JP2018084899A - Autonomous travel vehicle, controller, computer program, control method of autonomous travel vehicle - Google Patents

Abstract

PROBLEM TO BE SOLVED: To suppress a decrease in accuracy of autonomous traveling caused by a measurement error of a turning angle. An autonomous traveling vehicle 100 includes a controller 10 and a sensor 21 for measuring a turning angle θ of the autonomous traveling vehicle 100. The controller 10 executes the non-linear processing for the turning angle deviation e3, which is the difference between the turning angle θ of the autonomous traveling vehicle 100 and the turning angle θr of the virtual reference vehicle 200. The controller 10 executes a control process for controlling the running of the autonomous traveling vehicle 100 so as to follow the reference vehicle 200 based on the non-linearized turning angle deviation i (e3). The non-linearization process is a process of reducing the absolute value of the turning angle deviation e3 when the absolute value of the turning angle deviation e3 is smaller than the predetermined value ε. [Selection diagram] Fig. 1

Description

  The present invention relates to an autonomous vehicle or the like.

  For the autonomous running of the vehicle, it is required to measure the state of the vehicle (see, for example, Patent Document 1).

Japanese Patent Laid-Open No. 2015-36840

  For example, path following control may be used for autonomous traveling of the vehicle. The route following control is a control for causing the actual vehicle to follow the path of the virtual reference vehicle. In the route following control, a relative error between the actual vehicle and the reference vehicle is used. The relative error includes, for example, a lateral deviation and a turning angle deviation.

  In order to obtain the turning angle deviation, the turning angle of the actual vehicle is measured. The turning angle of the vehicle is measured as, for example, an azimuth angle of the vehicle. For example, a magnetic sensor that measures the magnetic azimuth is used to measure the azimuth.

  A measurement error may occur in the measurement of the turning angle. The measurement error of the turning angle reduces the accuracy of autonomous driving. For example, even an expensive magnetic sensor has a minute measurement error, and an inexpensive sensor has a larger measurement error.

  It is desired to suppress a decrease in accuracy of autonomous running caused by a measurement error of the turning angle.

  One aspect of the present invention is an autonomous vehicle. In the embodiment, the autonomous vehicle includes a controller and a sensor that measures a turning angle of the autonomous vehicle. The controller includes a non-linearization process for a turn angle deviation which is a difference between a turn angle of the autonomous vehicle and a virtual reference vehicle, and the reference vehicle based on the turn angle deviation subjected to the non-linear process. And a control process for controlling the travel of the autonomously traveling vehicle so as to follow the vehicle. The non-linearization process is a process of reducing the absolute value of the turning angle deviation when the absolute value of the turning angle deviation is smaller than a predetermined value.

  Another aspect of the present invention is a controller. Another aspect of the present invention is a computer program. Another aspect of the present invention is a method for controlling an autonomous vehicle.

It is a block diagram of a vehicle. It is explanatory drawing of path | route following control. It is a graph which shows a nonlinear function. It is a flowchart of the process by a controller. It is a figure which shows a simulation result. It is a figure which shows a simulation result. It is a figure which shows a simulation result. It is a figure which shows a simulation result. It is a figure which shows a simulation result. It is a figure which shows a simulation result. It is a figure which shows a simulation result. It is a figure which shows a simulation result. It is a figure which shows a simulation result. It is a figure which shows a simulation result.

[1. Outline of Embodiment]

[Section 1]
The autonomous traveling vehicle according to the embodiment includes a controller and a sensor that measures a turning angle of the autonomous traveling vehicle. The controller executes a non-linearization process for a turning angle deviation that is a difference between a turning angle of the autonomously traveling vehicle and a turning angle of a virtual reference vehicle. The controller executes a control process for controlling the travel of the autonomous vehicle so as to follow the reference vehicle based on the turning angle deviation subjected to the non-linear process. The non-linearization process is a process of reducing the absolute value of the turning angle deviation when the absolute value of the turning angle deviation is smaller than a predetermined value. When the absolute value of the turning angle deviation is smaller than a predetermined value, the absolute value of the turning angle deviation can be reduced to suppress a decrease in the accuracy of autonomous traveling even if a measurement error of the turning angle occurs. it can.

