and contraction of the front and rear suspensions including the tire per unit pitch angle, is represented by K ψ , and the distance between the center of gravity 11 and the pitch axis 13 which is the geometrical instantaneous rotation center of the vehicle body. Let the distance be h ψ .
 ここで図1に示した状態量推定部23における状態量推定の一例として、まず図3に示した4輪車の等価的な2輪車モデルを用いた車体の横すべり角βと横方向速度Vの算出方法を説明する。横方向速度Vの時間微分であるdV/dtとz軸周りに生じるヨーレートrの時間微分であるdr/dtは、車両の質量をm、前後タイヤの単位横すべり角あたりのコーナリングフォースであるコーナリングパワーをそれぞれK、K、車両のヨー慣性モーメントをIとして、以下の式(1)、式(2)で表される。 Here, as an example of state quantity estimation in the state quantity estimation unit 23 shown in FIG. 1, first, the side slip angle β and the lateral velocity V of the vehicle using the equivalent two-wheel vehicle model of the four-wheel vehicle shown in FIG. A method of calculating y will be described. DV y / dt, which is the time derivative of the lateral velocity V y , and dr / dt, which is the time derivative of the yaw rate r generated around the z-axis, are the cornering force per unit side slip angle of the front and rear tires, m. the cornering power each K f, K r, the yaw inertia moment of the vehicle as I z, the following equation (1), the formula (2).
Figure JPOXMLDOC01-appb-M000001
Figure JPOXMLDOC01-appb-M000001
Figure JPOXMLDOC01-appb-M000002
Figure JPOXMLDOC01-appb-M000002
 更にヨーレートrの出力偏差をフィードバックするオブザーバを構成し、式(1)、式(2)を状態方程式と出力方程式で表すと以下の式(3)、式(4)になる。 Further, an observer that feeds back the output deviation of the yaw rate r is constructed, and the following equations (3) and (4) are obtained by expressing the equations (1) and (2) by the state equation and the output equation.
Figure JPOXMLDOC01-appb-M000003
Figure JPOXMLDOC01-appb-M000003
Figure JPOXMLDOC01-appb-M000004
Figure JPOXMLDOC01-appb-M000004
であり、(V^,r^)は,(V,r)の推定値である。オブザーバでは偏差eが減少するようにオブザーバ入力が補正され、状態量の推定誤差が低減される。この式(3)、式(4)から横方向速度の推定値V^が得られ、車体の横すべり角の推定値β^は以下の式(5)を用いて算出できる。 And (V y ^, r ^) is an estimated value of (V y , r). In the observer, the observer input is corrected so that the deviation e decreases, and the state quantity estimation error is reduced. An estimated value V y ^ of the lateral speed is obtained from the equations (3) and (4), and an estimated value β ^ of the side slip angle of the vehicle body can be calculated using the following equation (5).
Figure JPOXMLDOC01-appb-M000005
Figure JPOXMLDOC01-appb-M000005
 次に図4に示した重心点11に作用する横加速度Gに伴うロール運動モデルを用いた車体のロール角φの算出方法と、図5に示した重心点11に作用する前後加速度Gに伴うピッチ運動モデルを用いた車体のピッチ角ψの算出方法の一例を説明する。車両が一定の加速度を持ち、重心点11に一定の慣性力が働いていると仮定した場合、ロール角φとピッチ角ψは重力加速度をgとして、以下の式(6)、式(7)で表される方程式を用いて算出できる。 Next, a method for calculating the roll angle φ of the vehicle body using the roll motion model associated with the lateral acceleration G y acting on the center of gravity 11 shown in FIG. 4 and the longitudinal acceleration G x acting on the center of gravity 11 shown in FIG. An example of a method for calculating the pitch angle ψ of the vehicle body using the pitch motion model associated with will be described. Assuming that the vehicle has a constant acceleration and a constant inertial force is acting on the center of gravity 11, the roll angle φ and the pitch angle ψ are expressed by the following equations (6) and (7) It can calculate using the equation represented by.
Figure JPOXMLDOC01-appb-M000006
Figure JPOXMLDOC01-appb-M000006
Figure JPOXMLDOC01-appb-M000007
Figure JPOXMLDOC01-appb-M000007
 以上の式(1)~式(7)において、車両の質量m、ばね上質量m、重心点11と前輪軸との距離、および後輪軸との距離l、l、車両のヨー慣性モーメントI、コーナリングパワーK、K、ロール剛性Kφf、Kφr、ピッチ剛性Kψ、前後のロールセンタを結ぶロール軸12と重心点11の間の距離hφ、車体の幾何学的な瞬間回転中心であるピッチ軸13と重心点11の間の距離hψはパラメータであり、それ以外の前後加速度Gやヨーレートrなどは図1で述べた検出値である。 In the above formulas (1) to (7), the vehicle mass m, the sprung mass m b , the distance between the center of gravity 11 and the front wheel axis, and the distances l f and l r between the rear wheel axis and the yaw inertia of the vehicle Moment I z , cornering power K f , K r , roll stiffness K φf , K φr , pitch stiffness K ψ , distance h φ between the roll axis 12 connecting the front and rear roll centers and the center of gravity point 11, body geometry The distance h ψ between the pitch axis 13 and the center of gravity 11 which is the center of instantaneous rotation is a parameter, and the other longitudinal acceleration G x and yaw rate r are the detection values described in FIG.
 これらのパラメータには従来、車両設計時の走行試験や数値解析の結果などに基づいて、経年劣化や環境変化などに対応するロバスト性を考慮した値が定義され、定数として状態量推定などに用いられている。しかし、ロバスト性を考慮した定数であるため、常に推定誤差が生じるという課題があった。それに対して本発明では、このパラメータを変数として扱い、図1で述べた外界認識手段2の検出値に基づいて実車に即した値に更新することで推定誤差を低減し、前記課題を解決する。 Conventionally, these parameters have been defined based on driving tests and numerical analysis results at the time of vehicle design, taking into account robustness to cope with aging and environmental changes, etc., and used as constants for estimating state quantities, etc. It has been. However, since it is a constant considering the robustness, there is a problem that an estimation error always occurs. On the other hand, in the present invention, this parameter is treated as a variable, and the estimation error is reduced by updating to a value according to the actual vehicle based on the detection value of the external recognition means 2 described in FIG. .
 次に、パラメータ更新部22におけるパラメータ算出方法の一例を説明する。図6は、車両に質量mの質点16が加わることによって重心点が14から15へ移動した様子を示す図である。なお、本実施例では理解が容易になるように、車体のロール角φが生じない位置に質点16が作用しているものと仮定した一例であり、より詳細に荷重移動を算出する場合には車体のロール角φを考慮することが望ましい。また、本実施例では車両設計時の走行試験や数値解析の結果などに基づくパラメータを設計値、更新パラメータを更新値とし、以降で述べる式の’が付く記号は更新値とする。ここでl’、l’は重心点(更新値)15と前輪軸との距離、および後輪軸との距離、xは重心点(設計値)14と重心点(更新値)15との距離、xは質点16と重心点(更新値)15との距離である。 Next, an example of a parameter calculation method in the parameter update unit 22 will be described. 6, the center of gravity by mass 16 mass m p is applied to the vehicle is a diagram showing a state that has moved to 14 from 15. In the present embodiment, for ease of understanding, this is an example assuming that the mass point 16 is acting at a position where the roll angle φ of the vehicle body does not occur. When calculating the load movement in more detail, It is desirable to consider the roll angle φ of the vehicle body. In this embodiment, a parameter based on a running test at the time of vehicle design, a result of numerical analysis, or the like is a design value, an update parameter is an update value, and a symbol with an 'in the expression described below is an update value. Here, l f ′ and l r ′ are the distance between the center of gravity (update value) 15 and the front wheel axis, and the distance between the rear wheel axis, and x G is the center of gravity (design value) 14 and the center of gravity (update value) 15. the distance, x P is the distance between the mass point 16 and the center of gravity point (updated value) 15.
 まず、検出値に基づいて車両の質量mとばね上質量mを算出する方法を説明する。 First, a method of calculating the mass m and the sprung mass m b of the vehicle based on the detected value.
 ばね定数などのサスペンション関連のパラメータが変化しておらず、タイヤを含む前後のサスペンションのばね定数Ksf、Ksrを線形ばねと仮定した場合、車両の質量とばね上質量の更新値m’、m’は、以下の式(8)で表される方程式を用いて算出できる。 When the suspension-related parameters such as the spring constant are not changed and the spring constants K sf and K sr of the suspension including the tire are assumed to be linear springs, the vehicle mass and the sprung mass update value m ′, m b ′ can be calculated using an equation represented by the following equation (8).
Figure JPOXMLDOC01-appb-M000008
Figure JPOXMLDOC01-appb-M000008
 ここで式(8)のm、mは車両の質量とばね上質量の設計値、mは質点16の質量、zf、はそれぞれ車体の前側、後側の上下変位であり、以下の式(9)で表される方程式を用いて算出できる。 Here m, m b is the mass and the sprung mass of the design value of the vehicle of the formula (8), m P is the mass of mass point 16, z f, z r is a vehicle body front side, the rear vertical displacement, respectively, It can calculate using the equation represented by the following formula | equation (9).
Figure JPOXMLDOC01-appb-M000009
Figure JPOXMLDOC01-appb-M000009
 ここで式(9)のピッチ角ψは、外界認識手段2で検出した車両運動状態量検出値200である。また、式(9)のl’、l’は、図6に示す重心点(更新値)15と前輪軸との距離、および後輪軸との距離の更新値であり、以下の式(10)で表される方程式を用いて算出できる。 Here, the pitch angle ψ in the equation (9) is the vehicle motion state quantity detection value 200 detected by the external recognition means 2. In addition, l f ′ and l r ′ in Expression (9) are updated values of the distance between the center of gravity (updated value) 15 and the front wheel shaft and the distance from the rear wheel shaft shown in FIG. It can be calculated using the equation represented by 10).
Figure JPOXMLDOC01-appb-M000010
Figure JPOXMLDOC01-appb-M000010
 ここで式(10)は車速Vが0とみなせるほどの低速で旋回している時に成立し、車体の横すべり角βは外界認識手段2で検出した車両運動状態量検出値200、実舵角δはドライバ入力量検出値の換算値である。以上の式(8)~式(10)を用いることで、車両の質量とばね上質量の更新値m’、m’を算出できる。 Here, the equation (10) is established when the vehicle is turning at a low speed such that the vehicle speed V can be regarded as 0, and the side slip angle β of the vehicle body is the vehicle motion state amount detection value 200 detected by the external recognition means 2 and the actual steering angle δ. Is a conversion value of the driver input amount detection value. By using the above equations (8) to (10), the updated values m ′ and m b ′ of the vehicle mass and sprung mass can be calculated.
 但し、この算出方法は車体の質量変化に伴ってピッチ角ψが変化することが前提であり、車内の人や荷物の質量および位置によってはそれらが変化せず、式(8)~式(10)だけでは正しい質量を算出できない場合が考えられる。そのため、以下の式(11)で表される方程式を用いて算出する方法を併用することが望ましい。ここで式(11)のTはタイヤ出力トルク、Rはタイヤ半径である。なお、タイヤ出力トルクTは、例えばエンジンやモータのトルクマップ、変速機の変速比、効率などから算出する方法や、トルクセンサを用いて駆動軸のトルクを直接検出する方法により取得できる。なお、式(11)を併用した場合、車両の質量とばね上質量の更新値m’、m’がそれぞれ2つ算出されるが、ピッチ角ψが変化する場合にはそれらの値は等しくなり、ピッチ角ψが変化しない場合には式(11)による更新値が式(8)~式(10)による更新値より大きくなるため、値が大きい方を選定することで正しい車両の質量とばね上質量の更新値m’、m’を算出できる。 However, this calculation method is based on the premise that the pitch angle ψ changes with a change in the mass of the vehicle body, and they do not change depending on the mass and position of the person in the vehicle or the load, and the equations (8) to (10) ) Alone cannot be used to calculate the correct mass. Therefore, it is desirable to use the method of calculating using the equation represented by the following formula (11). Here, T in the equation (11) is a tire output torque, and R is a tire radius. The tire output torque T can be obtained by, for example, a method of calculating from a torque map of the engine or motor, a transmission gear ratio, efficiency, or the like, or a method of directly detecting the torque of the drive shaft using a torque sensor. When Equation (11) is used in combination, two updated values m ′ and m b ′ of the vehicle mass and sprung mass are calculated, but when the pitch angle ψ changes, these values are equal. Therefore, when the pitch angle ψ does not change, the updated value according to the equation (11) becomes larger than the updated values according to the equations (8) to (10). The updated values m ′ and m b ′ of the sprung mass can be calculated.
