Autonomous driving method

Abstract

To provide an automated driving method in which a change in a vehicle total weight, a change in a gravity center position, and a change in a tire cornering coefficient which are state amounts of a control model formula of an automated driving vehicle are detected (estimated) in real time, the detected state amounts are substituted into the control model formula, and thus automated driving is performed.SOLUTION: An automated driving method includes a linear formula of Y=AX-B relating the vehicle acceleration Y to the accelerator opening X, detects a dead weight change from the gradient A of the straight line and detects the gradient from the Y segment B, and acquires in real time a sideslip coefficient, a stability factor, and a cornering coefficient in a partial stationary circle captured with the horizontal equivalent curvature in order to address the need to detect in real time the sideslip coefficient, the stability factor, and the cornering coefficient that change due to paving of a road, wetting, and loading in the actual operation to update a parameter of a control model.SELECTED DRAWING: Figure 9

Description

[Technical field]

[0001] This invention relates to an automatic driving method in which internal and external state quantities are substituted into a control model equation. Here, the internal state quantities refer to vehicle state quantities such as mass, center of gravity position, and tire load due to passengers getting on and off and luggage loading, while the external state quantities refer to vehicle external state quantities such as the uphill / downhill gradient (longitudinal gradient) of the road on which the vehicle travels, road surface cant (lateral gradient), and road surface friction coefficient due to wetness of the road surface. [Background technology]

[0002] Vehicle motion is governed by Newton's second law. That is, it is governed by the relationship between force, mass, and acceleration, or torque, moment of inertia, and rotational acceleration. It is governed by the relationship between action and reaction, that is, the relationship between the "action of the vehicle running, turning, and stopping" and the "reaction from the road surface."

[0003] Self-driving vehicles are controlled by controlling the relationship between action and reaction. PID control, which is tuned by control engineers based on their experience while observing the control response, is the most widely used method.

[0004] The control of autonomous vehicles must adapt to changes in internal state quantities such as mass, center of gravity, and tire load that accompany changes in passengers, luggage, etc. These changes occur suddenly when passengers get on and off at train stations and bus stops, and when luggage is loaded and unloaded at logistics terminals, so there is no time to tune the PID control.

[0005] Tuning is the process of correcting the deviation from the target based on given characteristics. There are limitations to tuning a control object whose state quantities change. Rather than tuning without using a model or using a fixed specification model, it is necessary to use a control model (mathematical model) described by state quantities and adapt to the changes by rewriting (replacing) the state quantities of the model according to the changes in the state quantities.

[0006] The control of an autonomous vehicle must adapt to changes in external state quantities such as the road's longitudinal gradient, lateral gradient, curvature, and changes in road moisture (changes in the coefficient of friction on the tire contact surface). It must adapt to changes in the route it is traveling at that time and place. It is permissible to operate the vehicle with road information on longitudinal gradient, lateral gradient, and curvature prepared in advance, and with preliminary experimental data on changes in road moisture, but it is necessary to understand and adapt to changes at that time and place.

[0007] In Non-Patent Document 1, Chapter 9, State Feedback Control and Observers, it is stated that state feedback control is possible on the premise that all states of the controlled object can be observed, however, it is rare that all states of an actual controlled object can be observed, and in such cases, it is necessary to estimate the state using an observer, but there is no actual description of this.

[0008] Non-Patent Document 2 reports a method for modeling the longitudinal motion of a large truck, but does not mention estimation of state quantities such as changes in vehicle weight.

[0009] Non-Patent Document 3 introduces a method for estimating changes in the load and center of gravity by detecting the natural frequencies of bouncing and pitching of a truck, but this method lacks versatility because it depends on the specifications of the suspension system.

[0010] Non-Patent Document 4 reports on cant lane keeping analysis and modeling of autonomous trucks, but there are still issues with modeling at the transition points between curvature and cant.

[0011] Patent Document 1 claims to estimate the vehicle's weight from the accelerator percentage, but does not mention how to estimate the vehicle's weight from the accelerator percentage, making it unpractical for use as an on-board system. On the other hand, it does show a method for estimating road gradients, which is useful as a reference.

[0012] Patent Document 2 shows a formula for calculating the change in the center of gravity position derived from the vehicle motion equation, and shows a method for calculating the change in the center of gravity position from the vehicle speed, yaw rate, and sideslip angle detected by GPS. However, this is related to a two-axle vehicle and does not mention multi-axle vehicles with three or four axles, and does not show a method for estimating the change in the vehicle's own weight, which is the cause of the change in the center of gravity position.