[Section 2]
It is preferable that the control process is a process for controlling traveling by reverse optimal control. Inverse optimal control has robustness against disturbance generated when the vehicle travels on rough terrain.

[Section 3]
The controller is configured to further execute a conversion process of the turning angle deviation subjected to the non-linear process, and the conversion process includes the turning angle deviation subjected to the non-linear process, the position of the autonomous vehicle, and the reference vehicle. A lateral deviation function value which is a difference in the horizontal direction with respect to the position is added to obtain a converted turning angle deviation, and the control process is executed based on the converted turning angle deviation. Is preferred. In this case, the converted turning angle deviation is a function of the lateral deviation and the turning angle deviation.

[Section 4]
The function is preferably h (z) = Az / (1 + Bz ² ). Here, h (z) is a function satisfying zh (z)> 0, ∀z ≠ 0 and h (0) = 0, A and B are setting parameters that take positive values, and z = E ₂ v, e ₂ is the lateral deviation, and v is the speed of the autonomous vehicle. In this case, the controller can be easily adjusted.

[Section 5]
The controller according to the embodiment, based on the non-linearization processing for the turning angle deviation that is the difference between the turning angle of the autonomous vehicle and the turning angle of the virtual reference vehicle, And a control process for controlling the travel of the autonomous vehicle so as to follow the reference vehicle. The non-linearization process is a process of reducing the absolute value of the turning angle deviation when the absolute value of the turning angle deviation is smaller than a predetermined value.

[Section 6]
The computer program according to the embodiment, the computer program, the non-linearization processing for the turning angle deviation that is the difference between the turning angle of the autonomous vehicle and the turning angle of the virtual reference vehicle, the non-linearization processing to the turning angle deviation And a control process for controlling the traveling of the autonomous vehicle so as to follow the reference vehicle.

[Section 7]
The method for controlling an autonomous traveling vehicle according to the embodiment includes a non-linearization process with respect to a turning angle deviation that is a difference between a turning angle of the autonomous traveling vehicle and a turning angle of a virtual reference vehicle. Controlling the traveling of the autonomous vehicle so as to follow the reference vehicle based on the turning angle deviation. The non-linearization process is a process of reducing the absolute value of the turning angle deviation when the absolute value of the turning angle deviation is smaller than a predetermined value.

[2. Details of Embodiment]

[2.1 Vehicle]

  FIG. 1 shows a vehicle 100. The vehicle 100 travels autonomously by the controller 10. The vehicle 100 is a traveling device, for example, a robot tractor. The robot tractor is an autonomously traveling tractor. The vehicle 100 is not limited to a robot tractor, and may be another type of vehicle.

  The vehicle 100 includes a controller 10. The controller 10 includes a computer having a processor 11 and a memory 12. The memory 12 stores a computer program executed by the processor 11. The computer functions as the controller 10 by the processor 11 executing the computer program.

  The vehicle 100 includes sensors 21 and 22 that measure the state of the vehicle. The sensor 21 measures the turning angle θ of the vehicle 100. The turning angle θ is detected as an azimuth angle. The sensor 21 is, for example, a magnetic sensor that measures a magnetic direction. The measured turning angle θ is given to the controller 10. The sensor 22 measures the position of the vehicle 100. The position of the vehicle 100 is detected as position coordinates (x, y). The sensor 22 is, for example, a global satellite positioning system (GNSS) receiver. The measured position (x, y) is given to the controller 10.

  The controller 10 outputs the steering angle φ of the vehicle 100 as a control input to the vehicle 100 that is a control target. However, in the following description, the controller 10 handles the speed v and the turning angular speed ω as control inputs. Since the vehicle 100 of the present embodiment turns when the steering angle φ changes, the controller 10 converts the speed v and the angular speed ω into the steering angle φ and outputs the result. This conversion will be described later.

  The controller 10 controls the actuator 30 that adjusts the steering angle of the vehicle according to the steering angle φ. Actuator 30 moves the steered wheel in accordance with a command from controller 10 to change the turning angle of vehicle 100. The controller 10 may output the speed v of the vehicle 100 as a control input to the vehicle 100.