Figure JPOXMLDOC01-appb-M000011
Figure JPOXMLDOC01-appb-M000011
 ここで、外界認識手段2の検出値を用いた車両の質量とばね上質量の更新値m’、m’の算出方法は、上記で述べた方法に限定されるものではなく、例えばサスペンションジオメトリを考慮した外界認識手段2の検出値と車両の質量とばね上質量の更新値m’、m’の関係を表す特性マップを予め車両状態量推定装置1に記憶しておき、その特性マップに外界認識手段2の検出値を入力することで車両の質量とばね上質量の更新値m’、m’を算出しても良い。更に、前記特性マップに加速度の軸を追加することで、路面傾斜による重力加速度の影響を除去した車両の質量とばね上質量の更新値m’、m’を算出できる。 Here, the calculation method of the updated values m ′ and m b ′ of the vehicle mass and the sprung mass using the detection value of the external recognition means 2 is not limited to the method described above. Is stored in the vehicle state quantity estimating device 1 in advance, and the characteristic map representing the relationship between the detected value of the external recognition means 2 taking into account the updated value m ′ and m b ′ of the vehicle mass and the sprung mass is stored in advance. The update values m ′ and m b ′ of the vehicle mass and sprung mass may be calculated by inputting the detection value of the external recognition means 2 into Furthermore, by adding an acceleration axis to the characteristic map, it is possible to calculate updated values m ′ and m b ′ of the vehicle mass and the sprung mass from which the influence of the gravitational acceleration due to the road surface inclination is removed.
 次に、検出値に基づいて重心高さhやロール軸12と重心11の間の距離hφなどの重心位置を算出する方法を説明する。 Next, a method of calculating the center of gravity position such as the center of gravity height h and the distance h φ between the roll shaft 12 and the center of gravity 11 based on the detected value will be described.
 ロール剛性Kφf、Kφr、ピッチ剛性Kψなどのサスペンション関連のパラメータが変化していないと仮定した場合、ロール軸12と重心11の間の距離、ピッチ軸13と重心11の間の距離、重心高さの更新値hφ’、hψ’、h’は、以下の式(12)で表される方程式を用いて算出できる。 Assuming that the suspension-related parameters such as roll stiffness K φf , K φr , and pitch stiffness K ψ have not changed, the distance between the roll shaft 12 and the center of gravity 11, the distance between the pitch shaft 13 and the center of gravity 11, The updated values h φ ′, h ψ ′, h ′ of the center of gravity height can be calculated using an equation represented by the following equation (12).
Figure JPOXMLDOC01-appb-M000012
Figure JPOXMLDOC01-appb-M000012
 次に検出値に基づいて図6に示す重心点(設計値)14と重心点(更新値)15との距離x、質点16と重心点(更新値)15との距離xを算出する方法を説明する。距離xは重心点と後輪軸との距離の設計値lrと更新値lrの差の絶対値であり、距離xは重心点(更新値)15を中心とした車両の質量の設計値mと質点16の質量mによるモーメントの釣り合いより、以下の式(13)で表される方程式を用いて算出できる。 Next, based on the detected value, the distance x G between the centroid point (design value) 14 and the centroid point (update value) 15 and the distance x P between the mass point 16 and the centroid point (update value) 15 shown in FIG. 6 are calculated. A method will be described. The distance x G is the absolute value of the difference between the design value lr and the update value lr of the distance between the center of gravity and the rear wheel axle, and the distance x P is the design value m of the vehicle mass centered on the center of gravity (update value) 15. From the balance of moments due to the mass m P of the mass point 16, it can be calculated using the equation represented by the following equation (13).
Figure JPOXMLDOC01-appb-M000013
Figure JPOXMLDOC01-appb-M000013
 ここで、外界認識手段2の検出値を用いた重心高さhなどの重心位置の算出方法は、上記で述べた方法に限定されるものではなく、例えばサスペンションジオメトリを考慮した外界認識手段2の検出値と重心位置の関係を表す特性マップを予め車両状態量推定装置1に記憶しておき、その特性マップに外界認識手段2の検出値を入力することで重心位置を算出しても良い。更に、前記特性マップに加速度の軸を追加することで、路面傾斜による重力加速度の影響を除去した重心位置を算出できる。 Here, the calculation method of the center of gravity position such as the center of gravity height h using the detection value of the external environment recognition unit 2 is not limited to the method described above, and for example, the external environment recognition unit 2 considering the suspension geometry. A characteristic map representing the relationship between the detected value and the center of gravity position may be stored in advance in the vehicle state quantity estimating device 1, and the center of gravity position may be calculated by inputting the detected value of the external field recognition means 2 to the characteristic map. Furthermore, by adding an acceleration axis to the characteristic map, it is possible to calculate the position of the center of gravity from which the influence of the gravitational acceleration due to the road surface inclination is removed.
 次に、検出値に基づいてヨー慣性モーメントIなどの慣性モーメントを算出する方法を説明する。 Next, a method of calculating the moment of inertia of such a yaw inertia moment I z on the basis of the detection value.
 慣性モーメントの算出方法の一例として、ヨー慣性モーメントの更新値I’は、平行軸の定理を用いた以下の式(14)で表される方程式で算出できる。ここで、式(14)のIはヨー慣性モーメントの設計値である。 As an example of a method of calculating the moment of inertia, the updated value I z ′ of the yaw moment of inertia can be calculated by an equation represented by the following formula (14) using the parallel axis theorem. Here, I z in equation (14) is the design value of the yaw moment of inertia.
Figure JPOXMLDOC01-appb-M000014
Figure JPOXMLDOC01-appb-M000014
 次に、検出値に基づいてコーナリングパワーK、Kなどのタイヤ特性を算出する方法を説明する。 Next, a method for calculating tire characteristics such as cornering powers K f and K r based on the detected values will be described.
 タイヤ特性の算出方法の一例として、コーナリングパワーの更新値K’、K’は、以下の式(15)で表される方程式を用いて算出できる。 As an example of a tire characteristic calculation method, the cornering power update values K f ′ and K r ′ can be calculated using an equation represented by the following equation (15).
Figure JPOXMLDOC01-appb-M000015
Figure JPOXMLDOC01-appb-M000015
 ここで式(15)に示すコーナリングフォースY、Y、タイヤの横すべり角β、βは、それぞれ以下の式(16)、式(17)で表される方程式を用いて算出できる。 Here, the cornering forces Y f and Y r and the tire side slip angles β f and β r shown in the equation (15) can be calculated using equations represented by the following equations (16) and (17), respectively.
Figure JPOXMLDOC01-appb-M000016
Figure JPOXMLDOC01-appb-M000016
Figure JPOXMLDOC01-appb-M000017
Figure JPOXMLDOC01-appb-M000017
 以上が本発明における車両運動状態量の推定方法、パラメータ算出方法の一例である。 The above is an example of the vehicle motion state estimation method and parameter calculation method in the present invention.
 前述した本発明の車両状態量推定装置1における具体的な実施形態について、図7から図13を用いて説明する。 Specific embodiments of the vehicle state quantity estimation device 1 of the present invention described above will be described with reference to FIGS.
 図7は、本発明の実施形態による車両状態量推定装置1を搭載した車両10の構成図を示したものである。 FIG. 7 shows a configuration diagram of a vehicle 10 equipped with the vehicle state quantity estimating device 1 according to the embodiment of the present invention.
 本実施形態の車両状態量推定装置1は車両10に搭載され、カメラやGPSなどの外界認識手段2、加速度センサ3、ジャイロセンサ4、車輪速センサ6から車両運動に関する状態量、操作角センサ5からドライバ操作に関する状態量の検出値を取得する。車両状態量推定装置1は、図1で述べたように検出値を用いて外界認識手段2の安定度を判断し、その判断結果に基づいてパラメータを更新、更新したパラメータと検出値を用いて車体の横すべり角や横方向速度などを推定し、その結果を車両の制駆動力を制御する駆動制御ユニット8とブレーキ制御ユニット9に出力する。 The vehicle state quantity estimation device 1 according to the present embodiment is mounted on a vehicle 10, and includes a state quantity related to vehicle motion and an operation angle sensor 5 from an external environment recognition means 2 such as a camera and GPS, an acceleration sensor 3, a gyro sensor 4, and a wheel speed sensor 6. The detection value of the state quantity related to the driver operation is acquired from the above. The vehicle state quantity estimation device 1 determines the stability of the external recognition means 2 using the detection value as described in FIG. 1, updates the parameter based on the determination result, and uses the updated parameter and detection value. The side slip angle and lateral speed of the vehicle body are estimated, and the results are output to the drive control unit 8 and the brake control unit 9 that control the braking / driving force of the vehicle.
 図8は、車両状態量推定装置1の処理概要を示すフローチャートである。まず、車両状態量推定装置1はパラメータ更新や状態量推定に必要な車両運動状態量およびドライバ操作量の検出値を、加速度センサ3やジャイロセンサ4などから取得する(ステップS801)。次にステップS801で取得した加速度センサ3などの検出値である車両運動状態量検出値100と、外界認識手段2の検出値である車両運動状態量検出値200を比較し、その大小関係に基づいて車両運動状態量検出値200が安定しているか否かを判断し、その安定度判断結果を出力する(ステップS802)。次にステップS802において安定判断がなされた場合、検出値に基づいて前述の式(8)~式(18)を用いて車両の質量mやヨー慣性モーメントIなどのパラメータを更新し、その更新パラメータを出力する(ステップS803)。最後にステップS803で更新されたパラメータと車両運動状態量検出値100、ドライバ操作量検出値に基づいて、上述の式(1)~式(7)を用いて車体の横すべり角βや横方向速度V、ロール角φ、ピッチ角ψを推定し、その車両運動状態量推定値300を駆動制御ユニット8やブレーキ制御ユニット9に出力し、終了する。なお、一般的に外界認識手段2の検出値の出力周期は、加速度センサなどの慣性センサの検出値の出力周期より遅い。そのため、後述する安定度判断やパラメータ更新などにおいて、出力周期の差による誤処理を防ぐには、処理周期を最も出力周期が遅いセンサの出力周期に合わせる、または最も出力周期が遅いセンサの検出値を時間微分し、その時間微分値に基づいて予測した値を用いることが望ましい。また、燃料ゲージや着座センサ、シートベルトセンサなどから取得した情報に基づいて更新パラメータの信頼性を評価し、その評価結果に基づいてパラメータ更新を行うか否かを判断する処理をステップS803とステップS804の間に追加しても良い。 FIG. 8 is a flowchart showing a processing outline of the vehicle state quantity estimation device 1. First, the vehicle state quantity estimation device 1 acquires the detected values of the vehicle motion state quantity and the driver operation quantity necessary for parameter update and state quantity estimation from the acceleration sensor 3 and the gyro sensor 4 (step S801). Next, the vehicle motion state quantity detection value 100 that is the detection value of the acceleration sensor 3 and the like acquired in step S801 is compared with the vehicle motion state quantity detection value 200 that is the detection value of the external recognition means 2, and based on the magnitude relationship. Then, it is determined whether or not the vehicle motion state quantity detection value 200 is stable, and the stability determination result is output (step S802). Then if the stability determination is made in step S802, the updated parameters such as the mass m and the yaw inertia moment I z of the vehicle based on the detection value using the above equations (8) to (18), the update A parameter is output (step S803). Finally, based on the parameters updated in step S803, the vehicle motion state detection value 100, and the driver operation amount detection value, the side slip angle β and the lateral speed of the vehicle body are calculated using the above formulas (1) to (7). V y , roll angle φ, and pitch angle ψ are estimated, and the vehicle motion state estimated value 300 is output to drive control unit 8 and brake control unit 9, and the process ends. In general, the output cycle of the detection value of the external recognition means 2 is slower than the output cycle of the detection value of an inertial sensor such as an acceleration sensor. Therefore, in order to prevent erroneous processing due to the difference in output cycle in stability determination and parameter update, which will be described later, the processing cycle is adjusted to the output cycle of the sensor with the slowest output cycle, or the detection value of the sensor with the slowest output cycle It is desirable to use a value obtained by performing time differentiation on the basis of the time derivative and predicting based on the time derivative value. Further, the process of evaluating the reliability of the update parameter based on the information acquired from the fuel gauge, the seating sensor, the seat belt sensor, etc., and determining whether to update the parameter based on the evaluation result is performed in steps S803 and S803. It may be added during S804.