[0013] Patent Document 3 discloses a method for detecting the center of gravity position of a four-axle vehicle by using a wheel speed sensor and a yaw angle sensor without using GPS, but does not disclose a method for estimating the change in vehicle weight, which is the cause of the change in the center of gravity position. [Prior art documents] [Patent documents]

[0014] [Patent Document 1] Patent No. 6890154 [Patent Document 2] JP 2021-70377 A [Patent Document 3] Patent Publication No. 2021-84587 [Non-patent literature]

[0015] [Non-Patent Document 1] Edited by Kenzo Nonami and co-authored by Hidekazu Nishimura; Fundamentals of Control Theory Using MATLAB (registered trademark) [Non-Patent Document 2] Fujio Momiyama et al.; “Identification and Modeling of Longitudinal Motion of Large Trucks” Transactions of the Society of Automotive Engineers of Japan, Vol. 43, No. 2, March 2012, No. 20124209, pp. 211-216 [Non-Patent Document 3] Fujio Momiyama et al.; Loading State Estimation of Lateral Motion Model for Autonomous Large Trucks, Transactions of the Society of Automotive Engineers of Japan, Vol. 44, No. 6, November 2013, No. 20134847, p. 1377-1382. [Non-Patent Document 4] Fujio Momiyama et al.; Cant Lane Keeping Analysis and Modeling for Autonomous Trucks, Transactions of the Society of Automotive Engineers of Japan, Vol. 45, No. 6, November 2014, No. 20144800, p. 1027-1034. Summary of the Invention [Problem to be solved by the invention]

[0016] The above-mentioned conventional technologies are unable to accurately estimate the amount of change in the internal and external state quantities of the control model of an autonomous vehicle, which change instantly when the vehicle is stopped or change from moment to moment while the vehicle is traveling, and are unable to reflect these changes in the control model in real time to enable continuous autonomous driving. [Means for solving the problem]

[0017] In order to solve the above problems, the present invention provides a vehicle state variable In this automatic driving method, an equation for controlling driving, described above, is incorporated into an on-board computer (ECU), vehicle state quantities estimated in real time are input while being updated into the equation, and a signal for controlling driving calculated by the equation is output from the on-board computer, wherein the state quantities are the vehicle's own weight, slope gradient, center of gravity position, and tire cornering coefficient, the vehicle's own weight and slope are estimated from the acceleration (deceleration) generated in response to an accelerator input, the center of gravity position is estimated by an air pressure sensor mounted on an air suspension device of the rear axle, and the tire cornering coefficient is estimated from the changing characteristics of the vehicle body side slip angle and turning radius generated when the vehicle turns, the generated acceleration in response to an accelerator input which estimates the vehicle's own weight and slope is calculated, and the accelerator opening degree for the required acceleration is determined from this generated acceleration.

[0018] In the above, the longitudinal gradient and vehicle weight are estimated using the vehicle longitudinal motion model equation, i.e., the "equation of acceleration generated in response to accelerator input" derived from the balance equation between engine output and running resistance. Specifically, the longitudinal gradient is estimated from the deceleration that occurs when the accelerator is released, and the vehicle weight is estimated from the acceleration that occurs when the accelerator is subsequently released. The front axle load is calculated from the estimated vehicle weight, the rear axle load detected by the air pressure of an air pressure sensor mounted on the rear air suspension, and the difference between the estimated vehicle weight and the detected rear axle load, and the position of the center of gravity is calculated from the relationship between the front axle load, the rear axle load, and the wheelbase, which is a vehicle specification.

[0019] In the above, the road friction coefficient is a tire cornering coefficient equation derived from a vehicle lateral motion model equation, i.e., a balance equation between the centrifugal force generated in a vehicle making a turn and the centripetal force due to the frictional force generated between the tire and the road surface. The tire cornering coefficient equation includes the "stability factor" and "side slip factor" which are indicators of the lateral motion characteristics of the vehicle. corner The "coefficients" are estimated by identifying the actual vehicle. This makes it possible to automatically control the acceleration / deceleration movement in the forward / rearward direction, and the lateral movement and rotational movement in the lateral direction, adapting to the conditions at that time and place.