[2.2 Path following control]
The controller 10 causes the vehicle 100 to autonomously travel by path following control. As shown in FIG. 2, a virtual reference vehicle 200 is used in the route following control. In the route following control, the vehicle 100 that is a real vehicle follows the reference track drawn by the reference vehicle 200 when a reference input is given to the reference vehicle 200. As shown in FIG. 2, in the route following control, the vehicle 100 is positioned directly beside the reference vehicle 200 and follows the reference vehicle 200. The reference trajectory is a desirable trajectory when the vehicle 100 travels autonomously.

When the state of the vehicle 100 is q = [x, y, θ] ^T and the state of the reference vehicle is q _r = [x _r , y _r , θ _r ] ^T , these kinematic models are expressed as follows: Is done.

Here, x and y indicate the position of the vehicle 100 in the xy coordinate system, and θ is the turning angle of the vehicle 100. x _r and y _r indicate the position of the reference vehicle 200 in the xy coordinate system, and θ _r is the turning angle of the reference vehicle 200. v and ω are the speed and angular velocity of the vehicle 100, and v _r and ω _r are the speed and angular velocity of the reference vehicle 200.

The relative error between the vehicle 100 and the reference vehicle 200 is defined as follows.

The error e ₁ is a difference in the position in the front-rear direction between the vehicle 100 and the reference vehicle 200. Hereinafter, the error e _{1 is referred} to as a front-back deviation. The error e ₂ is a difference in the left-right direction position between the vehicle 100 and the reference vehicle 200. Hereinafter, the error _{e 2,} that the lateral deviation. The error e ₃ is the difference between the turning angle θ of the vehicle 100 and the turning angle θ _r of the reference vehicle 200. In the following, the error e _3, that the turning angle deviation.

Under certain initial conditions, the deviations e ₁ , e ₂ and e ₃ converge to 0 when the speed v and the turning angular speed ω as control inputs are selected as follows.

_{However, K 1, K 2, K} 3 is a configuration parameter is a positive constant.

The following equation (6) is obtained by differentiating the relative error expressed by the equation (3) with respect to time.

When performing the follow-up control, there is no need to consider the difference in position between the vehicle 100 and the reference vehicle 200 in the front-rear direction, so an arbitrary speed is given to the traveling direction component v of the control input, and e ₁ = 0, Assuming that the time derivative of e ₁ = 0, the speed v _r of the reference vehicle 200 is determined from the equation (6) as follows.

Here, if the curvature of turn is ρ, the speed v _r and the angular speed ω _r of the reference vehicle 200 are as follows according to the speed v of the vehicle 100.

Therefore, the only input for converging e ₂ and e ₃ to 0 is ω determined by the equation (5). At this time, the equation (6) is rewritten as follows.

[2.3 Control law by controller]

[2.3.1 Inverse optimal control]
When the vehicle 100 travels on rough terrain such as farmland instead of a paved road, it receives disturbance due to unevenness and slippage of the ground. In the case where the vehicle 100 is a tractor, it is also subjected to disturbance due to vibrations and resistance caused by a working machine pulled by the tractor. Due to aging of the vehicle 100 and the like, modeling errors due to model changes and uncertainties also increase. In the present embodiment, the inverse optimal control method is adopted as a robust control law corresponding to disturbances and modeling errors. The controller 10 controls the travel of the vehicle 100 by reverse optimal control.

The optimal control problem is the system
In contrast, the control input u (x) satisfying the following condition is designed.

1. u (x) asymptotically converges the system to the target state.
2. u (x) minimizes the following evaluation function J:

However, to solve this optimal control problem, it is necessary to solve the Hamilton-Jacobi-Bellman equation. Therefore, in this embodiment, the following deoptimization approach that is easier is taken.
1. First, design the feedback law.
2. Thereafter, it is shown that the evaluation function is optimal for the evaluation function as shown in Equation (13).

[2.3.2 Control by Sontag format]
Consider the affine system given by

  Here, f is an n-dimensional vector and g is an n × m matrix. At this time, a function satisfying the following conditions is defined as a Control Lyapunov Function (CLF).

(Definition: Control Lyapunov Function) For all x ≠ 0, the smooth, positive definite and radially unbounded function V (x) is the Control Lyapunov Function for the system of Equation (14). (CLF).
At this time, it is clear that the following theorem holds.