 図9は、車両状態量推定装置1の安定度判断部21の処理概要を示すフローチャートである。本実施例ではジャイロセンサ4で検出したヨーレートrの時間積分値であるヨー角θを真値と仮定し、その真値に対する外界認識手段2で検出したヨー角θ^の誤差に基づいて安定度を判断する方法を説明する。まず、安定度判断部21はジャイロセンサ4からヨーレートr、外界認識手段2からヨー角θ^を取得する(ステップS901)。次にステップS901で取得したヨーレートrを時間積分し、ヨー角θを算出する(ステップS902)。次にステップS902で算出したヨー角θを真値として、真値とステップS901で取得したヨー角θ^の差であるヨー角誤差を算出する(ステップS903)。次にステップS903で算出したヨー角誤差が所定の閾値に対して小さいか否かを判定し(ステップS904)、小さい場合は(ステップS904、YES)、ステップS905に進んでカウント値に所定の値を加算するカウントアップ処理を行い、大きい場合は(ステップS904、NO)、ステップS906に進んでカウント値を0にするカウントリセット処理を行う。次にステップS905でのカウントアップ処理、またはステップS906でのカウントリセット処理をしたカウント値が所定の閾値に対して大きいか否かを判定し(ステップS907)、大きい場合は(ステップS907、YES)、外界認識手段2で検出した車両運動状態量検出値200が所定の期間において継続して安定し、信頼できると判断してステップS908に進んで安定判断を出力し、小さい場合は(ステップS907、NO)、外界認識手段2で検出した車両運動状態量検出値200が所定の期間において継続して安定しておらず、信頼できないと判断してステップS909に進んで不安定判断を出力し、処理を終了する。ここでステップS901~ステップS909における安定度を判断する方法は、上記で述べたカウントアップによる処理に限定されるものではなく、例えばカウント値から所定の値を減算するカウントダウンによる判断方法や、外界認識手段2が自己診断した情報に基づく判断方法であっても良い。また、安定度の判断対象は、上記で述べたヨー角誤差に限定されるものではなく、例えばジャイロセンサ4で検出したヨーレートrを真値とし、真値と外界認識手段2で検出したヨー角を時間微分したヨーレートr^との差であるヨーレート誤差を判断対象としても良い。 FIG. 9 is a flowchart showing a processing outline of the stability determination unit 21 of the vehicle state quantity estimation device 1. In this embodiment, it is assumed that the yaw angle θ, which is the time integral value of the yaw rate r detected by the gyro sensor 4, is a true value, and the stability is based on the error of the yaw angle θ ^ detected by the external field recognition means 2 with respect to the true value. A method of determining the will be described. First, the stability determination unit 21 acquires the yaw rate r from the gyro sensor 4 and the yaw angle θ ^ from the external environment recognition unit 2 (step S901). Next, the yaw rate r acquired in step S901 is time-integrated to calculate the yaw angle θ (step S902). Next, with the yaw angle θ calculated in step S902 as a true value, a yaw angle error that is the difference between the true value and the yaw angle θ ^ acquired in step S901 is calculated (step S903). Next, it is determined whether or not the yaw angle error calculated in step S903 is small with respect to a predetermined threshold (step S904). If small (step S904, YES), the process proceeds to step S905 and the count value is set to a predetermined value. Is increased (NO in step S904), the process proceeds to step S906, and a count reset process for setting the count value to 0 is performed. Next, it is determined whether or not the count value obtained by the count-up process in step S905 or the count reset process in step S906 is larger than a predetermined threshold (step S907). If the count value is larger (YES in step S907). If the vehicle motion state quantity detection value 200 detected by the external environment recognition means 2 continues to be stable during a predetermined period and is determined to be reliable, the process proceeds to step S908 to output a stability determination, and if small (step S907, NO), the vehicle motion state quantity detection value 200 detected by the external environment recognition means 2 is determined to be unreliable and unreliable for a predetermined period, and the process proceeds to step S909 to output an instability determination and processing. Exit. Here, the method of determining the stability in steps S901 to S909 is not limited to the above-described processing by count-up, and for example, a determination method by count-down that subtracts a predetermined value from the count value, or external recognition A determination method based on information self-diagnosed by the means 2 may be used. The stability determination target is not limited to the yaw angle error described above. For example, the yaw rate r detected by the gyro sensor 4 is set to the true value, and the true value and the yaw angle detected by the external recognition unit 2 are determined. The yaw rate error, which is the difference from the yaw rate r ^ obtained by time differentiation of, may be determined.
 図10は、車両状態量推定装置1のパラメータ更新部22の処理概要を示すフローチャートである。まず、パラメータ更新部22は安定度判断部21の出力である安定度判断結果を取得する(ステップS1001)。次にステップS1001で取得した安定度判断結果が安定判断であるか不安定判断であるかを判定し(ステップS1002)、安定判断である場合は(ステップS1002、YES)、外界認識手段2の検出値にパラメータを更新するための信頼性があると判断してステップS1003に進み、不安定判断である場合は(ステップS1002、NO)、外界認識手段2の検出値にパラメータを更新するための信頼性がないと判断してパラメータの更新を行わずに処理を終了する。ステップS1002において外界認識手段2の検出値にパラメータを更新するための信頼性があると判断された場合、車両運動状態量およびドライバ操作量の検出値を、加速度センサ3やジャイロセンサ4などから取得し(ステップS1003)、ステップS1004に進む。ステップS1004~ステップS1007では、ステップS1003で取得した検出値に基づいて、上述の式(8)~式(17)を用いて質量、重心位置、慣性モーメント、タイヤ特性を算出し、パラメータを更新、更新したパラメータを出力して、処理を終了する。 FIG. 10 is a flowchart showing an outline of processing of the parameter update unit 22 of the vehicle state quantity estimation device 1. First, the parameter update unit 22 acquires the stability determination result that is the output of the stability determination unit 21 (step S1001). Next, it is determined whether the stability determination result acquired in step S1001 is a stability determination or an instability determination (step S1002). If it is a stability determination (step S1002, YES), detection of the external recognition means 2 is performed. If it is determined that the value has reliability for updating the parameter, the process proceeds to step S1003. If the value is unstable (NO in step S1002), the reliability for updating the parameter to the detected value of the external recognition unit 2 is determined. The process is terminated without determining that the parameter is not updated. If it is determined in step S1002 that the detected value of the external environment recognizing means 2 is reliable for updating the parameter, the detected values of the vehicle motion state amount and the driver operation amount are acquired from the acceleration sensor 3 or the gyro sensor 4 or the like. (Step S1003), the process proceeds to step S1004. In steps S1004 to S1007, based on the detection value acquired in step S1003, the mass, the position of the center of gravity, the moment of inertia, and the tire characteristics are calculated using the above formulas (8) to (17), and the parameters are updated. The updated parameter is output and the process ends.
 ここでパラメータの更新方法は上記の方法に限定されるものではなく、例えば最適化手法やシステム同定手法を用いて走行中にセンサで取得した時系列データからパラメータを更新しても良い。また、パラメータ更新部22において更新するパラメータの値の範囲は、センサの誤検出による更新値の発散などを防ぐため、例えば質量の場合は空車時の質量を下限値、最大積載時の質量を上限値とするなど、各パラメータで変化する可能性がある範囲を予め定義し、その範囲内で更新することが望ましい。また、更新パラメータの保存形態は数値に限定せず、例えば式(16)と式(17)を用いて算出したコーナリングフォースY、Yとタイヤの横すべり角β、βに基づくタイヤ特性のマップとして保
存しても良い。
Here, the parameter updating method is not limited to the above-described method. For example, the parameter may be updated from time-series data acquired by a sensor during traveling using an optimization method or a system identification method. In addition, the parameter value range to be updated in the parameter updating unit 22 is set such that, for example, in the case of mass, the mass when empty is the lower limit value and the mass when maximum loading is the upper limit in order to prevent divergence of the updated value due to erroneous sensor detection. It is desirable to predefine a range that may change depending on each parameter, such as a value, and update within that range. Further, the storage form of the update parameter is not limited to a numerical value. For example, the tire characteristics based on the cornering forces Y f and Y r calculated using the equations (16) and (17) and the tire side slip angles β f and β r are used. You may save it as a map.
 図11は、車両状態量推定装置1の検出値、検出値の誤差、安定期間のカウント値、推定値と検出値の時間変化を示す図である。検出値、検出値の誤差、安定期間のカウント値は、図9で述べた安定度判断部21における処理結果の一例である。また、推定値は図10で述べたパラメータ更新部22から出力された更新パラメータを用いて状態量推定部23が推定した結果の一例である。図11に示す安定判断期間は、検出値の誤差(Q)が閾値(a)より小さく、そのカウント値(J)が閾値(b)より大きい期間であり、外界認識手段2で検出した車両運動状態量検出値200が所定の期間において継続して安定し、信頼できるとして、安定度判断部21が安定判断を出力した期間である。パラメータ更新部22は、前記の安定判断期間内にパラメータ更新を行い、それ以外の期間(不安定判断期間)ではパラメータ更新を中止する。その結果、状態量推定部23は安定判断期間において従来方法と同様に真値と概ね等しい高精度な推定値を出力できる。更に本実施例の状態量推定部23は、安定判断期間で更新された最新のパラメータを用いて推定を行うため、不安定判断期間において外界認識手段2の検出値による推定値の補正ができない従来方法に比べて真値に近い推定値を出力することができる。このように従来方法では定数として扱っていたパラメータを変数として扱い、そのパラメータを外界認識手段2の検出値に基づいて実車に即した値に更新し、その更新したパラメータを用いて推定を行うことで従来方法より推定誤差を低減できる。 FIG. 11 is a diagram showing a detected value of the vehicle state quantity estimating apparatus 1, an error of the detected value, a count value of a stable period, and a time change of the estimated value and the detected value. The detection value, the error of the detection value, and the count value of the stable period are examples of the processing result in the stability determination unit 21 described with reference to FIG. Further, the estimated value is an example of a result estimated by the state quantity estimating unit 23 using the update parameter output from the parameter updating unit 22 described in FIG. The stability determination period shown in FIG. 11 is a period in which the detected value error (Q) is smaller than the threshold value (a) and the count value (J) is larger than the threshold value (b). This is a period during which the stability determination unit 21 outputs a stability determination that the state quantity detection value 200 is continuously stable and reliable in a predetermined period. The parameter update unit 22 updates the parameters within the stability determination period, and stops the parameter update during other periods (unstable determination period). As a result, the state quantity estimation unit 23 can output a highly accurate estimated value substantially equal to the true value in the stability determination period as in the conventional method. Furthermore, since the state quantity estimation unit 23 of the present embodiment performs estimation using the latest parameters updated in the stability determination period, the estimated value cannot be corrected by the detected value of the external field recognition unit 2 in the instability determination period. An estimated value closer to the true value than the method can be output. In this way, a parameter that has been treated as a constant in the conventional method is treated as a variable, the parameter is updated to a value that matches the actual vehicle based on the detection value of the external recognition means 2, and estimation is performed using the updated parameter. Thus, the estimation error can be reduced compared to the conventional method.
 以上のような車両状態量推定装置1を適用した場合の効果を以下で説明する。 The effect of applying the vehicle state quantity estimation device 1 as described above will be described below.
 横滑り防止装置の場合、例えば設計時より低性能なタイヤに交換、またはタイヤ空気圧が低下、または磨耗したタイヤを使用した際、従来方法では横滑り防止装置の制動力指令が不足して横すべりが増大し、走行安定性が低下する恐れがあった。それに対して本実施例の車両状態量推定装置1を適用した場合、現状のタイヤ特性に基づいて横滑り防止装置の制動力指令を補正できるため、従来方法より横すべりを低減し、走行安定性を向上させることができる。 In the case of a skid prevention device, for example, when replacing a tire with a lower performance than at the time of design, or using a tire whose tire air pressure is reduced or worn, the skid force command of the skid prevention device is insufficient in the conventional method, and skidding increases. There was a risk that running stability would be reduced. On the other hand, when the vehicle state quantity estimation device 1 of the present embodiment is applied, the braking force command of the skid prevention device can be corrected based on the current tire characteristics, so the skid is reduced compared to the conventional method and the running stability is improved. Can be made.
 また、本実施例の車両状態量推定装置1を適用した場合、真値と概ね等しい高精度な推定値が得られる、つまりドライバの操作入力に対する状態量の予測値が得られるため、その予測値に基づいて横滑り防止装置の制動力指令を補正でき、従来方法より横すべりを低減し、走行安定性を向上できる。 Further, when the vehicle state quantity estimation device 1 of the present embodiment is applied, a highly accurate estimated value approximately equal to the true value is obtained, that is, a predicted value of the state quantity with respect to the driver's operation input is obtained. Accordingly, the braking force command of the skid prevention device can be corrected, and the side slip can be reduced as compared with the conventional method, and the running stability can be improved.
 また、パワーステアリング装置の場合、例えば設計時より低性能なタイヤに交換、またはタイヤ空気圧が低下、または磨耗したタイヤを使用した場合、従来方法ではパワーステアリング装置のアシストが変化しないため、タイヤ特性相応の車両挙動が生じていた。それに対して本実施例の車両状態量推定装置1を適用した場合、現状のタイヤ特性に応じてパワーステアリング装置のアシスト力や舵角を増減できるため、限界域を除いて純正タイヤ時や新品タイヤ時と同等の車両挙動を実現することができる。また、本実施例の車両状態量推定装置1を適用した場合、ノミナルモデルと最新パラメータの推定モデルによる推定値を比較する事でタイヤ空気圧の低下などを検出でき、ドライバに伝達する事ができる。 In the case of a power steering device, for example, when a tire with a lower performance than that at the time of design is used, or when a tire with a decreased or worn tire pressure is used, the assist of the power steering device does not change in the conventional method. Vehicle behavior was occurring. On the other hand, when the vehicle state quantity estimating device 1 of the present embodiment is applied, the assist force and the steering angle of the power steering device can be increased / decreased according to the current tire characteristics. The vehicle behavior equivalent to the time can be realized. In addition, when the vehicle state quantity estimation device 1 of the present embodiment is applied, a decrease in tire air pressure or the like can be detected by comparing the estimated values of the nominal model and the latest parameter estimation model, and can be transmitted to the driver.