[0020] Furthermore, changes in the internal state quantities of the vehicle's own weight, center of gravity position, and front and rear axle loads occur when the vehicle stops to allow passengers to get on and off at stations and bus stops, and when the vehicle stops to load and unload luggage at logistics terminals. Therefore, after the vehicle restarts, it will operate at a speed range (20 to 30 km / h) where the accelerator is turned off / on to detect the vehicle's own weight until the changes in the state quantities are detected, substituted into the control model equation, and reflected in the autonomous driving, and then it will operate autonomously at normal speed. Effect of the Invention

[0021] According to the present invention, it is possible to realize a control model required for controlling the longitudinal and lateral movements of an autonomous vehicle and state feedback control that adapts to changes in the state quantities of that model in real time. [Brief description of the drawings]

[0022] [Figure 1] FIG. 2 is an explanatory diagram of a longitudinal motion model and weight estimation and gradient estimation. [Diagram 2] FIG. 11 is an explanatory diagram of a change in load on a power performance diagram expressed in terms of acceleration. [Diagram 3] FIG. 4 is an explanatory diagram of a method for estimating a road gradient. [Figure 4] 1 is an explanatory diagram of a method for detecting a change in the position of the center of gravity of a vehicle by equipping an air suspension device of a rear axle with an air pressure sensor. [Diagram 5] FIG. 2 is an explanatory diagram of a lateral motion model and internal state quantities. [Figure 6] 4 is a graph showing a change in turning characteristic of a vehicle relative to vehicle speed; FIG. [Figure 7] FIG. 2 is an explanatory diagram of a conversion formula from the cant curve curvature of an actual road to the equivalent curvature of a horizontal road. [Figure 8] 1 is an explanatory diagram of a method for detecting a cant angle and a curvature of a road. [Figure 9] FIG. 11 is an explanatory diagram of a flow of state estimation. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] An embodiment of the present invention will be described with reference to FIGS. Figure 1 shows the longitudinal motion model and the estimation of vehicle weight and gradient. (A) is a simplified diagram of the drive system with the equivalent moment of inertia of the vehicle around the drive shaft as Ieq. E is the engine torque, ω is the engine speed, imn is the gear ratio of the transmission, if is the final reduction ratio, ωw is the wheel speed, v is the vehicle speed, r is the wheel radius of rotation, Rr, Rd, R θ are the rolling resistance, air resistance, and gradient resistance, respectively. The balance between engine output and running resistance can be expressed simply by ignoring transmission efficiency, etc., as shown in (B) in the figure.

[0024]

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[0025] From equation (1), we obtain equation (2) for the generated acceleration in response to accelerator input, as shown in (C) in the figure. The left side is the vehicle acceleration α, and the first term on the right side is the engine torque T E This is the acceleration generated by the engine, calculated by multiplying the driving force by the gear ratio imn and the final reduction ratio if of the transmission, divided by the tire radius r, and dividing it by the vehicle mass converted around the drive shaft. The second term on the right side is the deceleration equivalent to rolling resistance αr, the deceleration equivalent to air resistance αd, and the deceleration equivalent to gradient resistance α θ It is.

[0026]

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[0027] The vehicle acceleration α on the left side of equation (2) is Y, the part in brackets on the right side is A, and the accelerator opening X = engine torque T E If the deceleration in parentheses in the second term on the right side is B, the relationship between the accelerator opening (X) and the vehicle acceleration (Y) is expressed as a linear equation (3) of Y = AX - B. A corresponds to the acceleration resistance in Figure 2, which will be described later, and B corresponds to the coasting resistance.

[0028]

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[0029] That is, on a flat road, the rolling resistance is calculated from the Y-axis intercept B of equation (3). Equivalent deceleration αr and air resistance Equivalent deceleration The sum of αd is detected as the B value, and the rolling resistance is calculated. Equivalent deceleration If we ignore the change in air volume as being insignificant, the air resistance Equivalent deceleration Since value B does not depend on the empty load, it is determined as the same value as the empty load, and the loaded weight (total vehicle weight) can be calculated by multiplying the ratio of "A when empty / A when loaded" from value A in formula (3) by the unladen weight (vehicle weight).