(Theorem: Inverse optimal control from CLF)
When a (x), b (x), and p (x) are defined by the following equations,
Next input in Sontag format
Asymptotically stabilizes the system and minimizes the next evaluation function.

  Due to this optimality, the sought controller has a stability advantage called sectormargin (1/2, ∞).

[2.3.3 Inverse optimal path following control]
The controller 10 of the present embodiment performs path following control by inverse optimal control. Below, the inverse optimal controller 10 for path | route following control is demonstrated.

First, the following function is defined. However, A and B are setting parameters and are positive values.
Here, h (z) is a function that satisfies the conditions of zh (z)> 0, ∀z ≠ 0, and h (0) = 0. h (z) may be a function that satisfies such a condition, and is not limited to the form of Expression (21).

By the following equation (23), the controller 10 performs a conversion process for converting the turning angle deviation _{e 3.} In embodiments, the conversion process, the turning angle deviation e _3, by adding the value of the function h shown in equation (21), a process of obtaining the converted turning angle deviation e ^~ _3. Here, in Equation (21), z = e ₂ v, and h is a function of the lateral deviation e ₂ .

In reverse optimum control from the CLF, it is necessary to satisfy the equation (15), turning angle deviation e ₃ is alone, does not satisfy the relationship of equation (15). However, when a function h (z) that satisfies zh (z)> 0 is added to the turning angle deviation e ₃ , Expression (15) can be satisfied. As shown in Expression (23), the converted turning angle deviation e ^~ ₃ is expressed as a function of the lateral deviation e ₂ and the turning angle deviation e ₃ .

According to the equation (21), when the absolute value of e ₂ v is large, the degree to which e ₂ v is reflected in the turning angle deviations e ₃ ^to ₃ shown in the equation (23) is small. On the other hand, when the absolute value of e 2 _v is small, e 2 _v is the value corresponding to the magnitude is reflected in e ^~ _3.

From the equations (10) and (23), the following equation is obtained.

Based on Expression (24), V _i shown below is a Control Lyapunov Function (CLF).
Here, K _i2 and K _i3 are positive constants. K _i2 is the gain with respect to _{e 2, K i3} is the gain for e ^~ _3.

If, in equation (25), when e ^~ ₃ is _{e 3 are,} e 2, the gain _K for _{e 3 i2, K i3} to be to set _separately, gain individually for e 2, _{e 3} It can be adjusted and there is no problem. However, since _{V i} is Control Lyapunov Function (CLF), in formula (25), it is necessary to have e ^~ ₃ is employed. e ^~ ₃ is a function of _{e 2} and _{e 3,} only the gain _{K i2, K i3} set _separately, not always easy to adjust the gain for e 2, _{e 3} individually.

For example, the path following control, when the turning angle deviation e ₃ lateral deviation e ₂ is greater to occur, when you try to reduce the turning angle deviation e _3, can not be reduced lateral deviation e _2. On the other hand, if the lateral deviation e _{2 is} to be reduced, the turning angle deviation e ₃ is increased. In the path following control, there is a trade-off between the lateral deviation e ₂ and the turning angle deviation e ₃ . Accordingly, the e ^~ ₃ which is a function of _{e 2} and _{e 3,} it is not always easy to properly adjust a single gain _{K i3.}

On the other hand, in the present embodiment, by adjusting the setting parameters A and B included in the function h of Expression (21), how much the lateral deviation e ₂ is reflected in the converted turning angle deviations e ₃ ^to ₃ . It becomes easy to adjust the controller 10 appropriately. Here, in equation (21), setting parameter A determines the magnitude of the value of the function h, setting parameter B is, e ₂ of a value corresponding to the magnitude of e _{2 v} in e ^~ ₃ are reflected Determine the range of v.

Now, when _{V i} represented by the formula (25) is a Control Lyapunov Function (CLF),
It is.

By applying the equations (26) and (27) to the controller 10 that outputs the control input of the equation (19), the angular velocity ω that converges the lateral deviation e ₂ and the turning angle deviation e ₃ to 0 is determined. However, in order for the velocity v _{r to} be bounded, e ₃ ≠ π / 2 and e ₂ ω must be bounded.