 また、電動ブースタ装置の場合、例えば設計時より低性能なタイヤに交換、またはタイヤ空気圧が低下、または磨耗したタイヤを使用した場合、従来方法では電動ブースタ装置の制動力指令が変化しないため、タイヤ特性相応の制動力が生じる。それに対して本実施例の車両状態量推定装置1を適用した場合、現状のタイヤ特性に応じて電動ブースタ装置の制動力指令を補正できるため、限界域を除いて純正タイヤ時や新品タイヤ時と同等の制動力を発生させる事ができる。 Further, in the case of an electric booster device, for example, when a tire having a lower performance than that at the time of design or a tire pressure is reduced or worn is used, the braking force command of the electric booster device does not change in the conventional method. A braking force corresponding to the characteristics is generated. On the other hand, when the vehicle state quantity estimation device 1 of the present embodiment is applied, the braking force command of the electric booster device can be corrected according to the current tire characteristics. Equivalent braking force can be generated.
 また、電動パーキングブレーキ装置の場合、最新の質量に基づいて適切な制動力指令を生成できるため、従来方法より電力消費を削減する事ができる。 Also, in the case of an electric parking brake device, an appropriate braking force command can be generated based on the latest mass, so that power consumption can be reduced as compared with the conventional method.
 図12は、地面に固定した座標系に対する車両の重心点11の位置関係を示す図である。車両の重心点11の軌跡は、車両の重心点11の地面に固定された座標系に対する位置を(X,Y)、車両のX軸に対するヨー角をθとして、以下の式(18)で表される。 FIG. 12 is a diagram showing the positional relationship of the center of gravity 11 of the vehicle with respect to the coordinate system fixed to the ground. The trajectory of the center of gravity 11 of the vehicle is expressed by the following equation (18), where (X, Y) is the position of the center of gravity 11 of the vehicle with respect to the coordinate system fixed to the ground, and the yaw angle with respect to the X axis of the vehicle is θ. Is done.
Figure JPOXMLDOC01-appb-M000018
Figure JPOXMLDOC01-appb-M000018
 ここで、X、Y、θはそれぞれt=0でのX、Y、θの値、tは任意の時間である。この式(18)は車両の進行方向の速度Vや車体の横すべり角βといった外界認識手段2の検出値、または車両モデルを用いた式(1)などで算出される推定値を用いる。また、これらの外界認識手段2の検出値や車両モデルを用いて算出した推定値を用いずに車両の重心点11の軌跡を算出するには、例えば以下の式(19)を用いる方法がある。 Here, X 0 , Y 0 , and θ 0 are values of X, Y, and θ at t = 0, respectively, and t is an arbitrary time. This equation (18) uses the detected value of the external recognition means 2 such as the speed V in the traveling direction of the vehicle and the side slip angle β of the vehicle body, or an estimated value calculated by equation (1) using a vehicle model. Further, in order to calculate the trajectory of the center of gravity 11 of the vehicle without using the detected value of the external recognition means 2 or the estimated value calculated using the vehicle model, for example, there is a method using the following equation (19). .
Figure JPOXMLDOC01-appb-M000019
Figure JPOXMLDOC01-appb-M000019
 図13は、低性能なタイヤへの交換やタイヤ空気圧の低下に伴ってコーナリングパワーが低下した車両の定常円旋回時の重心点11の軌跡を示す図である。真値(e)は、外界認識手段2の検出値である車両運動状態量検出値200および式(18)を用いて算出した車両の重心点11の実軌跡である。車両モデルなし(f)は、式(19)を用いて算出した車両の重心点11の推定軌跡である。車両モデルあり, パラメータ更新なし(g)は、パラメータを更新していない車両モデルを用いて算出した推定値および式(18)を用いて算出した車両の重心点11の推定軌跡である。車両モデルあり, パラメータ更新なし(h)は、パラメータを更新した車両モデルを用いて算出した推定値および式(18)を用いて算出した車両の重心点11の推定軌跡である。 FIG. 13 is a diagram showing the trajectory of the center of gravity 11 during a steady circle turn of a vehicle whose cornering power has decreased due to replacement with a low-performance tire or a decrease in tire air pressure. The true value (e) is the actual locus of the center of gravity point 11 of the vehicle calculated using the vehicle motion state quantity detection value 200 that is the detection value of the external environment recognition means 2 and the equation (18). No vehicle model (f) is an estimated trajectory of the center of gravity 11 of the vehicle calculated using Expression (19). “With vehicle model and without heel parameter update (g)” is an estimated value calculated using a vehicle model whose parameters are not updated and an estimated locus of the center of gravity 11 of the vehicle calculated using equation (18). The vehicle model present and the saddle parameter not updated (h) are an estimated value calculated using the vehicle model with updated parameters and an estimated locus of the center-of-gravity point 11 of the vehicle calculated using Equation (18).
 図13に示すように車両の重心点11の実軌跡である真値(e)に対して、車両モデルあり, パラメータ更新あり(h)の推定軌跡は概ね等しいが、車両モデルなし(f)と車両モデルあり, パラメータ更新なし(g)の推定軌跡は推定誤差が大きい。 As shown in FIG. 13, the estimated trajectory with the vehicle model and with the heel parameter updated (h) is approximately equal to the true value (e) that is the actual trajectory of the center of gravity point 11 of the vehicle, but with no vehicle model (f). The estimation trajectory with the vehicle model and without the saddle parameter update (g) has a large estimation error.
 自動運転装置の場合、従来方法では外界認識手段の検出値に基づいて自己位置推定を行っており、雨天やレンズ汚れなどの悪条件では検出精度が低下し、自動運転が継続できなくなる恐れがあった。この外界認識手段の検出精度低下に対して、自動運転を継続する方法として、式(18)や式(19)を用いて推定した車両の軌跡などの情報に基づいてマップマッチングを行う方法が考えられるが、上記の車両モデルなし(f)や車両モデルあり, パラメータ更新なし(g)のように軌跡の推定誤差が大きい方法では安全な自動運転を継続できない恐れがあった。それに対して本実施例の車両状態量推定装置1を適用した場合、車両の軌跡などを高精度に推定できるため、少なくとも安全に停車できる場所まで自動運転を継続することができる。 In the case of an automatic driving device, the conventional method performs self-position estimation based on the detection value of the external recognition means, and there is a risk that automatic detection cannot be continued due to poor detection accuracy due to bad conditions such as rain or lens contamination. It was. As a method of continuing automatic driving in response to a decrease in the detection accuracy of the external recognition means, a method of performing map matching based on information such as the vehicle trajectory estimated using Equation (18) or Equation (19) is considered. However, there is a possibility that safe automatic driving cannot be continued by a method with a large estimation error of the trajectory such as no vehicle model (f) or a vehicle model and no saddle parameter update (g). On the other hand, when the vehicle state quantity estimation apparatus 1 of the present embodiment is applied, the vehicle trajectory and the like can be estimated with high accuracy, so that automatic driving can be continued to at least a place where the vehicle can be safely stopped.
 実施例2では、実施例1との差分について説明し、実施例1と同じ説明は省略する。 In the second embodiment, differences from the first embodiment will be described, and the same description as the first embodiment will be omitted.
 なお、実施例2と実施例1の主な違いは安定度判断部21における安定度の判断方法であり、図14と図15を用いて、実施例2における車両状態量推定装置1の処理概要を説明する。 The main difference between the second embodiment and the first embodiment is the stability determination method in the stability determination unit 21. The processing outline of the vehicle state quantity estimation device 1 in the second embodiment is described with reference to FIGS. Will be explained.
 図14は、実施例2における車両状態量推定装置1の安定度判断部21の処理概要を示すフローチャートである。図14のステップS1401~ステップS1403の処理は、実施例1における図9のステップS901~ステップS903と同じであり、説明は省略する。ステップS1404では、ステップS1403で算出したヨー角誤差を所定の期間で時間積分した値を算出する。次にステップS1404で算出したヨー角誤差の積分値が所定の閾値に対して小さいか否かを判定し(ステップS1405)、小さい場合は(ステップS1405、YES)、外界認識手段2で検出した車両運動状態量検出値200が所定の期間において継続して安定し、信頼できると判断してステップS1406に進んで安定判断を出力し、大きい場合は(ステップS1405、NO)、外界認識手段2で検出した車両運動状態量検出値200が所定の期間において継続して安定しておらず、信頼できないと判断してステップS1407に進んで不安定判断を出力し、処理を終了する。ここでステップS1404、ステップS1405における安定度の判断方法は、上記で述べた積分値による判断方法に限定されるものではなく、例えば平均値による判断方法であっても良い。 FIG. 14 is a flowchart showing a processing outline of the stability determination unit 21 of the vehicle state quantity estimation device 1 according to the second embodiment. The processing in steps S1401 to S1403 in FIG. 14 is the same as that in steps S901 to S903 in FIG. In step S1404, a value obtained by time-integrating the yaw angle error calculated in step S1403 over a predetermined period is calculated. Next, it is determined whether or not the integrated value of the yaw angle error calculated in step S1404 is smaller than a predetermined threshold (step S1405). If it is smaller (YES in step S1405), the vehicle detected by the external environment recognition unit 2 is determined. It is determined that the motion state quantity detection value 200 is continuously stable and reliable in a predetermined period, and the process proceeds to step S1406 to output a stability determination. If it is large (NO in step S1405), it is detected by the external recognition means 2. It is determined that the detected vehicle motion state quantity value 200 is not stable for a predetermined period and is not reliable, and the process proceeds to step S1407 to output an instability determination, and the process ends. Here, the stability determination method in steps S1404 and S1405 is not limited to the determination method based on the integral value described above, and may be a determination method based on an average value, for example.
 図15は、実施例2における車両状態量推定装置1の検出値、検出値の誤差、検出値の誤差の積分値、推定値と検出値の時間変化を示す図である。図15に示す安定判断期間は、検出値の誤差(Q)を所定の期間で積分した積分値(N)が閾値(c)より小さい期間であり、外界認識手段2で検出した車両運動状態量検出値200が所定の期間において継続して安定し、信頼できるとして、安定度判断部21が安定判断を出力した期間である。図15の検出値と検出値の誤差に示す閾値(a)を超過する瞬間的なノイズは、実際の車両の使用環境下において生じる可能性がある。このようなノイズが生じた場合、実施例1における車両状態量推定装置1ではカウント値がリセットされ、パラメータ更新が中止される。それに対して実施例2における車両状態量推定装置1では、ノイズの大きさが小さく、積分値(N)が閾値(c)より小さくなる期間を安定判断期間とすることで、実際の車両の使用環境下において、パラメータの更新にある一定の精度を確保しながら、パラメータの更新頻度を増やすことができる。 FIG. 15 is a diagram illustrating a detected value of the vehicle state quantity estimation device 1 in the second embodiment, an error of the detected value, an integrated value of the detected value error, and a time change of the estimated value and the detected value. The stability determination period shown in FIG. 15 is a period in which the integrated value (N) obtained by integrating the error (Q) of the detected value in a predetermined period is smaller than the threshold value (c), and the vehicle motion state quantity detected by the external recognition means 2 This is a period in which the stability determination unit 21 outputs a stability determination that the detection value 200 is continuously stable and reliable in a predetermined period. The instantaneous noise exceeding the threshold value (a) indicated by the error between the detection value and the detection value in FIG. 15 may occur under the actual use environment of the vehicle. When such noise occurs, the count value is reset in the vehicle state quantity estimation device 1 in the first embodiment, and the parameter update is stopped. On the other hand, in the vehicle state quantity estimation device 1 in the second embodiment, the actual vehicle use is determined by setting the period during which the magnitude of noise is small and the integral value (N) is smaller than the threshold value (c) as the stability determination period. Under the environment, it is possible to increase the parameter update frequency while ensuring a certain accuracy in the parameter update.
 実施例3では、実施例1および実施例2との差分について説明し、実施例1および実施例2と同じ説明は省略する。 In the third embodiment, differences from the first embodiment and the second embodiment will be described, and the same description as the first embodiment and the second embodiment will be omitted.
 なお、実施例3と実施例1および実施例2の主な違いは、パラメータ更新部22におけるパラメータの更新/未更新の判断方法であり、図16と図17を用いて、実施例3における車両状態量推定装置1の処理概要を説明する。 The main difference between the third embodiment, the first embodiment, and the second embodiment is a method for determining whether the parameter update unit 22 has updated or not updated parameters. The vehicle in the third embodiment is described with reference to FIGS. 16 and 17. A processing outline of the state quantity estimation device 1 will be described.
