CN113120001A - Transverse control method and device for automatic driving vehicle and vehicle - Google Patents
Transverse control method and device for automatic driving vehicle and vehicle Download PDFInfo
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- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
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Abstract
The application discloses a transverse control method and device for an automatic driving vehicle and the vehicle, wherein the method comprises the following steps: acquiring actual coordinates and a current course angle of a vehicle to obtain position information of a current pose and a target point; calculating the distance deviation, the distance deviation change rate, the course angle deviation and the angle deviation change rate between the current moment and the target point according to the current pose and position information, and calculating a state matrix; and determining a first model parameter matrix and a second model parameter matrix by using a vehicle dynamics model, simultaneously selecting the first weighting matrix and the second weighting matrix to determine an optimal matrix according to a Linear Quadratic Regulator (LQR) algorithm, and controlling a steering actuator of the vehicle to execute steering control quantity obtained by multiplying the optimal matrix and a state matrix. Therefore, stability and comfort of vehicle tracking on a complex road with rapid curvature and speed change are guaranteed, and control accuracy and adaptability of the LQR controller are improved.
Description
Technical Field
The present disclosure relates to vehicle technologies, and in particular, to a lateral control method and device for an autonomous vehicle, and a vehicle.
Background
The transverse control carries out tracking control according to information such as a path, curvature and the like output by upper-layer motion planning so as to reduce tracking errors and ensure the stability and comfort of vehicle running; depending on the vehicle model used for lateral control, it can be classified into two types: (1) a model-free lateral control method; (2) a model-based lateral control method. The model-based lateral control method can be further divided into the following steps: a lateral control method based on a vehicle kinematic model and a lateral control method based on a vehicle dynamic model.
The main flow PID (proportional-Integral-Differential) control algorithm is model-free lateral control, and takes the current path tracking deviation of the vehicle as an input quantity, and performs proportional (contribution), Integral (Integral) and Differential (Differentiation) control on the tracking deviation to obtain a steering control quantity.
However, the algorithm does not consider the characteristics of the vehicle, has poor robustness to external interference, cannot meet effective control of the vehicle in a high-speed driving process, and needs to be solved urgently.
Content of application
The application provides a transverse control method and device for an automatic driving vehicle and the vehicle, so that stability and comfortableness of vehicle tracking on a complex road with rapid curvature and speed change are guaranteed, and control accuracy and adaptivity of an LQR (Low-rank response) controller are improved.
An embodiment of a first aspect of the application provides a lateral control method of an automatic driving vehicle, which comprises the following steps:
acquiring actual coordinates and a current course angle of a vehicle to obtain position information of a current pose and a target point;
calculating the distance deviation, the distance deviation change rate, the course angle deviation and the angle deviation change rate between the current moment and a target point according to the current pose and position information, and calculating a state matrix; and
determining a first model parameter matrix and a second model parameter matrix by using a vehicle dynamics model, simultaneously selecting the first weighting matrix and the second weighting matrix, determining an optimal matrix according to a Linear Quadratic Regulator (LQR) algorithm, and controlling a steering actuator of the vehicle to execute a steering control quantity obtained by multiplying the optimal matrix and the state matrix.
Optionally, the method further comprises:
and determining the vehicle dynamic model according to the front wheel side deflection rigidity, the rear wheel side deflection rigidity, the distance from the front shaft to the gravity center of the vehicle, the distance from the rear shaft to the gravity center of the vehicle, the z-axis rotational inertia of the vehicle and the mass of the whole vehicle.
Optionally, the calculating a state matrix according to the distance deviation, the distance deviation change rate, the course angle deviation and the angle deviation change rate between the current time and the target point according to the current pose and the position information includes:
judging the type of the current road;
if the type is a straight type, the target point is the point which is closest to the current position on the track;
if the type is a curve type, the target point is a point away from the pre-aiming distance when the actual speed of the vehicle is greater than a preset threshold value, otherwise, the target point is a point away from the pre-aiming distance determined by the curvature of the road.
Optionally, the calculation formula of determining the distance from the curvature of the road is as follows:
L＝kV+lmin，
wherein k is the linear change proportion of the speed, V is the actual speed of the vehicle, and lmin is the minimum set value of the pre-aiming distance.