[0030] For reference, Figure 2 shows the change in load on the power performance diagram expressed in terms of acceleration. An example of a fully automatic electronic mechanical 12-speed transmission is shown. The left side of the figure shows an example of an empty load (1.7 tons), and the right side shows an example of a 12-ton load. The graph shows the acceleration generated at each gear position of the transmission from 1st to 12th when accelerating with the accelerator at 100% to a speed of 90 km / h, and the deceleration when the accelerator is released from a speed of 80 km / h to zero. The relationship between vehicle speed and acceleration is expressed by a hyperbolic curve (5) y = a / x, where y is the acceleration and x is the vehicle speed, and it can be seen that the hyperbolic constant a is inversely proportional to the change in the total vehicle weight (empty weight).

[0031] The A value on an uphill road with a gradient of ±θ% will be the A value for a flat road (θm / s^2), so the A value on that slope is calculated by subtracting the acceleration / deceleration for the gradient, "θm / s^2", from the A value on that slope. As described above, the loaded weight (total vehicle weight) can be calculated by multiplying the unladen weight (vehicle weight) by the ratio of "A value when empty / A value when loaded."

[0032] (D) in the figure shows how to obtain acceleration and deceleration during actual driving. A vehicle speed at which the AMT does not shift gears (25km / h in this case) is selected, and the deceleration when the accelerator is released (DCC) and the acceleration when an 80% accelerator opening pulse input is input (ACC) are obtained, and a graph of the generated acceleration versus accelerator opening shown in (E) in the figure is obtained.

[0033] The linear equation Y=AX-B connecting the average DCC and the average ACC can be used to obtain equation (4) of X=(Y+B) / A, from which the accelerator opening X for the required acceleration Y can be determined.

[0034]

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[0035] Figure 2 shows the load change in the power performance diagram expressed in terms of acceleration. An example of a fully automatic electronic mechanical 12-speed transmission is shown. Weight 11.7 tons), 12 tons loaded on the right side (total vehicle weight weight The following shows an example of a vehicle with a maximum load of 23.7 tons (approx. 100% acceleration). The graph shows the acceleration generated in each gear position from 1st to 12th in the process of accelerating with the accelerator pedal at 100% to a speed of 90 km / h, and the deceleration in the process of letting go of the accelerator pedal from a speed of 80 km / h to zero. The relationship between vehicle speed and acceleration is expressed by the hyperbolic equation (5) y = a / x, where y is the acceleration and x is the vehicle speed, and it can be seen that the hyperbolic constant a is inversely proportional to the change in the total vehicle weight (empty weight).

[0036]

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[0037] Also, the deceleration changes inversely proportional to the square of the vehicle speed due to air resistance, with the rolling resistance as the y-axis intercept. a / x corresponds to A in the above Y=AX-B. In other words, the change in the vehicle's own weight can be detected from A. Also, in the figure, y=-0.0003449x 2 We can understand that -0.006 = 0.0003449 is the drag coefficient of air resistance, and 0.06 is the rolling resistance. In other words, the gradient of a slope can be detected from the B value on flat ground and the B value on a slope.

[0038] FIG. 3, citing Patent Document 1, shows a method for estimating road gradient from wheel speed and an accelerometer. This is used in conjunction with the above paragraph (0030) to ensure reliability. An accelerometer is equipped to detect longitudinal acceleration. The longitudinal acceleration is zero when stationary on a flat road, but the acceleration Gx(static) when stationary on a slope with a gradient θ is given by equation (6) where the acceleration of gravity (-9.81m / s^2) is multiplied by Sinθ. The accelerometer reading Gx(run) while running is given by equation (7) where the circumference of the drive wheel 2πr is multiplied by the rotational speed ω of the drive wheel. From equations (6) and (7), the slope gradient is given by equation (8).

[0039]

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[0040] Fig. 4 shows an explanatory diagram of a method for detecting the center of gravity position by equipping the pneumatic suspension of the rear axle with an air pressure sensor. The upper left side of Fig. 4 shows a plan view of the vehicle, the lower left side shows a side view of the vehicle, and the right side shows an air piping diagram of the pneumatic suspension of the rear axle. Changes in vehicle weight are detected as described in paragraphs (0029) to (0031). The change in air pressure (Δp L、 Δp R ) to detect the rear axle load change (ΔNr) and convert it into the empty rear axle load N Er Add to the loaded axle load N Lr The detected rear axle load change is subtracted from the vehicle weight change to calculate the front axle load change, which is then added to the unladen front axle load to obtain the loaded front axle load. The wheelbase (l) is multiplied by the rear axle load / vehicle weight ratio to obtain the distance from the front axle to the center of gravity (l when unladen). Ef And when loading Lf ) can be seen. Lf and when empty Ef The change in the center of gravity can be determined from the difference.