 図16は、実施例3における車両状態量推定装置1のパラメータ更新部22の処理概要を示すフローチャートである。まず、実施例3のパラメータ更新部22は、1処理周期前の外界認識手段2で検出した車両運動状態量検出値200と、状態量推定部23で推定した推定値を取得する(ステップS1601)。ここで本実施例では、図8で述べた車両状態量推定装置1の処理概要を示すフローチャートのSTARTからENDまでを1処理として定義する。次にステップS1601で取得した検出値、推定値の差である推定誤差を算出する(ステップS1602)。次にステップS1602で算出した推定誤差が所定の閾値に対して大きいか否かを判定し(ステップS1603)、大きい場合は(ステップS1603、YES)、推定値の精度が不十分と判断してステップS1604に進み、小さい場合は(ステップS1603、NO)、推定値の精度が必要十分であり、パラメータの更新は不要と判断して処理を終了する。ステップS1604では、図10で述べたフローに従ってパラメータを更新し、処理を終了する。 FIG. 16 is a flowchart showing an outline of processing of the parameter update unit 22 of the vehicle state quantity estimation device 1 in the third embodiment. First, the parameter update unit 22 according to the third embodiment acquires the vehicle motion state quantity detection value 200 detected by the external field recognition unit 2 before one processing cycle and the estimation value estimated by the state quantity estimation unit 23 (step S1601). . Here, in this embodiment, one process is defined from START to END in the flowchart showing the outline of the process of the vehicle state quantity estimating apparatus 1 described in FIG. Next, an estimation error that is a difference between the detected value and the estimated value acquired in step S1601 is calculated (step S1602). Next, it is determined whether or not the estimation error calculated in step S1602 is larger than a predetermined threshold (step S1603). If it is larger (step S1603, YES), it is determined that the accuracy of the estimated value is insufficient. In S1604, if small (NO in Step S1603), the accuracy of the estimated value is necessary and sufficient, and it is determined that the parameter update is unnecessary, and the process ends. In step S1604, the parameters are updated according to the flow described in FIG. 10, and the process ends.
 図17は、実施例3における車両状態量推定装置1の検出値、検出値の誤差、安定期間のカウント値、推定誤差、推定値と検出値の時間変化を示す図である。図17に示すように、安定判断期間であっても推定誤差が所定の閾値(d)より小さい場合にはパラメータ更新を中止する。このように推定誤差の大きさに基づいてパラメータの更新/未更新を判断することで、ある一定の推定精度を確保しながら、車両状態量推定装置1の計算負荷を下げることができ、消費電力や発熱などを低減できる。 FIG. 17 is a diagram illustrating a detected value of the vehicle state quantity estimating apparatus 1 in the third embodiment, an error in the detected value, a count value in a stable period, an estimated error, and a time change of the estimated value and the detected value. As shown in FIG. 17, the parameter update is stopped if the estimation error is smaller than the predetermined threshold (d) even during the stability determination period. Thus, by determining whether the parameter is updated / not updated based on the magnitude of the estimation error, the calculation load of the vehicle state quantity estimation device 1 can be reduced while ensuring a certain estimation accuracy, and the power consumption And heat generation can be reduced.
 実施例4では、実施例1~実施例3との差分について説明し、実施例1~実施例3と同じ説明は省略する。 In the fourth embodiment, differences from the first to third embodiments will be described, and the same description as the first to third embodiments will be omitted.
 なお、実施例4と実施例1~実施例3の主な違いは、実施例1~実施例3の車両状態量推定装置1に出力値判断部24を追加した車両状態量出力装置30を構成したことであり、図18~図20を用いて実施例4における車両状態量出力装置30の処理概要を説明する。 The main difference between the fourth embodiment and the first to third embodiments is that the vehicle state quantity output device 30 is configured by adding an output value determining unit 24 to the vehicle state quantity estimating device 1 of the first to third embodiments. Therefore, an outline of the process of the vehicle state quantity output device 30 according to the fourth embodiment will be described with reference to FIGS.
 図18は、実施例4における車両状態量出力装置30の概念図である。図18に示すように、車両状態量出力装置30は実施例1~実施例3の車両状態量推定装置1に出力値判断部24を追加した構成になっている。出力値判断部24は、状態量推定部23の出力である車両運動状態量推定値300と、安定度判断部21の出力である安定度判断結果と、外界認識手段2で検出した車両運動状態量検出値200に基づいて、車両運動状態量推定値300と車両運動状態量検出値200のどちらを車両運動状態量出力値400として出力するかを判断する。ここで、図18には車両運動状態量出力値400のみを出力する場合を記載しているが、必要に応じて安定度判断結果や更新パラメータ、車両運動状態量推定値300を出力しても良く、車両状態量出力装置30の出力値は限定しない。 FIG. 18 is a conceptual diagram of the vehicle state quantity output device 30 according to the fourth embodiment. As shown in FIG. 18, the vehicle state quantity output device 30 has a configuration in which an output value determination unit 24 is added to the vehicle state quantity estimation device 1 of the first to third embodiments. The output value determination unit 24 includes a vehicle motion state quantity estimated value 300 that is an output of the state amount estimation unit 23, a stability determination result that is an output of the stability determination unit 21, and a vehicle motion state detected by the external environment recognition unit 2. Based on the amount detection value 200, it is determined which of the vehicle motion state amount estimated value 300 and the vehicle motion state amount detection value 200 is output as the vehicle motion state amount output value 400. Here, FIG. 18 shows a case where only the vehicle motion state quantity output value 400 is output. However, if necessary, the stability determination result, the update parameter, and the vehicle motion state quantity estimated value 300 may be output. The output value of the vehicle state quantity output device 30 is not limited.
 図19は、実施例4における車両状態量出力装置30の出力値判断部24の処理概要を示すフローチャートである。まず、出力値判断部24は安定度判断部21の出力である安定度判断結果を取得する(ステップS1901)。次にステップS1901で取得した安定度判断結果が安定判断であるか不安定判断であるかを判定し(ステップS1902)、安定判断である場合は(ステップS1902、YES)、外界認識手段2の検出値にパラメータを更新するための信頼性があると判断してステップS1903に進み、不安定判断である場合は(ステップS1902、NO)、外界認識手段2の検出値にパラメータを更新するための信頼性がないと判断してステップS1907に進み、推定値を出力して処理を終了する。ステップS1902において外界認識手段2の検出値にパラメータを更新するための信頼性があると判断された場合、1処理周期前の外界認識手段2で検出した車両運動状態量検出値200と、状態量推定部23で推定した推定値を取得する(ステップS1903)。ここで本実施例では、実施例1の図8で述べた車両状態量推定装置1の処理概要を示すフローチャートのSTARTからENDまでを1処理として定義する。次にステップS1903で取得した検出値、推定値の差である推定誤差を算出する(ステップS1904)。次にステップS1904で算出した推定誤差が所定の閾値に対して大きいか否かを判定し(ステップS1905)、大きい場合は(ステップS1905、YES)、推定値の精度が不十分と判断してステップS1906に進んで検出値を出力し、小さい場合は(ステップS1905、NO)、推定値の精度が必要十分であると判断してステップS1907に進んで推定値を出力し、処理を終了する。 FIG. 19 is a flowchart showing a processing outline of the output value determination unit 24 of the vehicle state quantity output device 30 according to the fourth embodiment. First, the output value determination unit 24 acquires the stability determination result that is the output of the stability determination unit 21 (step S1901). Next, it is determined whether the stability determination result acquired in step S1901 is a stability determination or an instability determination (step S1902). If it is a stability determination (step S1902, YES), detection of the external recognition means 2 is performed. It is determined that the value has reliability for updating the parameter, and the process proceeds to step S1903. If the value is unstable (NO in step S1902), the reliability for updating the parameter to the detected value of the external recognition unit 2 is determined. If it is determined that there is no possibility, the process advances to step S1907 to output an estimated value, and the process ends. If it is determined in step S1902 that the detected value of the external environment recognizing means 2 is reliable for updating the parameter, the vehicle motion state quantity detected value 200 detected by the external environment recognizing means 2 one processing cycle before, and the state quantity The estimated value estimated by the estimating unit 23 is acquired (step S1903). Here, in this embodiment, one process is defined from START to END in the flowchart showing the outline of the process of the vehicle state quantity estimation device 1 described in FIG. 8 of the first embodiment. Next, an estimation error that is a difference between the detected value and the estimated value acquired in step S1903 is calculated (step S1904). Next, it is determined whether or not the estimation error calculated in step S1904 is larger than a predetermined threshold (step S1905). If it is larger (step S1905, YES), it is determined that the accuracy of the estimated value is insufficient. The process proceeds to S1906 to output the detected value. If the detected value is small (NO in step S1905), it is determined that the accuracy of the estimated value is necessary and sufficient, and the process proceeds to step S1907 to output the estimated value and the process is terminated.
 図20は、実施例4における車両状態量出力装置30の検出値、検出値の誤差、安定期間のカウント値、推定誤差、推定値と検出値と出力値の時間変化を示す図である。図20に示すように車両状態量出力装置30は、安定判断期間であり、かつ推定誤差が閾値(d)より大きい場合には検出値を出力値として出力し、それ以外では推定値を出力値として出力する。このように安定度判断結果および推定誤差の大きさに基づいて、車両状態量出力装置30から出力する出力値を判断することで、車両状態量出力装置30の中で真値に最も近い値を出力することができる。 FIG. 20 is a diagram illustrating a detected value of the vehicle state quantity output device 30 in the fourth embodiment, an error in the detected value, a count value in a stable period, an estimation error, and a time change in the estimated value, the detected value, and the output value. As shown in FIG. 20, the vehicle state quantity output device 30 outputs the detected value as an output value when the estimation error is greater than the threshold (d) during the stability determination period, and the estimated value is output as the output value otherwise. Output as. As described above, by determining the output value output from the vehicle state quantity output device 30 based on the stability determination result and the magnitude of the estimation error, the value closest to the true value in the vehicle state quantity output device 30 is obtained. Can be output.
 実施例5では、実施例1~実施例4との差分について説明し、実施例1~実施例4と同じ説明は省略する。 In the fifth embodiment, differences from the first to fourth embodiments will be described, and the same description as in the first to fourth embodiments will be omitted.
 なお、実施例5と実施例1~実施例4の主な違いは、実施例1~実施例4の車両10にサスペンション制御ユニット40と制御サスペンション装置41を追加した車両10’を構成したことであり、図21~図24を用いて主に実施例5におけるサスペンション制御ユニット40の処理概要を説明する。なお、実施例5における車両状態量推定装置1は、車両状態量出力装置30であっても良い。 The main difference between the fifth embodiment and the first to fourth embodiments is that a vehicle 10 ′ in which a suspension control unit 40 and a control suspension device 41 are added to the vehicle 10 of the first to fourth embodiments is configured. A processing outline of the suspension control unit 40 in the fifth embodiment will be mainly described with reference to FIGS. Note that the vehicle state quantity estimation device 1 according to the fifth embodiment may be the vehicle state quantity output device 30.
 図21は、実施例5における車両状態量推定装置1あるいは車両状態量出力装置30を搭載した車両10’の構成図を示したものである。図21は、図7に対してサスペンション制御ユニット40と制御サスペンション装置41を追加した構成になっている。制御サスペンション装置41は、減衰特性を調整可能な減衰力調整式のショックアブソーバあるいは車体と車輪の間の上下方向の力を調整可能なアクティブサスペンションである。サスペンション制御ユニット40は、慣性センサやジャイロセンサなどの検出値や車両状態量推定装置1で更新した質量や重心位置などの更新パラメータに基づいて、乗心地制御やアンチロール制御などに必要な車両状態量を推定し、制御サスペンション装置41の減衰特性あるいは上下方向の力を制御する制御信号を生成する。 FIG. 21 shows a configuration diagram of a vehicle 10 ′ equipped with the vehicle state quantity estimation device 1 or the vehicle state quantity output device 30 in the fifth embodiment. FIG. 21 shows a configuration in which a suspension control unit 40 and a control suspension device 41 are added to FIG. The control suspension device 41 is a damping force adjustment type shock absorber capable of adjusting a damping characteristic or an active suspension capable of adjusting a vertical force between a vehicle body and a wheel. The suspension control unit 40 is a vehicle state required for riding comfort control, anti-roll control, and the like based on detection values of inertial sensors, gyro sensors, and the like, and updated parameters such as mass and center of gravity updated by the vehicle state quantity estimation device 1. The amount is estimated, and a control signal for controlling the damping characteristic or the vertical force of the control suspension device 41 is generated.
 次に図22~図24を用いて車両状態量推定装置1で更新した更新パラメータを用いたサスペンション制御ユニット40における乗心地制御、アンチロール制御、アンチダイブ・アンチスクワット制御の処理概要を説明する。 Next, an outline of processing of ride comfort control, anti-roll control, and anti-dive / anti-squat control in the suspension control unit 40 using the updated parameters updated by the vehicle state quantity estimating device 1 will be described with reference to FIGS.
 図22は実施例5における制御サスペンション装置41の1機能である乗心地制御を行うサスペンション制御ユニット40の概念図である。サスペンション制御ユニット40には、車両状態量推定装置1で更新された質量や重心位置などの更新パラメータや、慣性センサで検出したばね上上下加速度やばね下上下加速度などの車両運動状態量検出値100が入力される。 FIG. 22 is a conceptual diagram of a suspension control unit 40 that performs riding comfort control, which is one function of the control suspension device 41 in the fifth embodiment. The suspension control unit 40 includes update parameters such as mass and center of gravity updated by the vehicle state quantity estimation device 1 and vehicle motion state quantity detection values 100 such as sprung vertical acceleration and unsprung vertical acceleration detected by an inertial sensor. Is entered.
 サスペンション制御ユニット40は、上下速度推定部43と、目標減衰力算出部44と、減衰力マップ45を備える。 The suspension control unit 40 includes a vertical speed estimation unit 43, a target damping force calculation unit 44, and a damping force map 45.
