STEERING CONTROLLER AND STEERING CONTROL METHOD

A steering controller includes: a generation part that generates a vehicle model indicating a relationship between a velocity, a steering angle, a lateral deviation, an azimuth deviation of a vehicle, and a curvature; a calculation part that calculates, as an optimal steering angle, a steering angle that minimizes an output value of an evaluation function including an estimated lateral deviation and an estimated azimuth deviation calculated on the basis of a vehicle model, the steering angle, a change amount of the steering angle, a first weighting coefficient, a second weighting coefficient, a third weighting coefficient, and a fourth weighting coefficient; a second acquisition part that acquires a required acceleration; and an updating part that updates at least one weighting coefficient among the first weighting coefficient, the second weighting coefficient, the third weighting coefficient, and the fourth weighting coefficient according to the required acceleration.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to Japanese Patent Applications number 2022-143755, filed on Sep. 9, 2022 contents of which are incorporated herein by reference in their entirety.

BACKGROUND OF THE INVENTION

The present disclosure relates to a steering controller and a steering control method. An automatic steering system in which a target steering angle for causing a vehicle to follow a target trajectory is obtained, and the vehicle is driven in accordance with the obtained target steering angle has been known (for example, refer to Japanese Unexamined Patent Application Publication No. 2021-146920).

In conventional automatic steering systems, a linear vehicle prediction model was used to obtain a steering angle to follow a target trajectory. However, when a vehicle velocity changes due to acceleration or deceleration of a vehicle, the vehicle may behave non-linearly, thereby causing a model error between actual behavior of the vehicle and the vehicle model. This results in degradation of control accuracy.

BRIEF SUMMARY OF THE INVENTION

The present disclosure focuses on this point, and its object is to prevent degradation of following accuracy at the time of acceleration or deceleration of a vehicle.

A first aspect of the present disclosure provides a steering controller which causes a vehicle to follow a target trajectory; the steering controller including a first acquisition part that acquires a velocity of the vehicle, a steering angle of the vehicle, a lateral deviation with respect to the target trajectory of the vehicle, an azimuth deviation that is a difference between a direction of the vehicle and a target direction of the vehicle, and a curvature of a road surface on which the vehicle travels, at predetermined intervals; a generation part that generates a vehicle model indicating a relationship between the velocity, the steering angle, the lateral deviation, the azimuth deviation, and the curvature; a calculation part that calculates, as an optimal steering angle, the steering angle that minimizes an output value of an evaluation function including an estimated lateral deviation and an estimated azimuth deviation calculated on the basis of the vehicle model, the steering angle, a change amount of the steering angle, a first weighting coefficient of a term corresponding to the estimated lateral deviation, a second weighting coefficient of a term corresponding to the estimated azimuth deviation, a third weighting coefficient of a term corresponding to the steering angle, and a fourth weighting coefficient of a term corresponding to the amount of change; a second acquisition part that acquires a required acceleration when accelerating or decelerating the vehicle; and an updating part that updates at least one weighting coefficient among the first weighting coefficient, the second weighting coefficient, the third weighting coefficient, and the fourth weighting coefficient according to the required acceleration.

A second aspect of the present disclosure provides a steering control method, executed by a computer, for causing a vehicle to follow a target trajectory, the steering control method comprising the steps of: acquiring a velocity of the vehicle, a steering angle of the vehicle, a lateral deviation of the vehicle with respect to the target trajectory, an azimuth deviation that is a difference between a direction of the vehicle and a target direction of the vehicle, and a curvature of a road surface on which the vehicle travels, at predetermined intervals; generating a vehicle model indicating a relationship between the velocity, the steering angle, the lateral deviation, the azimuth deviation, and the curvature; calculating, as an optimal steering angle, the steering angle that minimizes an output value of an evaluation function including an estimated lateral deviation and an estimated azimuth deviation calculated on the basis of the vehicle model, the steering angle, a change amount of the steering angle, a first weighting coefficient of a term corresponding to the estimated lateral deviation, a second weighting coefficient of a term corresponding to the estimated azimuth deviation, a third weighting coefficient of a term corresponding to the steering angle, and a fourth weighting coefficient of a term corresponding to the change amount; acquiring a required acceleration when accelerating or decelerating the vehicle; and updating at least one weighting coefficient among the first weighting coefficient, the second weighting coefficient, the third weighting coefficient, and the fourth weighting coefficient according to the required acceleration.