[0041]

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[0042] Here, Δp Lis the change in air pressure due to the load change of the left air spring, Δp R is the change in air pressure due to the load change of the right air spring, ΔNr is the change in rear axle load, ΔN f is the change in front axle load, N Er is the rear axle load, N Ef is the front axle unladen load, N Lr is the rear axle load, N Lf is the front axle load, GVW is the total vehicle weight (weight when loaded), VW is the vehicle weight (weight when unloaded), l is the wheelbase, l Ef is the distance from the front axle to the center of gravity of the empty vehicle, l Lf is the distance from the front axle to the center of gravity of the loaded vehicle, and Δl is the change in the center of gravity due to the loaded vehicle.

[0043] We will explain the air piping diagram for the rear axle air suspension on the right side of Figure 4. In the diagram, R1RF refers to the right front air spring of the first rear axle, R1RR refers to the right rear air spring of the first rear axle, R1LF refers to the left front air spring of the first rear axle, R1LR refers to the left rear air spring of the first rear axle, R2RF refers to the right front air spring of the second rear axle, R2RR refers to the right front and rear air springs of the second rear axle, R2LF refers to the left front air spring of the second rear axle, and R2LR refers to the left rear air spring of the second rear axle.

[0044] The right front and rear air pipes of the first rear axle and the right front and rear air pipes of the second rear axle are connected via a joint, and similarly the left front and rear air pipes of the first rear axle and the left front and rear air pipes of the second rear axle are connected via a joint, with air being supplied from the rear air tank to each of the right and left connecting pipes via a rear axle height adjustment solenoid valve. The rear axle height adjustment solenoid valve receives a vehicle height signal from a vehicle height stroke sensor (not shown) installed on the right and left sides of the first or second rear axle, and supplies air from the rear air tank to the right and left connecting pipes to set the vehicle height to a set value. An air pressure sensor is connected to each of the right and left connecting pipes to detect changes in air pressure (Δp L ,Δp R ) and detects the change in center of gravity position due to the change in weight using the above equations (9) to (13).

[0045] Figure 5 shows the lateral motion model and its internal state quantities. On the left side of the figure, a plan view of a four-axle vehicle consisting of the first front axle, the second front axle, the first rear axle, and the second rear axle is shown in the ISO coordinate system with the front of the vehicle as the x-axis and the left side of the vehicle as the y-axis, and on the right side of that is a simplified diagram in which the left and right wheels of each axle are represented as single wheels on the x-axis. The simplified diagram will be explained below.

[0046] The xy coordinates are moving coordinates with the origin at the center of gravity of the vehicle. The equation for balance of motion in this case is as follows:

[0047]

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[0048] Here, m is the vehicle mass, I is the moment of inertia, v is the vehicle speed, r is the yaw rate, β is the vehicle side slip angle, CF1, CF2, CF3, and CF4 are the cornering forces of the first front axle, the second front axle, the first rear axle, and the second rear axle, Kf1, Kf2, Kr1, and Kr2 are the cornering powers of the first front axle, the second front axle, the first rear axle, and the second rear axle, β f1 , β f2 , β r1 , β r2 , are the tire slip angles of the first front axle, the second front axle, the first rear axle, and the second rear axle, Ccf and Ccr are the cornering coefficients of the first front axle and the second front axle tires, and the first rear axle and the second rear axle tires, N f1 ,N f2 ,N r1 ,N r2 , are the axle loads of the first front axle, the second front axle, the first rear axle, and the second rear axle, δ1, δ2 are the actual steering angles of the first front axle and the second front axle, a k is the angle ratio of δ1 to δ2. Setting lf1 to zero allows application to a four-axle vehicle with two front axles and two rear axles, or a three-axle vehicle with one front axle and two rear axles, setting lr1 to zero allows application to a three-axle vehicle with two front axles and one rear axle, or setting lf1 and lr1 to zero allows application to a two-axle vehicle with one front axle and one rear axle.

[0049] As shown in Figure 4, the air springs of the first and second rear axles are connected, so the axle load of the first and second rear axles is equal. For the axle load of the first and second front axles, leaf springs are usually used instead of air springs, but an equalizer function or similar design is used so that the axle load of the first and second front axles is roughly equal, so the above formulas (9) to (13) can be used to calculate the change in axle load due to a change in vehicle weight.