 上下速度推定部43は、車両状態量推定装置1の更新パラメータと車両運動状態量検出値100を入力として、ばね上およびばね下の上下速度を推定する。 The vertical speed estimation unit 43 receives the update parameter of the vehicle state quantity estimation device 1 and the vehicle motion state quantity detected value 100 as input, and estimates the vertical speed of the spring and unsprung.
 目標減衰力算出部44は、上下速度推定部42で推定した上下速度と車両運動状態量検出値100に基づいて、制御サスペンション装置41の目標減衰力を算出する。 The target damping force calculation unit 44 calculates the target damping force of the control suspension device 41 based on the vertical speed estimated by the vertical speed estimation unit 42 and the vehicle motion state quantity detection value 100.
 減衰力マップ45は、予め記憶された制御サスペンション装置41の特性のマップ情報であり、目標減衰力算出部44で算出した目標減衰力と車両運動状態量検出値100を入力として、制御サスペンション装置41を制御する指令電流を導出し、出力する。 The damping force map 45 is map information of the characteristics of the control suspension device 41 stored in advance. The control suspension device 41 receives the target damping force calculated by the target damping force calculation unit 44 and the vehicle motion state quantity detection value 100 as inputs. The command current for controlling the is derived and output.
 図23は実施例5における制御サスペンション装置41の1機能であるアンチロール制御を行うサスペンション制御ユニット40の概念図である。サスペンション制御ユニット40には、車両状態量推定装置1で更新された質量や重心位置などの更新パラメータや、慣性センサで検出したばね上上下加速度やばね下上下加速度などの車両運動状態量検出値100、舵角センサで検出した操舵角などのドライバ入力量検出値が入力される。 FIG. 23 is a conceptual diagram of a suspension control unit 40 that performs anti-roll control, which is one function of the control suspension device 41 in the fifth embodiment. The suspension control unit 40 includes update parameters such as mass and center of gravity updated by the vehicle state quantity estimation device 1 and vehicle motion state quantity detection values 100 such as sprung vertical acceleration and unsprung vertical acceleration detected by an inertial sensor. The driver input amount detection value such as the steering angle detected by the steering angle sensor is input.
 サスペンション制御ユニット40は、主に車両運動モデル46と、予め記憶されたロール制御ゲイン、ピッチ制御ゲインを備える。 The suspension control unit 40 mainly includes a vehicle motion model 46 and roll control gain and pitch control gain stored in advance.
 車両運動モデル46は、車両状態量推定装置1の更新パラメータと、車両運動状態量検出値100と、ドライバ入力量検出値を入力として車両の横加速度を推定する。 The vehicle motion model 46 estimates the lateral acceleration of the vehicle using the update parameter of the vehicle state quantity estimation device 1, the vehicle motion state quantity detection value 100, and the driver input quantity detection value as inputs.
 サスペンション制御ユニット40は、車両運動モデル46で推定した横加速度を微分した横加加速度とロール制御ゲイン、車両運動モデル46で推定した横加速度の絶対値とピッチ制御ゲインに基づいて、制御サスペンション装置41を制御する指令電流を算出し、出力する。 The suspension control unit 40 controls the control suspension device 41 based on the lateral jerk and roll control gain obtained by differentiating the lateral acceleration estimated by the vehicle motion model 46, and the absolute value and pitch control gain of the lateral acceleration estimated by the vehicle motion model 46. Calculate and output the command current to be controlled.
 図24は実施例5における制御サスペンション装置41の1機能であるアンチダイブ制御およびアンチスクワット制御を行うサスペンション制御ユニット40の概念図である。サスペンション制御ユニット40には、車両状態量推定装置1で更新された質量や重心位置などの更新パラメータと、ブレーキマスタシリンダ圧やエンジントルク、ギア位置などの車両運動状態量検出値100が入力される。 FIG. 24 is a conceptual diagram of a suspension control unit 40 that performs anti-dive control and anti-squat control, which are one function of the control suspension device 41 in the fifth embodiment. The suspension control unit 40 is input with updated parameters such as mass and center of gravity updated by the vehicle state quantity estimation device 1 and vehicle motion state quantity detection values 100 such as brake master cylinder pressure, engine torque, and gear position. .
 サスペンション制御ユニット40は、主に車両運動モデル46と、予め記憶されたピッチ制御ゲインを備える。 The suspension control unit 40 mainly includes a vehicle motion model 46 and a pitch control gain stored in advance.
 車両運動モデル46は、車両状態量推定装置1の更新パラメータと、車両運動状態量検出値100を入力として車両の前後加速度を推定する。 The vehicle motion model 46 estimates the longitudinal acceleration of the vehicle using the update parameter of the vehicle state quantity estimation device 1 and the vehicle motion state quantity detection value 100 as inputs.
 サスペンション制御ユニット40は、車両運動モデル46で推定した前後加速度を微分した前後加加速度とピッチ制御ゲインに基づいて、制御サスペンション装置41を制御する指令電流を算出し、出力する。 The suspension control unit 40 calculates and outputs a command current for controlling the control suspension device 41 based on the longitudinal jerk obtained by differentiating the longitudinal acceleration estimated by the vehicle motion model 46 and the pitch control gain.
 図25と図26は実施例5における更新パラメータを入力とした効果を示すシミュレーションの結果の一例である。図25と図26は共に高速うねり路において更新パラメータである質量の更新有無が乗心地に与える影響を比較した結果であり、図25はフロア、Frタワー、Rrタワーの上下加速度PSD、図26はフロア上下変位、ピッチ角、ロール角の時間変化を示す図である。図25と図26に示すようにパラメータ更新あり(本発明)は、予め記憶したロバスト性を考慮した設計時のパラメータを用いたパラメータ更新なし(従来方法)に対して、特にRrタワーの上下加速度PSDとピッチ角が小さくなっており、より高性能な乗心地制御が実現できる。 FIG. 25 and FIG. 26 are examples of simulation results showing the effect of using the update parameter in the fifth embodiment as an input. FIG. 25 and FIG. 26 both show the results of comparing the influence of mass update, which is an update parameter, on the riding comfort on a high-speed wavy road. FIG. 25 shows the vertical acceleration PSD of the floor, Fr tower, and Rr tower. It is a figure which shows the time change of a floor up-down displacement, a pitch angle, and a roll angle. As shown in FIG. 25 and FIG. 26, the parameter update (invention) is particularly effective in the vertical acceleration of the Rr tower, compared with the parameter update using the parameters stored in the design in consideration of the robustness stored in advance (conventional method). PSD and pitch angle are small, and higher performance riding comfort control can be realized.
 以上の構成により、最新の質量や重心位置などのパラメータを用いて高精度に推定したばね上上下速度や横加速度などの車両状態量に基づいてサスペンションを制御する指令電流を生成できるため、従来の予め記憶したロバスト性を考慮した設計時のパラメータを用いた場合より高性能なサスペンション制御を実現できる。また、質量や重心位置などが影響する予め記憶された減衰力マップやロール制御ゲイン、ピッチ制御ゲインは、最新の質量や重心位置などのパラメータを用いて更新することが望ましい。 With the above configuration, a command current for controlling the suspension can be generated based on vehicle state quantities such as the sprung vertical speed and lateral acceleration estimated with high accuracy using parameters such as the latest mass and center of gravity. Higher-performance suspension control can be realized than when design parameters are used in consideration of robustness stored in advance. In addition, it is desirable to update the damping force map, roll control gain, and pitch control gain stored in advance, which are affected by the mass, the center of gravity position, and the like, using parameters such as the latest mass and the center of gravity position.
 実施例6では、実施例5との差分について説明し、実施例5と同じ説明は省略する。 In the sixth embodiment, differences from the fifth embodiment will be described, and the same description as in the fifth embodiment will be omitted.
 なお、実施例6と実施例5の主な違いは、実施例5の車両10’に車高センサ42を追加した車両10”を構成したことであり、図27~図28を用いて主に実施例6におけるサスペンション制御ユニット40の処理概要を説明する。 The main difference between the sixth embodiment and the fifth embodiment is that a vehicle 10 ″ in which a vehicle height sensor 42 is added to the vehicle 10 ′ of the fifth embodiment is configured, and mainly using FIGS. 27 to 28. A processing outline of the suspension control unit 40 in the sixth embodiment will be described.
 図27は、実施例6における車両状態量推定装置1あるいは車両状態量出力装置30を搭載した車両10”の構成図を示したものである。図27は、図21に対して車高センサ42を追加した構成になっている。 FIG. 27 shows a configuration diagram of a vehicle 10 ″ equipped with the vehicle state quantity estimating device 1 or the vehicle state quantity output device 30 according to the sixth embodiment. FIG. 27 shows a vehicle height sensor 42 with respect to FIG. Is added.
 車高センサ42は、路面と車体の相対的なz軸方向の距離、あるいは車両のサスペンションの変位量を検出する。サスペンション制御ユニット40は、車高センサ42などの各種センサの検出値や車両状態量推定装置1あるいは車両状態量出力装置30の更新パラメータに基づいて制御サスペンション装置41の減衰特性あるいは上下方向の力を制御する。車高センサ42は、車体の右前側、左前側、右後側、左後側の上下変位zfr、zfl、zrr、zrlを直接検出することができるため、車両の質量とばね上質量の更新値m’、m’は、以下の式(20)で表される方程式を用いて算出できる。 The vehicle height sensor 42 detects the relative distance between the road surface and the vehicle body in the z-axis direction or the displacement of the vehicle suspension. The suspension control unit 40 determines the damping characteristics or the vertical force of the control suspension device 41 based on the detection values of various sensors such as the vehicle height sensor 42 and the update parameters of the vehicle state quantity estimation device 1 or the vehicle state quantity output device 30. Control. The vehicle height sensor 42 can directly detect the vertical displacements z fr , z fl , z rr , z rl on the right front side, left front side, right rear side, and left rear side of the vehicle body. The mass update values m ′ and m b ′ can be calculated using an equation represented by the following equation (20).
Figure JPOXMLDOC01-appb-M000020
Figure JPOXMLDOC01-appb-M000020
 一般的に外界認識手段2で検出したピッチ角ψなどに基づいて算出した上下変位に比べて、車高センサ42は上下変位を直接検出できるために精度が高く、車両状態量推定装置1あるいは車両状態量出力装置30において高精度なパラメータ更新と、それを用いた高性能なサスペンション制御を実現できる。また、質量や重心位置、慣性モーメントは外界認識手段2および車高センサ42の検出値から算出できるため、これらのパラメータに関してはどちらか一方が故障した場合であっても車両状態量推定装置1あるいは車両状態量出力装置30によるパラメータ更新を継続することができる。 In general, the vehicle height sensor 42 can directly detect the vertical displacement compared to the vertical displacement calculated based on the pitch angle ψ detected by the external environment recognition means 2, and the vehicle state quantity estimating device 1 or the vehicle is highly accurate. The state quantity output device 30 can implement highly accurate parameter updating and high-performance suspension control using the parameter updating. Further, since the mass, the position of the center of gravity, and the moment of inertia can be calculated from the detected values of the external recognition means 2 and the vehicle height sensor 42, even if one of these parameters fails, the vehicle state quantity estimating device 1 or The parameter update by the vehicle state quantity output device 30 can be continued.
 図28は、質量mの質点が作用する車両に生じる上下変位を示す図である。図28は、質量mの質点が作用する車体の右前側、左前側、右後側、左後側の上下変位zfr、zfl、zrr、zrlが生じる様子を示したものである。この車体の上下変位を用いて、車体のロール角φとピッチ角ψは、以下の式(21)で表される方程式を用いて算出できる。ここで本実施例では理解を容易にするために、車両前後輪のトレッド幅が等しいdと仮定する。 FIG. 28 is a diagram illustrating the vertical displacement generated in the vehicle on which the mass point of mass m P acts. FIG. 28 shows a state in which the vertical displacements z fr , z fl , z rr , and z rl of the right front side, the left front side, the right rear side, and the left rear side of the vehicle body on which the mass point of mass m P acts are generated. . Using the vertical displacement of the vehicle body, the roll angle φ and the pitch angle ψ of the vehicle body can be calculated using an equation expressed by the following equation (21). In this embodiment, in order to facilitate understanding, it is assumed that the tread widths of the vehicle front and rear wheels are equal d.
Figure JPOXMLDOC01-appb-M000021
Figure JPOXMLDOC01-appb-M000021
 この式(21)を用いて算出したロール角φとピッチ角ψを外界認識手段2の検出値と比較することで、センサ故障の有無や検出値の信頼性を判断することができる。 The presence / absence of a sensor failure and the reliability of the detected value can be determined by comparing the roll angle φ and the pitch angle ψ calculated using the equation (21) with the detected value of the external recognition means 2.