Optionally, the calculation formula of the state matrix is:
state(0,0)＝e1；state(1,0)＝e2；state(2,0)＝e3；state(3,0)＝e4，
wherein, state (0,0) is the state matrix that the distance deviation corresponds, 1 is the distance deviation, state (1,0) are the state matrix that the distance deviation corresponds, e2 is the distance deviation rate of change, state (2,0) are the state matrix that the distance deviation rate of change corresponds, e3 is the course angle deviation, state (3,0) are the state matrix that the course angle deviation corresponds, e4 is the angle deviation rate of change, state (4,0) are the state matrix that the angle deviation rate of change corresponds.
An embodiment of a second aspect of the present application provides a lateral control device of an autonomous vehicle, comprising:
the acquisition module is used for acquiring the actual coordinates and the current course angle of the vehicle to obtain the current pose and the position information of the target point;
the calculation module is used for calculating the distance deviation, the distance deviation change rate, the course angle deviation and the angle deviation change rate between the current moment and a target point according to the current pose and position information and calculating a state matrix; and
and the control module is used for determining a first model parameter matrix and a second model parameter matrix by using a vehicle dynamics model, selecting the first weighting matrix and the second weighting matrix at the same time, determining an optimal matrix according to a Linear Quadratic Regulator (LQR) algorithm, and controlling a steering actuator of the vehicle to execute a steering control quantity obtained by multiplying the optimal matrix and the state matrix.
Optionally, the method further comprises:
and the determining module is used for determining the vehicle dynamic model according to the front wheel side deflection rigidity, the rear wheel side deflection rigidity, the distance from the front shaft to the gravity center of the vehicle, the distance from the rear shaft to the gravity center of the vehicle, the z-axis rotating inertia of the vehicle and the mass of the whole vehicle.
Optionally, the calculation module includes:
the judging unit is used for judging the type of the current road;
the first determining unit is used for determining that the target point is the point closest to the current position on the track if the type is the straight type;
and the second determining unit is used for determining that the target point is a point away from the pre-aiming distance when the actual speed of the vehicle is greater than a preset threshold value if the type is the curve type, and otherwise, determining that the target point is a point away from the pre-aiming distance according to the curvature of the road.
Optionally, the calculation formula of determining the distance from the curvature of the road is as follows:
L＝kV+lmin，
wherein k is the linear change proportion of the speed, V is the actual speed of the vehicle, and lmin is the minimum set value of the pre-aiming distance.
Optionally, the calculation formula of the state matrix is:
state(0,0)＝e1；state(1,0)＝e2；state(2,0)＝e3；state(3,0)＝e4，
wherein, state (0,0) is the state matrix that the distance deviation corresponds, e1 is distance deviation, state (1,0) are the state matrix that the distance deviation corresponds, e2 is the distance deviation change rate, state (2,0) are the state matrix that the distance deviation change rate corresponds, e3 is the course angle deviation, state (3,0) are the state matrix that the course angle deviation corresponds, e4 is the angle deviation change rate, state (4,0) are the state matrix that the angle deviation change rate corresponds.
An embodiment of a third aspect of the present application provides a vehicle comprising the lateral control device of an autonomous vehicle described above.
Therefore, the actual coordinates and the current course angle of the vehicle can be obtained, the position information of the current pose and the target point is obtained, the distance deviation change rate, the course angle deviation and the angle deviation change rate between the current moment and the target point are calculated according to the current pose and the position information, the state matrix is calculated, the first model parameter matrix and the second model parameter matrix are determined by using the vehicle dynamics model, the first weighting matrix and the second weighting matrix are selected simultaneously, the optimal matrix is determined according to the linear quadratic regulator LQR algorithm, and the steering actuator of the vehicle is controlled to execute the steering control quantity obtained by multiplying the optimal matrix and the state matrix, so that the stability and the comfort of vehicle tracking on a complex road with fast curvature and speed change are ensured, and the control accuracy and the self-adaptability of the LQR controller are improved.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a lateral control method for an autonomous vehicle according to an embodiment of the present application;
FIG. 2 is an exemplary illustration of an LQR lateral longitudinal error according to one embodiment of the present application;
FIG. 3 is a flow diagram of an LQR algorithm according to one embodiment of the present application;
FIG. 4 is a graphical illustration of the tracking effect of curves at different speeds before optimization according to one embodiment of the present application;
FIG. 5 is a graph illustrating the tracking effect of curves at different speeds after optimization according to one embodiment of the present application;
fig. 6 is a block diagram illustrating an example of a lateral control apparatus of an autonomous vehicle according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The following describes a lateral control method and device for an autonomous vehicle, and a vehicle according to an embodiment of the present application, with reference to the drawings.