[2.3.4 Input conversion]
The control input of the equation (19) is the speed v and the turning angular speed ω of the vehicle 100, but the actual vehicle 100 turns by changing the steering angle φ of the front wheels that are the steering wheels. The relationship between the steering angle φ, the speed v, and the turning accuracy ω is as follows. Here, L is the distance between the front and rear wheels of the vehicle 100.

  The controller 10 converts the determined angular velocity ω into the steering angle φ by the equation (28), and controls the actuator 30 according to the steering angle φ.

[Nonlinear of 2.3.5 once turning angle deviation _{e 3]}
Turning angle deviation e ₃ is influenced by the measurement error of the azimuth angle to be measured (turning angle) theta, it may be larger than the actual turning angle deviation. If the control is performed based on the turning angle deviation e ₃ which is larger than the actual one, it is difficult to converge the lateral deviation e ₂ . In this case, by reducing the gain for the turning angle deviation e ₃ when obtaining the control input, that amount can be reduced the influence of the measurement error of the azimuth angle theta. However, simply, reducing the gain for turning angle deviation e _3, when the turning angle deviation e ₃ really big, for converging the lateral deviation e ₂ is also difficult.

In the present embodiment, in order to reduce the influence of the measurement error, the controller 10 performs a non-linear process for the turning angle deviation e ₃ . The function i (e ₃ ) for the non-linearization process of the turning angle deviation e ₃ is defined as follows, for example. Hereinafter, the value of the function i (e ₃ ) is referred to as non-linearized turning angle deviation.
Here, a represents the inclination of the function i (e ₃ ) at the origin, and ε represents the range in which non-linearization is performed, and is determined so as to satisfy the following condition, for example.

The non-linearization process using the function i (e ₃ ) is a process for reducing the absolute value of the turning angle deviation when the absolute value of the turning angle deviation e ₃ is smaller than the predetermined value ε. The solid line in FIG. 3 shows an example of the function i (e ₃ ). Here, a = 0.1 and ε = 0.020 were determined. In addition, the dotted line in FIG.

In the non-linearization process, the function i is a non-linear function in the range where the absolute value of the turning angle deviation e ₃ is smaller than the predetermined value ε, that is, in the range of −ε ≦ e ₃ ≦ ε, as shown in FIG. In this way, nonlinearization is performed in which the absolute value of the nonlinear turning angle deviation i (e ₃ ) is smaller than the absolute value of the turning angle deviation e ₃ . The range has a small absolute value of the turning angle deviation e _3, the influence of the measurement error is relatively large, the non-linear process, not emphasizing turning angle deviation e ₃ by reducing the absolute value of the turning angle deviation e ₃ In this way, the influence of measurement errors is reduced. This makes it easy to converge the horizontal deviation e _2.

On the other hand, in the range where the absolute value of the turning angle deviation e ₃ is larger than the predetermined value ε, that is, in the range of −ε> e ₃ , e ₃ <ε, the function i is a linear function, and i (e ₃ ) = e ₃ That is, when the absolute value of the turning angle deviation e ₃ is larger than the predetermined value ε, the non-linearized turning angle deviation i (e ₃ ) maintains the value of the turning angle deviation e ₃ before the non-linearization process. Times the absolute value is larger range of turning angle deviation e _3, since the influence of the measurement error is relatively small, by using the determination of the control input without reducing the absolute value of the turning angle deviation e _3, turning angle deviation When e ₃ is really large, it is easy to converge the lateral deviation e ₂ .

The inclination a is appropriately set according to the magnitude of the measurement error. When the measurement error is small, the degree to which the function i (e ₃ ) in the range of −ε ≦ e ₃ ≦ ε reduces the absolute value of the turning angle deviation e ₃ by increasing the slope a in the above range. Becomes smaller. On the other hand, when the measurement error is large, the function i (e ₃ ) in the range of −ε ≦ e ₃ ≦ ε can reduce the absolute value of the turning angle deviation e ₃ by reducing the slope a in the above range. The degree of reduction increases.

  The controller 10 may use a preset value as the inclination a, or may dynamically change the inclination a according to the magnitude of the measurement error. For example, the magnitude of the measurement error can be estimated by the controller 10 using a Kalman filter.

  The non-linearization range ε is also appropriately set according to the magnitude of the measurement error. When the measurement error is small, the non-linearization range ε can be reduced within the above range to narrow the range of non-linearization processing. Conversely, when the magnitude of the measurement error is large, the non-linearization range ε can be increased within the above range to widen the range of the non-linearization processing.