 実施例7では、実施例5および実施例6との差分について説明し、実施例5および実施例6と同じ説明は省略する。なお、実施例7と実施例5および実施例6の主な違いは、サスペンション制御ユニット40で算出したタイヤの接地荷重に基づいて、車両状態量推定装置1がパラメータ更新の可否を判断することであり、図29~図33を用いて、主に実施例7におけるサスペンション制御ユニット40と車両状態量制御装置1あるいは車両状態量出力装置30の処理概要を説明する。 In Example 7, differences from Example 5 and Example 6 will be described, and the same description as Example 5 and Example 6 will be omitted. The main difference between the seventh embodiment, the fifth embodiment, and the sixth embodiment is that the vehicle state quantity estimating device 1 determines whether the parameter can be updated based on the tire ground contact load calculated by the suspension control unit 40. A processing outline of the suspension control unit 40 and the vehicle state quantity control device 1 or the vehicle state quantity output device 30 in the seventh embodiment will be mainly described with reference to FIGS. 29 to 33.
 図29は、実施例7における車両状態量推定装置1’の概念図である。図29に示すように、車両状態量推定装置1’は実施例1~実施例3の車両状態量推定装置1のパラメータ更新部22にサスペンション制御ユニット40で算出したタイヤの接地荷重計算値を入力する構成になっている。なお、式(15)などで述べたコーナリングパワーK、Kは接地荷重によって大きさが変化するため、車両運動状態量推定値300の推定精度を向上させるためには接地荷重計算値を状態量推定部23に入力する構成にすることが望ましい。また、接地荷重計算値は実施例4の車両状態量出力装置30のパラメータ更新部22に入力する構成であっても良い。 FIG. 29 is a conceptual diagram of the vehicle state quantity estimation device 1 ′ in the seventh embodiment. As shown in FIG. 29, the vehicle state quantity estimation device 1 ′ inputs the tire ground contact load calculation value calculated by the suspension control unit 40 to the parameter update unit 22 of the vehicle state quantity estimation device 1 of the first to third embodiments. It is configured to do. Note that the cornering powers K f and K r described in the equation (15) and the like change in magnitude depending on the ground load. Therefore, in order to improve the estimation accuracy of the vehicle motion state quantity estimated value 300, the ground load calculated value is the state. It is desirable that the input is input to the quantity estimation unit 23. The ground load calculation value may be input to the parameter updating unit 22 of the vehicle state quantity output device 30 of the fourth embodiment.
 次にサスペンション制御ユニット40におけるタイヤの接地荷重計算値の推定方法を説明する。 Next, a method of estimating the tire ground contact load calculation value in the suspension control unit 40 will be described.
 図30は、1自由度の1/4車両モデルを示す図である。図30は、質量mのばね下上下変位zによって質量mのばね上が上下変位する様子を示したものである。ここでサスペンションのばね定数をK、サスペンションの減衰係数をC、ばね上の上下変位をz、ばね下の上下変位をz、タイヤの接地荷重をWとする。ばね上およびばね下の運動は、それぞれ以下の式(22)、式(23)で表される。 FIG. 30 is a diagram showing a ¼ vehicle model with one degree of freedom. Figure 30 is one in which the sprung mass m b showed how the vertical displacement by the unsprung vertical displacement z t of the mass m t. Here, it is assumed that the suspension spring constant is K s , the suspension damping coefficient is C s , the vertical displacement on the spring is z b , the vertical displacement on the spring is z t , and the tire ground load is W 0 . The unsprung and unsprung movements are expressed by the following equations (22) and (23), respectively.
Figure JPOXMLDOC01-appb-M000022
Figure JPOXMLDOC01-appb-M000022
Figure JPOXMLDOC01-appb-M000023
Figure JPOXMLDOC01-appb-M000023
 ここで式(22)、式(23)のサスペンションの減衰係数Cには、制御サスペンション装置41が減衰特性を調整可能な減衰力調整式のショックアブソーバの場合はサスペンション制御ユニット40の指令電流に基づく減衰係数を入力する。また、車体と車輪の間の上下方向の力を調整可能なアクティブサスペンションの場合には、式(22)、式(23)の右辺をサスペンション制御ユニット40で導出した上下方向の力に置き換える。また、d/dt、dt/dtは、ばね上およびばね下に設置した上下加速度センサの検出値およびそれらに基づく推定値に限定されるものではなく、例えば加速度センサ3や車高センサ42などの検出値に基づく推定値であっても良い。 Here, in the suspension damping coefficient C s of the equations (22) and (23), the command current of the suspension control unit 40 is used in the case of a damping force adjustment type shock absorber in which the control suspension device 41 can adjust the damping characteristics. Enter the damping factor based. Further, in the case of an active suspension capable of adjusting the vertical force between the vehicle body and the wheel, the right side of the equations (22) and (23) is replaced with the vertical force derived by the suspension control unit 40. Further, d 2 z b / dt 2 and d 2 z t / dt 2 are not limited to the detected values of the vertical acceleration sensor installed on the spring and under the spring and the estimated value based on them, for example, the acceleration sensor 3 or an estimated value based on a detected value of the vehicle height sensor 42 or the like.
 式(23)のΔWは接地荷重の変動分であり、接地荷重Wは静止時の接地荷重にこの変動分を加算した以下の式(24)で表される。 In equation (23), ΔW 0 is the amount of change in the contact load, and the contact load W 0 is expressed by the following equation (24) in which this change is added to the contact load at rest.
Figure JPOXMLDOC01-appb-M000024
Figure JPOXMLDOC01-appb-M000024
 以上の方法により、タイヤの接地荷重を算出することができる。 The ground contact load of the tire can be calculated by the above method.
 図31は、実施例7における車両状態量推定装置1’のパラメータ更新部22の処理概要を示すフローチャートである。図31に示すフローチャートでは、パラメータを補正するにあたって、接地荷重変動周波数と接地荷重変動差の算出を行う。この接地荷重変動周波数と接地荷重変動差は、荒れた路面によって生じる接地荷重の振動や路面のくぼみによって生じる接地荷重の抜けなどのパラメータを補正する補正ゲインを導出するために算出する。まず、図31の説明に先立ち、図31の理解が容易になるように、図32、図33を用いて接地荷重変動差の算出方法について説明する。 FIG. 31 is a flowchart showing a processing outline of the parameter update unit 22 of the vehicle state quantity estimation device 1 ′ in the seventh embodiment. In the flowchart shown in FIG. 31, the ground load variation frequency and the ground load variation difference are calculated in correcting the parameters. The ground load variation frequency and the difference in ground load variation are calculated in order to derive a correction gain for correcting parameters such as ground load vibration caused by a rough road surface and missing ground load caused by road surface depression. First, prior to the description of FIG. 31, a method for calculating the difference in ground load variation will be described with reference to FIGS. 32 and 33 so that the understanding of FIG. 31 is facilitated.
 図32は、重心点11に作用する加速度に伴う荷重移動を示す図である。図32は、前後加速度Gと横加速度Gが作用するばね上質量mの車両の各タイヤに接地荷重の変動量ΔWfl、ΔWfr、ΔWrl、ΔWrrが生じる様子を示したものである。なお、図32は理解が容易になるように、車体のロール角φおよびピッチ角ψが生じないと仮定した一例であり、より詳細に荷重移動を算出する場合には車体のロール角φおよびピッチ角ψに伴う荷重移動を考慮することが望ましい。 FIG. 32 is a diagram illustrating load movement accompanying acceleration acting on the barycentric point 11. Figure 32 shows how the variation amount [Delta] W fl ground load on each tire of the vehicle sprung mass m b acting longitudinal acceleration G x and the lateral acceleration G y, ΔW fr, ΔW rl , the [Delta] W rr occur It is. Note that FIG. 32 is an example assuming that the roll angle φ and the pitch angle ψ of the vehicle body do not occur so as to facilitate understanding. When calculating the load movement in more detail, the roll angle φ and the pitch of the vehicle body are illustrated. It is desirable to consider the load movement associated with the angle ψ.
 図33は、カント角σのバンク路を走行する車両の重心点11に作用する加速度を示す図である。なお、図33は理解が容易になるように、車体のロール角φおよび車両の横すべり角βなどが生じないと仮定した一例であり、より詳細に荷重移動を算出する場合にはそれらを考慮して荷重移動を計算することが望ましい。 FIG. 33 is a diagram showing acceleration acting on the gravity center point 11 of the vehicle traveling on the bank road having the cant angle σ. Note that FIG. 33 is an example on the assumption that the roll angle φ of the vehicle body and the side slip angle β of the vehicle do not occur so that the understanding can be easily understood. It is desirable to calculate load transfer.
 図32、図33に示したモデルを用いて、各タイヤの接地荷重の変動量ΔWfl、ΔWfr、ΔWrl、ΔWrrの算出方法の一例を説明する。車両が一定の加速度を持ち、重心点11に一定の慣性力が働いていると仮定した場合、接地荷重変動量の推定値ΔWfl、ΔWfr、ΔWrl、ΔWrrは、以下の式(25)で表される。 An example of a method for calculating the contact load variation amounts ΔW fl , ΔW fr , ΔW rl , ΔW rr of each tire will be described using the models shown in FIGS. 32 and 33. Assuming that the vehicle has a constant acceleration and a constant inertial force is acting on the center of gravity point 11, the estimated values ΔW fl , ΔW fr , ΔW rl , ΔW rr of the ground load variation are expressed by the following equation (25 ).
Figure JPOXMLDOC01-appb-M000025
Figure JPOXMLDOC01-appb-M000025
 ここで式(25)の右辺第一項は前後加速度Gxに伴う接地荷重変動量、右辺第二項は左右加速度Gyに伴う接地荷重変動量、右辺第三項はカント角σによって生じる上下加速度の変化に伴う接地荷重変動量である。また、式(25)で用いるカント角σは、以下の式(26)で表される方程式で算出できる。 Here, the first term on the right side of Equation (25) is the amount of ground load variation associated with the longitudinal acceleration Gx, the second term on the right side is the amount of ground load variation associated with the lateral acceleration Gy, and the third term on the right side is the vertical acceleration caused by the cant angle σ. This is the amount of change in grounding load due to change. Further, the cant angle σ used in the equation (25) can be calculated by an equation represented by the following equation (26).
Figure JPOXMLDOC01-appb-M000026
Figure JPOXMLDOC01-appb-M000026
 次に接地荷重変動差ΔWFY、ΔWRY、ΔWLX、ΔWRXは、サスペンション制御ユニット40から入力された最新の接地荷重をWfl、Wfr、Wrl、Wrr、最新の静止時の接地荷重をWfl0、Wfr0、Wrl0、Wrr0として、式(25)、式(26)から算出した接地荷重変動量の推定値ΔWfl、ΔWfr、ΔWrl、ΔWrrを用いて、以下の式(27)で表される。 Next, the ground load fluctuation differences ΔW FY , ΔW RY , ΔW LX , ΔW RX are the latest ground loads inputted from the suspension control unit 40, W fl , W fr , W rl , W rr , the latest ground contact at rest. Assuming that the loads are W fl0 , W fr0 , W rl0 , W rr0 , and using the estimated values ΔW fl , ΔW fr , ΔW rl , ΔW rr of ground contact load fluctuation amounts calculated from the equations (25), (26), (27)
Figure JPOXMLDOC01-appb-M000027
Figure JPOXMLDOC01-appb-M000027
 ここで式(27)の第一式は前輪左右の接地荷重変動量の計算値と推定値の差、第二式は後輪左右の接地荷重変動量の計算値と推定値の差、第三式は左輪前後の接地荷重変動量の計算値と推定値の差、第四式は右輪前後の接地荷重変動量計算値と推定値の差である。 Here, the first expression of Expression (27) is the difference between the calculated value and the estimated value of the contact load fluctuation amount on the left and right front wheels, the second expression is the difference between the calculated value and the estimated value of the contact load fluctuation amount on the left and right of the rear wheel, The formula is the difference between the calculated value and the estimated value of the ground load variation before and after the left wheel, and the fourth formula is the difference between the calculated value and the estimated value of the ground load variation before and after the right wheel.
 図31に戻り、実施例7における車両状態量推定装置1’のパラメータ更新部22の処理概要を示すフローチャートを説明する。 Referring back to FIG. 31, a flowchart showing an outline of the process of the parameter update unit 22 of the vehicle state quantity estimation device 1 'in the seventh embodiment will be described.
 まず、実施例7のパラメータ更新部22は、サスペンション制御ユニット40で算出した接地荷重計算値を取得する(ステップS3101)。ここで本実施例では、図8で述べた車両状態量推定装置1の処理概要を示すフローチャートのSTARTからENDまでを1処理として定義する。 First, the parameter updating unit 22 according to the seventh embodiment acquires the contact load calculation value calculated by the suspension control unit 40 (step S3101). Here, in this embodiment, one process is defined from START to END in the flowchart showing the outline of the process of the vehicle state quantity estimating apparatus 1 described in FIG.
 次にステップS3001で取得した接地荷重計算値をFFT処理することで接地荷重の変動周波数を算出する(ステップS3102)。 Next, the variation frequency of the ground load is calculated by performing FFT processing on the ground load calculated value acquired in step S3001 (step S3102).
 次に前後加速度Gや横加速度G、ヨーレートrなどの検出値と、式(25)と式(26)に基づいて接地荷重変動量推定値を算出する(ステップS3103)。 Next, a ground load variation estimation value is calculated based on detected values such as longitudinal acceleration G x , lateral acceleration G y , and yaw rate r, and equations (25) and (26) (step S3103).