Before describing the lateral control method of an autonomous vehicle according to an embodiment of the present application, a processing manner in the related art will be briefly described.
In the related technology, the LQR algorithm uses a two-degree-of-freedom dynamic model to design a transverse controller, and has the advantages that the LQR algorithm is effectively combined with steering feedforward, so that the steady-state tracking error of a part of curves in running can be well solved, and the steady-state error of the curves in running at a medium speed is close to zero, thereby greatly improving the tracking performance.
However, the tracking effect is obviously reduced under the conditions of large curvature and high speed, and the dependence degree on environment and parameter selection is high, namely the tracking under the new state condition cannot be well adapted under the condition of sudden change of the environment. Meanwhile, the adjustment of the LQR parameters is complex, the model parameters of the vehicle are required to be obtained, the QR matrix of the LQR target function is required to be adjusted, the tracking performance of the LQR algorithm is greatly reduced due to inaccurate selection of the QR matrix, and therefore control failure is caused.
The present application thus provides a lateral control method of an autonomous vehicle, in which method, the actual coordinates and the current course angle of the vehicle can be obtained to obtain the current pose and the position information of the target point, and calculates the distance deviation, distance deviation change rate, course angle deviation and angle deviation change rate between the current time and the target point according to the current pose and position information, calculates a state matrix, determining a first model parameter matrix and a second model parameter matrix by using a vehicle dynamics model, and selecting a first weighting matrix and a second weighting matrix simultaneously, determining an optimal matrix according to a linear quadratic regulator LQR algorithm, controlling a steering actuator of the vehicle to execute a steering control quantity obtained by multiplying the optimal matrix and a state matrix, therefore, stability and comfort of vehicle tracking on a complex road with fast curvature and speed change are guaranteed, and control accuracy and adaptability of the LQR controller are improved.
Specifically, fig. 1 is a schematic flowchart of a lateral control method of an autonomous vehicle according to an embodiment of the present disclosure.
As shown in fig. 1, the lateral control method of the autonomous vehicle includes the steps of:
in step S101, the actual coordinates and the current heading angle of the vehicle are obtained, and the position information of the current pose and the target point is obtained.
It can be understood that the method of obtaining the actual coordinates and the current heading angle of the vehicle and obtaining the position information of the current pose and the target point according to the actual coordinates and the current heading angle may adopt a processing method in the related art, and detailed description is not given here to avoid redundancy.
In step S102, a distance deviation change rate, a course angle deviation, and an angle deviation change rate between the current time and the target point are calculated according to the current pose and the position information, and a state matrix is calculated.
Optionally, in some embodiments, the distance deviation change rate, the heading angle deviation and the angle deviation change rate between the current time and the target point are calculated according to the current pose and the position information, and the calculating the state matrix includes: judging the type of the current road; if the type is a straight type, the target point is the point closest to the current position on the track; if the type is a curve type, when the actual speed of the vehicle is greater than a preset threshold value, the target point is a point away from the pre-aiming distance, otherwise, the target point is a point away from the pre-aiming distance determined by the curvature of the road.
The preset threshold may be a threshold preset by a user, may be a threshold obtained through a limited number of experiments, or may be a threshold obtained through a limited number of computer simulations. Preferably, the preset threshold is 60 km/h.