[0050] Figure 6 shows the change in the vehicle's turning characteristics with respect to vehicle speed. The vehicle is turning at a radius of R0. The vehicle is turning at a slow speed, and the steering angle (steering angle) is determined so that the vehicle can turn at a constant radius of R0. The vehicle is then gradually accelerated (V low →V high ), the turning radius changes (Rv_low → Rv_high) as shown in the figure, and the vehicle side slip angle changes (β0 → β v ), where vehicle speed is the speed of the center of gravity, and vehicle sideslip angle is the sideslip angle of the center of gravity. The speed of the center of gravity is obtained by GPS, and vehicle sideslip angle is the difference between the center of gravity speed azimuth and the vehicle azimuth, or the cosine angle of the center of gravity speed and the vehicle longitudinal speed, obtained by GPS (or by equipping a sideslip angle meter). If a full circle turn of radius R0 is not possible due to the area of ​​the turning field, a partial circle turn is acceptable. A partial circle turn using the above-mentioned "steering wheel angle (holding steering angle) that allows a turn at a constant radius R0" is performed at different vehicle speeds, and the above-mentioned "change in turning radius (Rv_low → Rv_high)" and "change in vehicle sideslip angle (β0 → β v )" on the horizontal axis, and β on the vertical axis. v Taking / β0 and Rv / R0, we obtain the change characteristics of the sideslip angle and the turning radius, and calculate the sideslip angle coefficient K β0 and obtain the stability factor Ksf, where β v Since the "1.0" point at zero vehicle speed for / β0 and Rv / R0 is uniquely determined, the actual measurement point v 2 By obtaining at least one level and connecting it to the "1.0" point, the horizontal slip corner The coefficient, stability factor, is obtained. cornerAn equation for the tire cornering coefficient (a physical quantity equivalent to the tire's road friction coefficient) including the coefficient and stability factor is prepared to adapt to changes in load and road friction during actual operation, which will be described later in Figure 7.

[0051] Side slip corner Coefficient (K β0 ) and stability factor (K sf ) are derived from the above-mentioned state equations (14) and (15) and expanded to obtain equations (24) and (25).

[0052]

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[0053] Here, in both equations (24) and (25), by setting lf1 to zero, it is possible to apply the formula from a four-axle vehicle with two front axles and two rear axles to a three-axle vehicle with one front axle and two rear axles, by setting lr1 to zero, it is possible to apply the formula to a three-axle vehicle with two front axles and one rear axle, and by setting lf1 and lr1 to zero, it is possible to apply the formula to a two-axle vehicle with one front axle and one rear axle. K β0 From equation (24), the cornering coefficient Ccf of the front axle tires is given by equation (26).

[0054]

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[0055] We obtain equation (27) from equation (25) for Ksf.

[0056]

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[0057] Substituting equation (26) into equation (27) gives equation (28), which can be rearranged to give equation (29).

[0058]

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[0059] The root of the quadratic equation (29), i.e., the rear axis tire The cornering coefficient is given by equation (30). By substituting equation (30) into equation (26), we obtain the front axle tire cornering coefficient equation (31).

[0060]

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[0061] Side slip included in equation (31-2) corner Coefficient K β0 By substituting the experimentally identified values ​​for the stability factors Ksf included in equations (31-3) and (31-4) and substituting them into equations (30) and (31) together with equations (30-1) and (30-5), the rear and front axles are tire The cornering coefficient can be estimated.

[0062] As described above, in Fig. 6, a circular turn or partial circular turn with a radius of R0 is performed to obtain the sideslip coefficient, stability factor, and tire cornering coefficient used for vehicle control. This method is performed in advance at a vehicle manufacturing plant, a vehicle operating company, or a vehicle maintenance company's premises, and data is acquired to prepare for control of actual operation. However, in actual operation, the sideslip coefficient, stability factor, and tire cornering coefficient may change depending on the road surface, moisture, and loading. corner Since the coefficient, stability factor, and tire cornering coefficient change, it is necessary to detect them in real time and update the parameters of the control model. Furthermore, since the road surface has a cant, it is necessary to detect the cant and update the parameters of the control model by taking into account the influence of the cant. tire The cornering coefficient must be obtained in real time and used for control.