 次にステップS3103で算出した接地荷重変動量推定値と、ステップS3101で取得した接地荷重計算値と、式(27)に基づいて接地荷重変動差を算出する(ステップS3104)。 Next, a ground load variation difference is calculated based on the estimated value of the ground load variation calculated in step S3103, the calculated ground load obtained in step S3101, and equation (27) (step S3104).
 次にステップS3102で算出した接地荷重変動周波数、ステップS3104で算出した接地荷重変動差に基づいて補正ゲインを算出する。補正ゲインの算出方法としては、接地荷重変動周波数と接地荷重変動差を入力とし、予め記憶された補正ゲインマップに基づいて導出する方法が考えられるが、数式であっても良く、補正ゲインを算出する方法を限定しない。 Next, a correction gain is calculated based on the ground load fluctuation frequency calculated in step S3102 and the ground load fluctuation difference calculated in step S3104. As a method of calculating the correction gain, a method of deriving based on a correction gain map stored in advance with the ground load fluctuation frequency and the ground load fluctuation difference as inputs can be considered. There is no limitation on the method to be performed.
 ステップS3105では、ステップS3105で算出した補正ゲインを用いてパラメータを補正する。補正する方法はパラメータに補正ゲインを乗算する方法が考えられるが、除算する方法であっても良く、補正ゲインによるパラメータの補正方法を限定しない。 In step S3105, the parameter is corrected using the correction gain calculated in step S3105. As a correction method, a method of multiplying a parameter by a correction gain is conceivable, but a method of division may be used, and the method of correcting the parameter by the correction gain is not limited.
 以上の構成により、外界認識手段2の検出値が不安定な状態で、接地荷重の変動が大きい悪路を走行している場合であっても接地状態に応じた適切なパラメータを用いることができるため、状態量推定部23においてより高精度な推定や、サスペンション制御ユニット40においてより高性能なサスペンション制御を実現できる。 With the above configuration, it is possible to use an appropriate parameter according to the ground contact state even when the detected value of the external environment recognition unit 2 is unstable and the vehicle is traveling on a rough road where the ground load varies greatly. Therefore, it is possible to realize more accurate estimation in the state quantity estimation unit 23 and higher-performance suspension control in the suspension control unit 40.
1:車両状態量推定装置、2:外界認識手段、3:加速度センサ、4:ジャイロセンサ、5:操舵角センサ、6:車輪速センサ、7:タイヤ、8:駆動制御ユニット、9:ブレーキ制御ユニット、10、10’:車両、11:重心点、12:ロール軸、13:ピッチ軸、14:重心点(設計値)、15:重心点(更新値)、16:質点、21:安定度判断部、22:パラメータ更新部、23:状態量推定部、24:出力値判断部、30:車両状態量出力装置、40:サスペンション制御ユニット、41:制御サスペンション装置、42:車高センサ、43:上下速度推定部、44:目標減衰力算出部、45:減衰力マップ、46:車両運動モデル、100:車両状態量検出値、200:車両状態量検出値 1: vehicle state quantity estimation device, 2: external recognition means, 3: acceleration sensor, 4: gyro sensor, 5: steering angle sensor, 6: wheel speed sensor, 7: tire, 8: drive control unit, 9: brake control Unit: 10, 10 ′: Vehicle, 11: Center of gravity, 12: Roll axis, 13: Pitch axis, 14: Center of gravity (design value), 15: Center of gravity (updated value), 16: Mass, 21: Stability Determination unit, 22: parameter update unit, 23: state quantity estimation unit, 24: output value determination unit, 30: vehicle state quantity output device, 40: suspension control unit, 41: control suspension device, 42: vehicle height sensor, 43 : Vertical velocity estimation unit, 44: target damping force calculation unit, 45: damping force map, 46: vehicle motion model, 100: vehicle state quantity detection value, 200: vehicle state quantity detection value

Claims (14)

  1.  慣性センサの検出値と、外界認識手段の検出値が入力され、該慣性センサの検出値と予め設定されている車両パラメータに基づいて車両の状態量を出力する車両状態量推定装置において、
     前記外界認識手段の検出値を用いて、前記車両パラメータを更新することを特徴する車両状態量推定装置。
    In a vehicle state quantity estimation device that receives a detection value of an inertial sensor and a detection value of an external recognition means, and outputs a state quantity of the vehicle based on the detection value of the inertial sensor and a preset vehicle parameter.
    A vehicle state quantity estimation apparatus, wherein the vehicle parameter is updated using a detection value of the external world recognition means.
  2.  前記外界認識手段の検出値が安定している場合に、前記車両パラメータを更新することを特徴とする請求項1に記載の車両状態量推定装置。 2. The vehicle state quantity estimating device according to claim 1, wherein the vehicle parameter is updated when a detected value of the outside world recognizing means is stable.
  3.  所定の期間における前記慣性センサの検出値と前記外界認識手段の検出値の差の時間積分値または平均値が、所定の値より小さい場合に、前記外界認識手段の検出値が安定していると判断することを特徴とする請求項2に記載の車両状態量推定装置。 When the time integral value or average value of the difference between the detected value of the inertial sensor and the detected value of the external field recognizing unit in a predetermined period is smaller than a predetermined value, the detected value of the external field recognizing unit is stable The vehicle state quantity estimation device according to claim 2, wherein the determination is made.
  4.  前記慣性センサの検出値と前記外界認識手段の検出値の差が所定の値より小さくなった時からのカウント値が、所定の値より大きくなった場合に、前記外界認識手段の検出値が安定していると判断することを特徴とする請求項2に記載の車両状態量推定装置。 When the count value from when the difference between the detected value of the inertial sensor and the detected value of the external environment recognizing means becomes smaller than a predetermined value, the detected value of the external environment recognizing means becomes stable. The vehicle state quantity estimation device according to claim 2, wherein the vehicle state quantity estimation device is determined to be.
  5.  前記外界認識手段から自己診断情報が入力され、該自己診断情報に基づいて、前記外界認識手段の検出値が安定していると判断することを特徴とする請求項2に記載の車両状態量推定装置。 3. The vehicle state quantity estimation according to claim 2, wherein self-diagnosis information is input from the external environment recognition means, and based on the self-diagnosis information, it is determined that a detection value of the external environment recognition means is stable. apparatus.
  6.  前記外界認識手段の検出値と前記状態量推定部の推定値の差が所定の値より大きい場合に、前記車両パラメータを更新することを特徴とする請求項1に記載の車両状態量推定装置。 2. The vehicle state quantity estimating device according to claim 1, wherein the vehicle parameter is updated when a difference between a detected value of the external environment recognizing means and an estimated value of the state quantity estimating unit is larger than a predetermined value.
  7.  前記慣性センサの検出値と、前記外界認識手段の検出値を入力として、前記車両パラメータを出力する特性マップを予め備え、前記特性マップに基づいて前記車両パラメータを更新することを特徴とする請求項1乃至5の何れかに記載の車両状態量推定装置。 2. A characteristic map for outputting the vehicle parameter with the detection value of the inertial sensor and the detection value of the external field recognition means as inputs, and updating the vehicle parameter based on the characteristic map. The vehicle state quantity estimation apparatus according to any one of 1 to 5.
  8.  前記車両パラメータは所定の上限値および所定の下限値の範囲内で更新されることを特徴とする請求項1に記載の車両状態量推定装置。 2. The vehicle state quantity estimating device according to claim 1, wherein the vehicle parameter is updated within a range of a predetermined upper limit value and a predetermined lower limit value.
  9.  請求項1乃至8の何れかに記載の車両状態量推定装置から更新された車両パラメータを入力される車両運動制御装置であって、前記更新された車両パラメータに基づいて、第二の車両の状態量を推定することを特徴とする車両運動制御装置。 A vehicle motion control device to which an updated vehicle parameter is input from the vehicle state quantity estimation device according to any one of claims 1 to 8, wherein the second vehicle state is based on the updated vehicle parameter. A vehicle motion control device characterized by estimating a quantity.
  10.  前記第二の車両の状態量に基づき、車両運動の制御指令値を出力する特性マップまたはゲインを予め備え、前記更新された前記車両パラメータに基づいて、前記特性マップまたはゲインを更新することを特徴とする請求項9に記載の車両運動制御装置。 A characteristic map or gain for outputting a vehicle movement control command value based on a state quantity of the second vehicle is provided in advance, and the characteristic map or gain is updated based on the updated vehicle parameter. The vehicle motion control device according to claim 9.
  11.  路面凹凸度合いを検出する手段と、前記路面凹凸度合いを入力とする特性マップまたはゲインを備え、前記特性マップまたはゲインを用いて、前記車両パラメータを補正することを特徴とする請求項1に記載の車両状態量推定装置。 The means for detecting a road surface unevenness level and a characteristic map or a gain with the road surface unevenness level as an input are provided, and the vehicle parameter is corrected using the characteristic map or the gain. Vehicle state quantity estimation device.
  12.  各タイヤの接地荷重の計算値の周波数を入力として、補正ゲインを出力するマップを予め備え、前記補正ゲインに基づいて更新された前記車両パラメータを補正することを特徴とする請求項1に記載の車両状態量推定装置。 The vehicle parameter updated according to claim 1, wherein a map for outputting a correction gain is provided in advance by inputting a frequency of a calculated value of a ground contact load of each tire, and the vehicle parameter updated based on the correction gain is corrected. Vehicle state quantity estimation device.
  13.  各タイヤの接地荷重の計算値の周波数が、所定の値より低い場合に、前記車両パラメータを更新することを特徴とする請求項1に記載の車両状態量推定装置。 2. The vehicle state quantity estimating device according to claim 1, wherein the vehicle parameter is updated when a frequency of a calculated value of a contact load of each tire is lower than a predetermined value.
  14.  タイヤ間の接地荷重の変動計算値と変動推定値の差が、所定の値より小さい場合に、前記車両パラメータを更新することを特徴とする請求項1に記載の車両状態量推定装置。 2. The vehicle state quantity estimating device according to claim 1, wherein the vehicle parameter is updated when a difference between a fluctuation calculation value of a ground contact load between tires and a fluctuation estimation value is smaller than a predetermined value.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113525393A (en) * 2020-04-17 2021-10-22 广州汽车集团股份有限公司 Vehicle longitudinal acceleration estimation method and system and computer equipment thereof
US11861953B1 (en) 2018-09-26 2024-01-02 Allstate Insurance Company Systems and methods for using sensors to determine a holistic vehicle characteristic and/or to generate parameters for a vehicle service

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6748765B1 (en) * 2019-06-25 2020-09-02 株式会社ショーワ Ground load estimating device, control device and ground load estimating method
JP6695481B1 (en) * 2019-06-25 2020-05-20 株式会社ショーワ Ground load estimating device, control device and ground load estimating method
JP6817483B1 (en) * 2020-06-29 2021-01-20 株式会社ショーワ Road surface load estimation device, vehicle control device and road surface load estimation method
JP7553413B2 (en) 2021-08-23 2024-09-18 先進モビリティ株式会社 Autonomous driving method

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH09245298A (en) * 1996-03-13 1997-09-19 Toyota Motor Corp Automatic running device for vehicle
JP2003168197A (en) * 2001-11-30 2003-06-13 Hitachi Ltd Method and device for recognizing traveling circumstance
JP2005349876A (en) * 2004-06-08 2005-12-22 Nissan Motor Co Ltd Vehicular turning and traveling control device
JP2009514721A (en) * 2005-11-02 2009-04-09 ローベルト ボツシユ ゲゼルシヤフト ミツト ベシユレンクテル ハフツング Method and apparatus for roll angle calculation for occupant protection device
JP5402244B2 (en) * 2009-05-26 2014-01-29 トヨタ自動車株式会社 Vehicle physical quantity estimation device

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH02151569A (en) * 1988-11-30 1990-06-11 Nissan Motor Co Ltd Vehicle motion estimating device
JP5137872B2 (en) * 2009-02-12 2013-02-06 三菱電機株式会社 Vehicle lateral disturbance estimation device
JP6478318B2 (en) * 2014-12-25 2019-03-06 株式会社Subaru Vehicle motion parameter estimation device

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH09245298A (en) * 1996-03-13 1997-09-19 Toyota Motor Corp Automatic running device for vehicle
JP2003168197A (en) * 2001-11-30 2003-06-13 Hitachi Ltd Method and device for recognizing traveling circumstance
JP2005349876A (en) * 2004-06-08 2005-12-22 Nissan Motor Co Ltd Vehicular turning and traveling control device
JP2009514721A (en) * 2005-11-02 2009-04-09 ローベルト ボツシユ ゲゼルシヤフト ミツト ベシユレンクテル ハフツング Method and apparatus for roll angle calculation for occupant protection device
JP5402244B2 (en) * 2009-05-26 2014-01-29 トヨタ自動車株式会社 Vehicle physical quantity estimation device

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11861953B1 (en) 2018-09-26 2024-01-02 Allstate Insurance Company Systems and methods for using sensors to determine a holistic vehicle characteristic and/or to generate parameters for a vehicle service
CN113525393A (en) * 2020-04-17 2021-10-22 广州汽车集团股份有限公司 Vehicle longitudinal acceleration estimation method and system and computer equipment thereof

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