Specifically, as shown in fig. 2, Vx is the vehicle longitudinal vehicle speed, Vy is the vehicle lateral vehicle speed,
for the yaw rate of the vehicle, ratio is the ratio of the steering wheel angle and the wheel angle of the vehicle, and as can be seen from FIG. 2, the calculation of the lateral error and the longitudinal error is based on the comparison of the current time point and the target point, so the following description will proceedThe rows detail how the target points are acquired.
Specifically, the road type may generally include a straight road type and a curve type, and if the road type is the straight road type, the target point is selected as the point closest to the current position on the trajectory, so as to ensure the accuracy of the straight line tracking; if the type of road is a curve type, the determination may be made based on the actual vehicle speed of the vehicle. For example, when the actual speed of the vehicle is greater than a preset threshold, such as 60km/h, the target point is selected as the pre-aiming distance L; for another example, when the actual vehicle speed of the vehicle is less than the preset threshold, the road curvature determines the distance, and the calculation formula may be:
L＝kV+lmin。
wherein k is the linear change proportion of the speed, V is the actual speed of the vehicle, and lmin is the minimum set value of the pre-aiming distance.
It should be noted that the selection of k, lmax and lmin is performed by selecting through single scene controlled variable simulation, and the optimal solution can be obtained by adjusting the curvature and the speed respectively, and then substituted according to the above formula. Preferably, lmin is 15, lmax is 30, and k is 0.05.
Further, in some embodiments, e1 is the displacement from the current time to the target point, and the state matrix state is calculated by the following formula;
e3＝θ-θ1；
e2＝Vx*e2+Vy；
state (0,0) ═ e1 is obtained; state (1,0) ═ e 2; state (2,0) ═ e 3; state (3,0) e4,
wherein, R is the curvature radius of the target track point, state (0,0) is the state matrix corresponding to the distance deviation, e1 is the distance deviation, state (1,0) is the state matrix corresponding to the distance deviation, e2 is the change rate of the distance deviation, state (2,0) is the state matrix corresponding to the change rate of the distance deviation, e3 is the course angle deviation, state (3,0) is the state matrix corresponding to the course angle deviation, e4 is the change rate of the angle deviation, and state (4,0) is the state matrix corresponding to the change rate of the angle deviation.
In step S103, a first model parameter matrix and a second model parameter matrix are determined by using the vehicle dynamics model, and the first weighting matrix and the second weighting matrix are simultaneously selected to determine an optimal matrix according to the LQR algorithm, and to control a steering actuator of the vehicle to execute a steering control amount obtained by multiplying the optimal matrix and the state matrix.
As shown in fig. 3, fig. 3 is a flowchart of the LQR algorithm, which mainly includes the following steps:
and S301, sensing environment and vehicle information.
Wherein sensing environmental and vehicular information comprises: and the coordinates and the course angle of the vehicle, and the coordinates and the course angle of the tracking target point.
And S302, processing data.
Wherein, after the data processing, the processed data is sent to step S309.
S303, judging the curvature of the straight road curve, if the curve is a straight road, executing the step S304, and if the curve is a curve, executing the step S305.
S304, the target point is the point closest to the current position on the track, and the step S308 is executed.
S305, judging whether the actual speed of the vehicle is greater than a preset threshold value, if so, executing a step S306, otherwise, executing a step S307.
And S306, selecting the target point as a pre-aiming distance L (lmax), and jumping to execute the step S308.
And S307, selecting a target point as a pre-aiming distance L which is kV + lmin.
S308, state quantity, and jumps to execute step S310.
And calculating the distance deviation e1, the distance deviation change rate e2, the course deviation e3 and the angle deviation change rate e4 of the vehicle and the target point at the current moment according to the real-time pose and the position information of the target point, thereby obtaining the state matrix state.
S309, QR weight matrix selector.
That is, the embodiment of the present application may determine the model parameter matrices a and B according to the above-mentioned kinetic model parameters, and select the weighting matrices Q and R (selection of QR weight).
Thus, a state matrix is obtained, and the state matrix and the QR weight matrix selector are input to the LQR controller.
And S310, an LQR controller.
S311, the vehicle turns to the actuator, and skips to execute the step S301.