DETAILED DESCRIPTION OF THE INVENTION

Hereinafter, the present disclosure will be described through exemplary embodiments, but the following exemplary embodiments do not limit the invention according to the claims, and not all of the combinations of features described in the exemplary embodiments are necessarily essential to the solution means of the invention.

<Outline of a Driving Control System>

FIG.1shows a configuration of a driving control system S. The driving control system S is a system for causing a vehicle to travel along a target trajectory by controlling a steering angle of the vehicle. The driving control system S is mounted on the vehicle. The target trajectory is a predetermined trajectory, and includes a plurality of travel positions that are targets for the vehicle and directions, corresponding to the plurality of travel positions, that are targets for the vehicle. The driving control system S includes a state identifying apparatus1, a travel control apparatus2, and a steering controller10.

The state identifying apparatus1identifies a parameter indicating the state of the vehicle, at a regular control period. The parameter indicating the state of the vehicle includes, for example, a velocity, a steering angle, a lateral deviation, an azimuth deviation, and a curvature of a road surface. The lateral deviation is a difference between a traveling position of the vehicle and a target traveling position of the vehicle, in a direction orthogonal to the traveling direction of the vehicle. The azimuth deviation is a difference between the direction of the vehicle at the position at which the vehicle is travelling and the target direction of the vehicle corresponding to this position.

The state identifying apparatus1acquires a velocity of the vehicle measured by a velocity sensor, for example. Further, the state identifying apparatus1acquires a steering angle of the vehicle measured by a steering angle sensor, for example. The steering angle acquired by the state identifying apparatus1is a rotation angle of a steering wheel shaft, or a difference between the direction of the vehicle and the direction of a tire of the vehicle.

The state identifying apparatus1acquires the position and the direction of the vehicle by obtaining a GPS (Global Positioning System) signal, for example. The state identifying apparatus1identifies the lateral deviation of the vehicle on the basis of the acquired vehicle position and the target travel position for the vehicle corresponding to the position of the vehicle. The state identifying apparatus1identifies the azimuth deviation of the vehicle on the basis of the acquired direction of the vehicle and the target direction of the vehicle corresponding to the position of the vehicle.

The state identifying apparatus1identifies the curvature of the road surface corresponding to the acquired position of the vehicle, on the basis of map information stored in a storage of the state identifying apparatus1, for example. The state identifying apparatus1outputs the velocity, the steering angle, the lateral deviation, the azimuth deviation, and the curvature of the road surface to the steering controller10at a regular control period.

The travel control apparatus2controls the velocity and the direction of the vehicle. The travel control apparatus2controls the direction of the vehicle in accordance with a steering angle at the time of the next control period. The steering angle is output by the steering controller10at the regular control period.

The steering controller10controls the steering angle in order to cause the vehicle to follow the target trajectory. The steering controller10generates a vehicle model corresponding to the state of the vehicle input from the state identifying apparatus1. The steering controller10uses the generated vehicle model to calculate the steering angle at the regular control period, in order to cause the vehicle to travel in a target direction. The regular control period is a sampling period in the model prediction control. The steering controller10inputs the calculated steering angle to the travel control apparatus2, thereby causing the vehicle to travel in the target direction. Hereinafter, the configuration and operation of the steering controller10will be described in detail.

<Configuration of the Steering Controller>

As shown inFIG.1, the steering controller10includes a storage20and a control part30.

The storage20includes a read only memory (ROM) storing a basic input output system (BIOS) of a computer or the like, and a random access memory (RAM) serving as a work area. The storage20is a large-capacity storage device such as a hard disk drive (HDD), a solid state drive (SSD), and the like that stores an operating system (OS), an application program, and various types of information to be referred to at the time of executing the application program.

The control part30is a processor such as a central processing unit (CPU) or a graphics processing unit (GPU). The control part30functions as a first acquisition part32, a generation part33, a calculation part34, a travel control part35, a second acquisition part36, and an updating part37by executing the program stored in the storage20.

The first acquisition part32acquires a state quantity of the vehicle output from the state identifying apparatus1at predetermined intervals (control period). Specifically, the first acquisition part32acquires a velocity of the vehicle, a steering angle of the vehicle, a lateral deviation with respect to a target trajectory of the vehicle, an azimuth deviation that is a difference between a direction of the vehicle and a target direction of the vehicle, and a curvature of a road surface on which the vehicle travels, at predetermined intervals. The first acquisition part32stores the acquired state quantity of the vehicle in the storage20.