  The controller 10 may use a preset value as the non-linearization range ε, or may dynamically change the non-linearization range ε according to the magnitude of the measurement error. As described above, the magnitude of the measurement error can be estimated by the controller 10 using the Kalman filter, for example.

In the embodiment, i (e ₃ ) in the range of −ε ≦ e ₃ ≦ ε is a smooth continuous function for i (e ₃ ) = e ₃ in the range of −ε> e ₃ and e ₃ <ε. Is set as A function i _{(e 3)} in the range of -ε ≦ _{e 3} ≦ ε, and _{-ε> e 3,} e 3 <function in the range of ε i _{(e 3),} it is what smoothly continuous, Control is stabilized.

Using equation (29), equation (23) can be rewritten as follows:

Thereby, the following equation is obtained in the same manner as equation (24).

Based on Expression (33), V _i shown in Expression (25) is a Control Lyapunov Function (CLF). At this time,
It is.

By applying the equations (34) and (35) to the controller 10 that outputs the control input of the equation (19), the angular velocity ω that converges the lateral deviation e ₂ and the turning angle deviation e ₃ to 0 is determined. However, in order for the velocity v _{r to} be bounded, e ₃ ≠ π / 2 and e ₂ ω must be bounded.

[2.4 Processing procedure by controller]
FIG. 4 shows a processing procedure by the controller 10 that performs the path following control by inverse optimal control. In step S1, the controller 10 calculates the lateral deviation _{e 2} and turning angle deviation _{e 3.} The lateral deviation e ₂ is calculated from the position (x, y) measured by the sensor 22, the position (x _r , y _r ) of the reference vehicle 200, and the turning angle θ _r of the reference vehicle 200 (formula (See (3)). Turning angle deviation e ₃ includes a turning angle θ measured by the sensor 21, and the turning angle θr of the reference vehicle 200 from, also calculates turning angle deviation e ₃ (see equation (3)).

In step S2, the controller 10, to the turning angle deviation _{e 3,} it performs non-linear processing, obtaining the non-linearized turning angle deviation i _{(e 3).} The function i for nonlinear processing is stored as data 121 for nonlinear processing in the memory 12 of the controller 10. The processor 11 reads the data 121 for non-linearization processing and performs non-linearization processing. Since the non-linear processing is performed on the turning angle deviation e _3, as described above, can contain measurement errors have been turning angle θ measured by the sensor 21, the effect is reduced.

In step S3, the controller 10 performs a conversion process according to the equation (32) on the non-linearized turning angle deviation i (e ₃ ). In the conversion process, the nonlinearized turning angle deviation e (e ₃ ) and the value of the function h (e ₂ v) of the lateral deviation e ₂ are added to obtain the converted nonlinearized turning angle deviation e ^~ _3. It is done. Setting parameters A and B for conversion processing (see equation (21)) are set as a parameter value 122 and a parameter value 123 in the memory 12 of the controller 10. The processor 11 reads out the parameter values 122 and 123 as setting parameters A and B, and performs conversion processing. Since there are setting parameters A and B, the converted non-linearized turning angle deviation e ^~ ₃ is appropriate. The setting parameters A and B may be adjusted by user input, or the controller 10 itself may adjust according to the traveling environment of the vehicle 100 and the like.

In step S4, the controller 10, based on the lateral deviation e ₂ and transformed non-linearized turning angle deviation e ^~ _3, to determine the angular velocity for converging e ₂ and e ^~ ₃ omega by reverse optimal control. The controller 10 obtains the steering angle φ from the angular velocity ω based on the equation (28), and controls the actuator 30.

[2.5 Simulation results]

[2.5.1 Nonlinearization processing]
Figure 5A, 5B, 6A, 6B, in a case where there is a measurement error of turning angle theta, shows the simulation result of differences in the status of the vehicle 100 due to existence of the non-linear processing for turning angle deviation e _3. FIG. 5A shows the lateral deviation e ₂ and the turning angle deviation e ₃ when the non-linearization process is performed, and FIG. 5B shows the lateral deviation e ₂ and the turning angle deviation e ₃ when the non-linearization process is not performed. 6A shows the steering angle φ when the non-linearization process is performed, and FIG. 6B shows the steering angle φ when the non-linearization process is not performed.