Thus, the steering control amount of the autonomous vehicle is calculated based on the determined controller parameter, and transmitted to the steering actuator for execution.
Optionally, in some embodiments, the method further comprises: and determining a vehicle dynamic model according to the front wheel side deflection rigidity, the rear wheel side deflection rigidity, the distance from the front shaft to the gravity center of the vehicle, the distance from the rear shaft to the gravity center of the vehicle, the z-axis rotational inertia of the vehicle and the mass of the whole vehicle.
That is, the parameters of the vehicle dynamics model mainly include: the vehicle dynamic model comprises a front wheel side deflection rigidity Cf, a vehicle dynamic model Cr, a distance lf from a front shaft to the gravity center of the vehicle, a distance lr from a rear shaft to the gravity center of the vehicle, a z-axis rotating inertia IZ of the vehicle and the mass m of the whole vehicle.
It should be noted that the parameters of the vehicle dynamics model may be obtained by querying basic information of the vehicle, or may be obtained by re-measuring, and specifically may be processed by a person skilled in the art according to actual situations, and are not limited herein.
Further, the calculation formula for determining the first model parameter matrix _ a _ and the second model parameter matrix _ b _ using the vehicle dynamics model may be as follows:
further, a first weighting matrix Q and a second weighting matrix R are selected, the first weighting matrix Q is selected, and a diagonal matrix _ Q _ diag [ Q1, Q2, Q3, Q4] is selected, wherein Q1, Q2, Q3 and Q4 correspond to four variables of a state matrix state respectively, and the selection of Q1 and Q3 is the key of LQR control; the second weighting matrix Rky selects the identity matrix _ r ═ 1; it can be known from the above flowchart of fig. 3 that the tracking system obtains environmental information by sensing for selection:
(1) firstly, judging the curvature radius R of a road, R1 and R2, wherein R1 and R2 are boundary conditions for distinguishing a straight road from a curved road, and distinguishing a small curvature from a large curvature;
(2) when R < R1, judging the tracking curve to be a straight line, selecting q 3-kq-q 1, and kq-0.1V;
(3) when R1< R < R2, the tracking track is judged to be a curve with small curvature, and q3 is selected to kq q1, and kq is selected to be V;
(4) when R < R2, the tracking track is judged to be a curve with large curvature, and q3 is selected to be kq q1, and kq is selected to be 10V;
(5) according to the formula, the initial value of Q1 can be determined by only real vehicle single straight line tracking to obtain the first weighting matrix Q. Further, an optimal matrix _ k is determined according to numerical iteration solution of the Riccati equation, and is obtained by the following formula;
while(num_iteration++<max_num_iteration)
{
matrix_p_next＝
matrix_a_T*matrix_p_*matrix_a_-(matrix_a_T*matrix_p_*matrix_b_)*(matrix_r_+matrix_b_T*matrix_p_*matrix_b_).inverse()*(matrix_b_T*matrix_p_*matrix_a_)
+matrix_q_；
matrix_p_＝matrix_p_next；
}
matrix_k_＝(matrix_r_+matrix_b_T*matrix_p_*matrix_b_).inverse()*(matrix_b_T*matrix_p_*matrix_a_)
wherein max _ num _ iteration is 150 selected as the maximum iteration number, matrix _ a _ T and matrix _ b _ T are respectively the transpose matrices of matrix _ a _ and matrix _ b _ and matrix _ p is the process iteration matrix.
Further, multiplying the optimal matrix _ k and the state matrix state to obtain a front wheel rotation angle, and finally multiplying the front wheel rotation angle by the ratio to output the front wheel rotation angle to an execution mechanism to realize tracking;
through simulation comparison verification of a curve with an initial deviation of 0.5m added (under the condition of simulated abrupt change of environment), as shown in fig. 4 and 5, fig. 4 is a schematic diagram of tracking errors in the case of no target point and no parameter adaptation, wherein the line 1 is the case of V20 km/h, the line 2 is the case of V30 km/h, the line 3 is the case of V40 km/h, the line 4 is the case of V50 km/h, the line 5 is the case of V60 km/h, fig. 5 is a schematic diagram of tracking errors after adaptive optimization of the target point and the QR matrix, wherein the line 6 is the case of V20 km/h, the line 7 is the case of V30 km/h, the line 8 is the case of V40 km/h, the line 9 is the case of V50 km/h, and the line 10 is the case of V60 km/h, obviously, the optimized system can generate smaller fluctuation after interference, can quickly recover stability, and improves the comfort and the stability of the system.