The generation part33generates a vehicle model indicating a relationship between the velocity, the steering angle, the lateral deviation, the azimuth deviation, and the curvature. For example, the generation part33generates a vehicle model corresponding to a reference point shown inFIG.2. The generation part33stores the generated vehicle model in the storage20.

FIG.2is a schematic diagram showing a vehicle model. The vehicle motion corresponding to the reference point shown inFIG.2can be expressed by the following Equation 1 using a vehicle velocity v, a lateral deviation ez, an azimuth deviation eθ, and a curvature kr.

Assuming that the azimuth deviation during following of the target trajectory is extremely small, an equation of the vehicle motion can be linearized as shown in Equation 2.

Here, a steering angle of a vehicle traveling on any curvature trajectory can be derived from the mechanical relationship of the vehicle, as shown in Equation 3, where L denotes the wheel base of the vehicle.

When the steering angle of Equation 3 is input to the vehicle, the assumption of Equation 4 is established when the vehicle travels along the target trajectory.

Then, Equation 5 can be derived by Tyler expansion of tan δ in the vicinity of δr.

By substituting Equation 5 into Equation 2, the equation of the vehicle motion can be linearized as in Equation 6.

Assuming that the sample time is sufficiently small and Equation 6 is discretized by the forward Euler method, Equation 7 can be derived. By designing the model prediction control on the basis of Equation 7, it is possible to realize a steering control system capable of handling operations from starting to stopping.

The calculation part34calculates, as an optimal steering angle, a steering angle that minimizes the output value of an evaluation function corresponding to the vehicle model generated by the generation part33. Here, the evaluation function includes an estimated lateral deviation and an estimated azimuth deviation which are calculated on the basis of the vehicle model, a steering angle, a change amount of the steering angle, a first weighting coefficient of a term corresponding to the estimated lateral deviation, a second weighting coefficient of a term corresponding to the estimated azimuth deviation, a third weighting coefficient of a term corresponding to the steering angle, and a fourth weighting coefficient of a term corresponding to the change amount.

Specifically, the calculation part34first inputs the calculated estimated lateral deviation and estimated azimuth deviation, the steering angle, and the change amount of the steering angle to the evaluation function corresponding to the vehicle model based on the vehicle velocity acquired by the first acquisition part32. The calculation part34then calculates the steering angle that minimizes the output value of the evaluation function as the optimal steering angle.

Here, when the state variable x of the state space equation is represented by the following Equation 8, an observed output y is represented by Equation 9, where ezis the estimated lateral deviation and eo is the estimated azimuth deviation.

The calculation part34estimates the state variable x using a steady state Kalman filter, and calculates the optimization problem of the model prediction control using the evaluation function shown in the following Equation 10. In Equation 10, p denotes a predicted horizon, δ denotes a steering angle input, Δδ denotes a difference between the steering angle input and the steering angle input of the immediately preceding control period, and “max” and “min”, which are suffixes of the input and output variables, denote upper and lower limit values of the signal. Q1is a first weighting coefficient of a term corresponding to the estimated lateral deviation ez, Q2is a second weighting coefficient of a term corresponding to the estimated azimuth deviation eθ, R1is a third weighting coefficient of a term corresponding to the steering angle δ, and R2is a fourth weighting coefficient of a term corresponding to the change amount Δδ of the steering angle.

subject to δmin≤δ[k+kt]≤δmax

The calculation part34performs an optimization calculation for minimizing the output value J of the evaluation function shown in Equation 10 to calculate the steering angle in real time, thereby achieving following of the target trajectory of the vehicle. By calculating the steering angle with the calculation part34in this manner, the steering controller10can cause the vehicle to travel at a position where the error with respect to the target trajectory is small at the timing of each of a plurality of control periods.

In a case where priority is given to converging the lateral deviation and the azimuth deviation to 0, the calculation part34sets the weighting coefficients Q1and Q2to be larger than a predetermined value, or sets the weighting coefficients R1and R2to be smaller than a predetermined value. The predetermined value is a value set in advance by experiment or the like, for example. When priority is given to reducing the change amount of the steering angle, the calculation part34sets R2to be smaller.

The travel control part35causes the vehicle to travel on the basis of the steering angle calculated by the calculation part34. For example, the travel control part35outputs the steering angle calculated by the calculation part34to the travel control apparatus2at a regular control period, thereby causing the vehicle to travel at the calculated steering angle.