As shown in FIG. 5B, the lateral deviation e ₂ when the non-linearization process is not performed is about 0.1 m, whereas the lateral deviation when the non-linearization process is performed as shown in FIG. 5A. e ₂ is reduced to about 0.04～0.05M. It was confirmed by simulation that the lateral deviation e ₂ can be suppressed by performing the non-linearization processing even if there is a measurement error of the turning angle θ.

[2.5.2 Setting parameters A and B]
7A, 7B, 8A, 8B, 9A, and 9B show the lateral deviation e ₂ , the turning angle deviation e ₃ , and the steering angle φ when the parameters A and B in Expression (21) are adjusted. 7A and 7B show the case where A = 1 and B = 1, and in this case, the lateral deviation e ₂ is about 0.04 m. 8A and 8B show the case where A = 1 and B = 30, and in this case, the lateral deviation e ₂ is about 0.025 m. FIGS. 9A and 9B show the case where A = 1 and B = 15. In this case, the lateral deviation e ₂ is about 0.023 m. Thus, the lateral deviation e ₂ can be easily reduced by appropriately adjusting the parameters A and B, particularly the parameter B.

  In addition, this invention is not limited to the said embodiment, A various deformation | transformation is possible.

10 controller 11 processor 12 memory 21 sensor 22 sensor 30 actuator 100 vehicle 200 reference vehicle

Claims (7)

An autonomous vehicle,
A controller,
A sensor for measuring the turning angle of the autonomous vehicle;
With
The controller is
Non-linearization processing for the turning angle deviation which is the difference between the turning angle of the autonomous vehicle and the turning angle of the virtual reference vehicle;
Control processing for controlling the traveling of the autonomous traveling vehicle so as to follow the reference vehicle based on the turning angle deviation subjected to the non-linearization processing;
Is configured to run
The non-linearization process is a process of reducing the absolute value of the turning angle deviation when the absolute value of the turning angle deviation is smaller than a predetermined value. The autonomous traveling vehicle according to claim 1, wherein the control process is a process of controlling traveling by reverse optimal control. The controller is configured to further execute a conversion process of the turning angle deviation subjected to the non-linearization process,
The conversion process is performed by adding the turning angle deviation subjected to the non-linearization process and a value of a function of a lateral deviation that is a difference in a horizontal direction between the position of the autonomous vehicle and the position of the reference vehicle. To determine the turning angle deviation
The autonomous traveling vehicle according to claim 2, wherein the control process is executed based on the converted turning angle deviation. The function is h (z) = Az / (1 + Bz ² ),
h (z) is a function that satisfies zh (z)> 0, ∀z ≠ 0, and h (0) = 0.
A and B are setting parameters that take positive values.
z = e ₂ v,
e ₂ is the lateral deviation,
The autonomous traveling vehicle according to claim 3, wherein v is a speed of the autonomous traveling vehicle. A non-linearization process for a turning angle deviation that is a difference between a turning angle of the autonomous vehicle and a turning angle of a virtual reference vehicle;
Control processing for controlling the traveling of the autonomous traveling vehicle so as to follow the reference vehicle based on the turning angle deviation subjected to the non-linearization processing;
Is configured to run
The non-linearization process is a process of reducing the absolute value of the turning angle deviation when the absolute value of the turning angle deviation is smaller than a predetermined value. On the computer,
Non-linearization processing for the turning angle deviation which is the difference between the turning angle of the autonomous vehicle and the turning angle of the virtual reference vehicle,
Control processing for controlling the traveling of the autonomous traveling vehicle so as to follow the reference vehicle based on the turning angle deviation subjected to the non-linearization processing;
A computer program that executes A method for controlling an autonomous vehicle,
A non-linearization process for a turning angle deviation which is a difference between a turning angle of the autonomous vehicle and a turning angle of a virtual reference vehicle;
Controlling the traveling of the autonomously traveling vehicle so as to follow the reference vehicle based on the turning angle deviation subjected to the non-linearization process,
The non-linearization process is a process of reducing the absolute value of the turning angle deviation when the absolute value of the turning angle deviation is smaller than a predetermined value.