Therefore, based on LQR control, adaptive preview selection control selection is added to ensure the stability and comfort of vehicle tracking on complex roads with fast curvature and speed change; meanwhile, the problem of QR matrix selection is solved by summarizing the selection of the self-adaptive formula to determine the QR matrix, and finally, the effect is improved by simulation contrast updating.
According to the transverse control method of the automatic driving vehicle provided by the embodiment of the application, the actual coordinate and the current course angle of the vehicle can be obtained, the position information of the current pose and the target point can be obtained, and calculates the distance deviation, distance deviation change rate, course angle deviation and angle deviation change rate between the current time and the target point according to the current pose and position information, calculates a state matrix, determining a first model parameter matrix and a second model parameter matrix by using a vehicle dynamics model, and selecting a first weighting matrix and a second weighting matrix simultaneously, determining an optimal matrix according to a linear quadratic regulator LQR algorithm, controlling a steering actuator of the vehicle to execute a steering control quantity obtained by multiplying the optimal matrix and a state matrix, therefore, stability and comfort of vehicle tracking on a complex road with fast curvature and speed change are guaranteed, and control accuracy and adaptability of the LQR controller are improved.
Next, a lateral control apparatus of an autonomous vehicle according to an embodiment of the present application will be described with reference to the drawings.
Fig. 6 is a block schematic diagram of a lateral control device of an autonomous vehicle according to an embodiment of the present application.
As shown in fig. 6, the lateral control device 10 of the autonomous vehicle includes: an acquisition module 100, a calculation module 200 and a control module 300.
The acquiring module 100 is configured to acquire an actual coordinate and a current heading angle of a vehicle to obtain a current pose and position information of a target point;
the calculation module 200 is configured to calculate a distance deviation, a distance deviation change rate, a course angle deviation and an angle deviation change rate between the current time and a target point according to the current pose and position information, and calculate a state matrix; and
the control module 300 is configured to determine a first model parameter matrix and a second model parameter matrix by using a vehicle dynamics model, and select the first weighting matrix and the second weighting matrix at the same time, so as to determine an optimal matrix according to a linear quadratic regulator LQR algorithm, and control a steering actuator of the vehicle to execute a steering control quantity obtained by multiplying the optimal matrix and a state matrix.
Optionally, in some embodiments, the lateral control device 10 of the autonomous vehicle further includes:
the determining module is used for determining a vehicle dynamic model according to the front wheel side deflection rigidity, the rear wheel side deflection rigidity, the distance from the front shaft to the gravity center of the vehicle, the distance from the rear shaft to the gravity center of the vehicle, the z-axis rotating inertia of the vehicle and the mass of the whole vehicle.
Optionally, in some embodiments, the calculation module 200 comprises:
the judging unit is used for judging the type of the current road;
the first determining unit is used for determining that the target point is the point closest to the current position on the track if the type is the straight track type;
and the second determining unit is used for determining that the target point is a point away from the pre-aiming distance when the actual speed of the vehicle is greater than a preset threshold value if the type is the curve type, and otherwise, determining that the target point is a point away from the pre-aiming distance according to the curvature of the road.
Optionally, in some embodiments, the calculation formula for determining the distance from the curvature of the road is:
L＝kV+lmin，
wherein k is the linear change proportion of the speed, V is the actual speed of the vehicle, and lmin is the minimum set value of the pre-aiming distance.
Optionally, in some embodiments, the calculation formula of the state matrix is:
state(0,0)＝e1；state(1,0)＝e2；state(2,0)＝e3；state(3,0)＝e4，
wherein, state (0,0) is a state matrix corresponding to the distance deviation, e1 is the distance deviation, state (1,0) is the state matrix corresponding to the distance deviation, e2 is the change rate of the distance deviation, state (2,0) is the state matrix corresponding to the change rate of the distance deviation, e3 is the heading angle deviation, state (3,0) is the state matrix corresponding to the heading angle deviation, e4 is the change rate of the angle deviation, and state (4,0) is the state matrix corresponding to the change rate of the angle deviation.