Since the vehicle velocity v is included in the coefficient matrix, the state equation of Equation 7 is a linear parameter dependent on the vehicle velocity. Under the condition that the vehicle velocity is constant, the state equation of Equation 7 is equivalent to a linear time-invariant system. On the other hand, in a case where traveling, such as in autonomous driving, in which a vehicle velocity changes sharply, is assumed, there has been a concern that control accuracy and stability may be degraded with the model prediction control based on Equation 7. In contrast, in order to prevent degradation of path following control at the time of acceleration or deceleration of a vehicle, the steering controller10of the present embodiment updates the weighting coefficients of the evaluation function in real time according to a required acceleration at the time of the acceleration or deceleration of the vehicle, as will be described below. The steering controller10includes a second acquisition part36and an updating part37in order to update the weighting coefficients.

The second acquisition part36acquires a required acceleration when accelerating or decelerating a vehicle. The required acceleration is an acceleration corresponding to operations of the accelerator and brakes for a driver to accelerate or decelerate the vehicle, for example. The second acquisition part36can identify the required acceleration from the driver's operations of the accelerator and brakes detected by the state identifying apparatus1.

According to the required acceleration acquired by the second acquisition part36, the updating part37updates the weighting coefficients of the evaluation function calculated by the calculation part34as the optimal steering angle. In the present embodiment, the updating part37updates at least one weighing coefficient among the first weighting coefficient Q1, the second weighting coefficient Q2, the third weighting coefficient R1, and the fourth weighting coefficient R2according to the required acceleration.

The updating part37updates the weighting coefficient in real time according to the required acceleration while the vehicle is traveling (specifically, during automatic steering). By applying the weighting coefficient updated by the updating part37to the evaluation function, the calculation part34calculates the optimal steering angle in which the updated weighting coefficient has been reflected. The travel control part35causes the vehicle to travel on the basis of the optimal steering angle in which the updated weighting coefficient has been reflected. The weighting coefficient is immediately updated at the time of the acceleration or deceleration of the vehicle, and therefore the vehicle is steered on the basis of the optimal steering angle to which the updated weighting coefficient has been reflected. As a result, it is possible to prevent degradation of the path following control at the time of the acceleration or deceleration of the vehicle.

The updating part37may update the second weighting coefficient Q2and the fourth weighting coefficient R2according to the required acceleration. Specifically, the updating part37updates the second weighting coefficient Q2and the fourth weighting coefficient R2while values of the first weighting coefficient Q1and the third weighting coefficient R1are fixed. Advantages of updating only the second weighting coefficient Q2and the fourth weighting coefficient R2are as follows. As a form of a trajectory following error, a deviation (azimuth deviation) occurs between the vehicle and the target trajectory in the traveling direction, and this results in an occurrence of a lateral shift (lateral deviation) from the target trajectory if the vehicle travels without correcting the deviation in the traveling direction. From this, it can be said that if the azimuth deviation can be reduced, the lateral deviation can also be reduced. Therefore, the updating part37prioritizes updating of the second weighting coefficient Q2among the first weighting coefficient Q1and the second weighting coefficient Q2. On the other hand, when the second weighting coefficient Q2is updated, the steering angle may change sharply. Therefore, the updating part37prevents a sharp change in the steering angle by updating the fourth weighting coefficient R2.

The updating part37may reference a lookup table stored in the storage20when updating the first weighting coefficient Q1, the second weighting coefficient Q2, the third weighting coefficient R1, and the fourth weighting coefficient R2. The storage20stores the lookup table indicating correspondence information in which (i) the magnitude of the required acceleration and (ii) update ranges of the first weighting coefficient Q1, the second weighting coefficient Q2, the third weighting coefficient R1, and the fourth weighting coefficient R2are associated with each other. The updating part37updates the weighting coefficients to the magnitude of the weighting coefficients corresponding to the required acceleration acquired by the second acquisition part36by referencing the update ranges included in the correspondence information. By referencing the lookup table in this manner, the first weighting coefficient Q1, the second weighting coefficient Q2, the third weighting coefficient R1, and the fourth weighting coefficient R2can be easily updated in real time.

The updating part37may update all of the first weighting coefficient Q1, the second weighting coefficient Q2, the third weighting coefficient R1, and the fourth weighting coefficient R2according to the required acceleration. That is, the updating part37updates all of the four weighting coefficients according to a change in the velocity of the vehicle. By updating all of the weighting coefficients in this manner, it is possible to effectively prevent degradation of the path following control at the time of the acceleration or deceleration of the vehicle.

In the above description, the updating part37updates two weighting coefficients or four weighting coefficients, but the present embodiment is not limited thereto. For example, the updating part37may update one of the four weighting coefficients or three weighting coefficients.