[0063] Figure 7 explains the conversion formula from the canted curved road curvature of an actual road to the equivalent curvature of a horizontal road. A photograph of the rear of a truck is shown turning while maintaining its posture perpendicular to a canted road surface. The vehicle height adjustment valves installed on the left and right sides of the air suspension on the rear axle enable steady turning with a roll angle of zero. The origin is "Point O," where the vertical center of the rear of the vehicle hits the road surface, with the z axis perpendicular and the y axis horizontal. There is a cant of the road surface at an angle of "ξ." The cant angle component force "G_inside" (Equation 33) of the gravitational acceleration Gz (Equation 32) acting on "Point O" acts in the downward direction of the cant angle, and the centrifugal acceleration "G_outside" (Equation 34) due to the speed "v (m / s)" along the road curvature acts in the upward direction of the cant angle. In other words, the horizontal road equivalent curvature "ρ_plane", which is the curvature conversion of the lateral acceleration obtained by subtracting the centripetal acceleration due to cant from the centrifugal acceleration caused by the curvature of the route, is expanded from the relationship lateral acceleration = vehicle speed squared × curvature to obtain equations (35) and (36).

[0064]

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[0065] That is, the road surface condition and the lateral slippage that changes depending on the wetness and loading of the road during actual operation. corner In order to meet the need to detect the coefficient, stability factor, and tire cornering coefficient in real time and update the parameters of the control model, the coefficient of sideslip, stability factor, and tire The cornering coefficient is obtained in real time and used for control.

[0066] A method for detecting the cant angle and curvature of a road is explained in Fig. 8. The cant angle ξ included in equations (33) to (36) for calculating the horizontal equivalent curvature on the canted curved road in Fig. 7 must be detected as road data or in real time and used. A method for detecting the cant angle in real time is explained using equations (37), (38), and (39).

[0067] The right side of Fig. 8 shows the attitude state quantities of a vehicle traveling on a canted curved road. The vehicle leans to the left due to the left cant ξ, which generates a roll angle φ. In order to suppress the tendency to turn downward through the cant angle, the vehicle moves straight ahead with an actual steering angle δ and vehicle body sideslip angle β directed upward through the cant, and steering for turning to the left is added, generating a yaw rate and turning. In this state, the lateral acceleration Gy measured by the lateral accelerometer mounted on the vehicle is expressed by equation (37), which is the difference between the centripetal acceleration due to the cant angle and roll angle and the centrifugal acceleration due to the vehicle speed and yaw rate.

[0068]

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[0069] The roll angle φ in equation (37) becomes zero in the steady state due to the auto-leveling mechanism of the air suspension, so the path curvature is calculated using equation (38) and the path curvature is calculated using equation (39). road The cant angle can be detected.

[0070]

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[0071] The flow of state estimation is shown in Figure 9. This flow includes steps 1 and 2, which are related to the state quantities provided in the vehicle beforehand, and steps 3 to 5, which are for adapting to state changes during the driving process. Step 1 is a step for confirming that the vehicle's own weight, center of gravity position, and tire cornering coefficient, the state of which is to be estimated, are parameters (state quantities) required by the control model, and that their numerical values ​​are set (set) to empty vehicle state values. Step 2 is to confirm that an empty vehicle identification experiment of the tire cornering coefficient has been conducted and that the identified value is installed in the model. Note that the cornering coefficient identification experiment here is not limited to an experiment on a dry road surface, but it is also desirable to conduct an experiment on a wet road surface and an experiment on an icy and snowy road surface to understand the rate of decrease in the value obtained on a dry road on a wet and icy and snowy road surface, and to verify the actual road value in step 5 described later to ensure reliability assurance.

[0072] Process 3 is the process of detecting changes in rear axle load that occur due to changes in the number of passengers and freight during operation. Changes in rear axle load that occur due to changes in the number of cargoes at truck terminals and changes in the number of passengers getting on and off at bus terminals and bus stops are detected by air pressure sensors installed on the rear axle suspension, and this value is added to the unladen rear axle load to recognize the loaded rear axle load. In step 4, the total vehicle weight is detected in accordance with changes in the number of passengers and load during operation, the center of gravity is detected, and the front and rear loads are recognized. Immediately after the rear axle load is detected, the vehicle is driven at a reduced speed, and the accelerator is turned on and off at a speed of 20-30km / h on a flat road to detect the vehicle's own weight. The center of gravity is calculated based on the rear axle load value already detected, and the front and rear axle loads are recognized together with the total vehicle weight. In step 5, data is sampled during partial circle turns to detect the tire cornering coefficient under actual loaded vehicle conditions, and the model specifications are updated after comparing the data with those from step 2 above to determine their validity. The locations where partial circle turns can be sampled, i.e., locations where data on steering angle, steering time, and vehicle speed can be obtained during partial circle turns, are determined within the operation process, and the loaded vehicle lateral slip is calculated. corner Detecting coefficients and loaded vehicle stability factors; tire Substitute the specifications obtained in steps 3 and 4 into the cornering coefficient formula and calculate the actual road tire The cornering coefficient is obtained, and the control model parameters are rewritten to the parameters obtained in steps 3, 4, and 5 for use in control.