FIGS.3A and3Bshow simulation results. InFIG.3A, the horizontal axis represents time and the vertical axis represents lateral deviation. InFIG.3B, the horizontal axis represents time and the vertical axis represents azimuth deviation. InFIG.3AandFIG.3B, a broken line shows a control response in a case of a comparative example in which a weighting coefficient is not updated, and a solid line shows a control response in a case where the weighting coefficient is updated as in the present embodiment. In the comparative example, control accuracy deteriorates at the starting of a vehicle (when the time is 0) or at the time of the acceleration or deceleration of the vehicle (when the time is about 70 (s)). On the other hand, in the case where the weighting coefficient is updated as in the present embodiment, the accuracy of the lateral deviation and the azimuth deviation is improved even at the starting of a vehicle or at the time of the acceleration or deceleration of the vehicle. That is, by updating the weighting coefficient in accordance with the acceleration and deceleration of the vehicle, a good control response can be realized.

<Operation Example of the Steering Controller>

FIG.4is a flowchart showing an example of a steering angle calculation process performed by the steering controller10. The process shown inFIG.4is performed while the vehicle is traveling.

First, the first acquisition part32acquires, from the state identifying apparatus1, a state quantity of a vehicle such as a velocity of the vehicle, a steering angle of the vehicle, a lateral deviation of the vehicle, an azimuth deviation of the vehicle, and a curvature of a road surface (step S102). Next, the generation part33generates a vehicle model corresponding to the state quantity of the vehicle acquired by the first acquisition part32(step S104).

Next, the calculation part34calculates an estimated lateral deviation and an estimated azimuth deviation by inputting the velocity, the steering angle, the lateral deviation, the azimuth deviation, and the curvature to a state space model corresponding to the vehicle model generated by the generation part33(step S106).

Next, the calculation part34inputs the estimated lateral deviation and the estimated azimuth deviation calculated by the calculation part34to an evaluation function corresponding to the vehicle model generated by the generation part33, and calculates a steering angle that minimizes an output value of the evaluation function (step S108). The steering controller10repeats the processes of steps S102to S108described above until the vehicle stops. By doing this, the steering angle is optimized during traveling of the vehicle.

FIG.5is a flowchart showing an example of a process of updating a weighting coefficient performed by the steering controller10. The processing ofFIG.5, as well, is performed while the vehicle is traveling. First, the second acquisition part36acquires a required acceleration of a traveling vehicle (step S122). For example, the second acquisition part36acquires, from the state identifying apparatus1, a required acceleration at the time of accelerating or decelerating the vehicle.

Next, the updating part37determines whether or not the required acceleration acquired by the second acquisition part36has changed (step S124). When the required acceleration has changed in step S124(Yes), the updating part37updates at least one weighting coefficient among the first weighting coefficient Q1, the second weighting coefficient Q2, the third weighting coefficient R1, and the fourth weighting coefficient R2of the evaluation function corresponding to a vehicle model generated by the generation part33according to the required acceleration (step S126).

Next, the calculation part34applies the updated weighting coefficient to the evaluation function (step S128). In step S108, the calculation part34calculates a steering angle using the evaluation function in which the updated weighting coefficient has been reflected. By doing this, the calculation part34calculates an optimal steering angle while the weighting coefficient is updated. The steering controller10repeats the processes of steps S122to S128described above until the vehicle stops.

EFFECTS OF THE PRESENT EMBODIMENT

The steering controller10of the present embodiment calculates, as the optimal steering angle, the steering angle that minimizes the output value of the evaluation function corresponding to the vehicle model. In addition, the steering controller10acquires the required acceleration when accelerating or decelerating the vehicle, and updates at least one weighting coefficient among the first weighting coefficient Q1, the second weighting coefficient Q2, the third weighting coefficient R1, and the fourth weighting coefficient R2of the evaluation function according to the acquired required acceleration. By doing this, the steering controller10calculates the optimal steering angle by applying the weighting coefficient updated according to the required acceleration to the evaluation function when the vehicle accelerates or decelerates. As a result, it is possible to prevent degradation of the path following accuracy at the time of the acceleration or deceleration of the vehicle, as compared with the case where the weighting coefficient is not updated.

The present disclosure has been described above on the basis of the exemplary embodiments. The technical scope of the present disclosure is not limited to the scope explained in the above embodiments, and it is obvious to those skilled in the art that various changes and modifications within the scope of the invention may be made. An aspect to which such changes and modifications are added can be included in the technical scope of the present invention is obvious from the description of the claims.