[0073] As described above, the present invention relates to a method for detecting state quantities in order to detect changes in the state quantities of a vehicle, i.e., total vehicle weight, center of gravity position, and tire load, that accompany changes in passengers and luggage, and to perform adaptive control while updating the state quantities of a control model that correspond to those changes. That is This method detects changes in rear axle load using an air pressure sensor installed on the rear axle suspension, and adds this to the unladen rear axle load to detect the loaded rear axle load. This method detects the vehicle's own weight when the accelerator is turned ON and OFF, and detects the center of gravity position in comparison with the rear axle load that has already been detected, recognizing the front and rear axle loads along with the total vehicle weight. This method captures partial circular turning scenes on actual roads, samples data, and detects the tire cornering coefficient under actual loaded vehicle conditions. Thus, the present invention addresses the need for adaptive control of an autonomous vehicle to accommodate changes in the vehicle's forward and backward motion characteristics and lateral motion characteristics associated with changes in passenger and load.

Claims

1. An automatic driving method in which a formula for controlling driving described by a state quantity of a vehicle is incorporated in an on-board computer (ECU), a state quantity of the vehicle estimated in real time is input while being updated to the formula, and a signal for controlling driving calculated by the formula is output from the on-board computer, The state quantities are the vehicle weight, the gradient of the slope, the position of the center of gravity, and the tire cornering coefficient, and the vehicle weight and the gradient of the slope are estimated from the acceleration (deceleration) generated in response to an accelerator input, the position of the center of gravity is estimated by an air pressure sensor attached to the pneumatic suspension of the rear axle, and the tire cornering coefficient is estimated from the change characteristics of the vehicle body side slip angle and turning radius generated when the vehicle turns. The acceleration generated in response to the accelerator input for estimating the vehicle weight and the gradient of the slope is calculated by the following equation (2), where the vehicle acceleration α on the left side of equation (2) is the required acceleration Y, (i mn i f / r) / m eq on the right side of equation (2) is A, the accelerator opening X = engine torque T E , and (α r + α d + α θ ) in the second term on the right side of equation (2) is B, the accelerator opening X (%) is plotted on the horizontal axis, and the acceleration Y (m / s 2 ) to obtain equation (3) of Y = AX - B, the loaded weight is estimated by multiplying the unladen weight by A when unladen / A when loaded, the gradient of the location is estimated by subtracting the flat road B value from the B value of the location, and the accelerator opening X for the required acceleration Y is determined by obtaining X = (Y + B) / A from Y = AX - B. [0010] In equation (2), α is the vehicle acceleration, i ｍｎ is the gear ratio of the transmission, i ｆ is the final drive ratio, r is the tire radius, m ｅｑ is the equivalent mass of the vehicle including the drivetrain, α ｒ is the deceleration equivalent to rolling resistance, α ｄ is the air resistance-driven deceleration, α θ is the deceleration equivalent to gradient resistance. [0025]

2. In the automatic driving method according to claim 1, the estimated value of the center of gravity position is calculated based on the air pressure change (Δp L , Δp R ) and detect the rear axle load change (ΔNr) and convert it into the empty rear axle load N Er Add to the loaded axle load N Lr The change in the rear axle load is detected, and the change in the front axle load is calculated by subtracting the detected change in rear axle load from the change in vehicle weight. This is then added to the unladen front axle load to obtain the loaded front axle load. The wheelbase (l) is multiplied by the ratio of the rear axle load to the vehicle weight to obtain the distance from the front axle to the center of gravity (l when unladen). Ef And when loading Lf ) and calculate the loading time l Lf When empty Ef An automatic driving method characterized by estimating the center of gravity position from the difference between the above.

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