It should be noted that the foregoing explanation of the embodiment of the lateral control method of the autonomous vehicle is also applicable to the lateral control device of the autonomous vehicle of this embodiment, and will not be described again here.
According to the transverse control device of the automatic driving vehicle provided by the embodiment of the application, the actual coordinate and the current course angle of the vehicle can be obtained, the position information of the current pose and the target point can be obtained, and calculates the distance deviation, distance deviation change rate, course angle deviation and angle deviation change rate between the current time and the target point according to the current pose and position information, calculates a state matrix, determining a first model parameter matrix and a second model parameter matrix by using a vehicle dynamics model, and selecting a first weighting matrix and a second weighting matrix simultaneously, determining an optimal matrix according to a linear quadratic regulator LQR algorithm, controlling a steering actuator of the vehicle to execute a steering control quantity obtained by multiplying the optimal matrix and a state matrix, therefore, stability and comfort of vehicle tracking on a complex road with fast curvature and speed change are guaranteed, and control accuracy and adaptability of the LQR controller are improved.
In addition, the embodiment of the application also provides a vehicle, and the vehicle comprises the transverse control device of the automatic driving vehicle.
According to the vehicle provided by the embodiment of the application, the actual coordinate and the current course angle of the vehicle can be obtained through the transverse control device of the automatic driving vehicle, the position information of the current pose and the target point is obtained, the distance deviation change rate, the course angle deviation and the angle deviation change rate between the current moment and the target point are calculated according to the current pose and the position information, the state matrix is calculated, the first model parameter matrix and the second model parameter matrix are determined by using a vehicle dynamics model, the first weighting matrix and the second weighting matrix are selected simultaneously, the optimal matrix is determined according to the linear quadratic regulator LQR algorithm, the steering actuator of the vehicle is controlled to execute the steering control quantity obtained by multiplying the optimal matrix and the state matrix, and therefore the stability and the comfort of vehicle tracking on a complex road with fast curvature and speed change are ensured, the control precision and the adaptability of the LQR controller are improved.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of implementing the embodiments of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
Claims (10)
1. A lateral control method of an autonomous vehicle, characterized by comprising the steps of:
acquiring actual coordinates and a current course angle of a vehicle to obtain position information of a current pose and a target point;
calculating the distance deviation, the distance deviation change rate, the course angle deviation and the angle deviation change rate between the current moment and a target point according to the current pose and position information, and calculating a state matrix; and
determining a first model parameter matrix and a second model parameter matrix by using a vehicle dynamics model, simultaneously selecting the first weighting matrix and the second weighting matrix to determine an optimal matrix according to a Linear Quadratic Regulator (LQR) algorithm, and controlling a steering actuator of the vehicle to execute a steering control quantity obtained by multiplying the optimal matrix and the state matrix.
2. The method of claim 1, further comprising:
and determining the vehicle dynamic model according to the front wheel side deflection rigidity, the rear wheel side deflection rigidity, the distance from the front shaft to the gravity center of the vehicle, the distance from the rear shaft to the gravity center of the vehicle, the z-axis rotational inertia of the vehicle and the mass of the whole vehicle.
3. The method according to claim 1, wherein the calculating of the distance deviation, the distance deviation change rate, the course angle deviation and the angle deviation change rate from the target point at the current moment according to the current pose and the position information and the calculating of the state matrix comprise:
judging the type of the current road;
if the type is a straight type, the target point is the point which is closest to the current position on the track;
if the type is a curve type, the target point is a point away from the pre-aiming distance when the actual speed of the vehicle is greater than a preset threshold value, otherwise, the target point is a point away from the pre-aiming distance determined by the curvature of the road.
4. The method of claim 3, wherein the calculation formula for determining the distance from the curvature of the road is:
L＝kV+lmin，
wherein k is the linear change proportion of the speed, V is the actual speed of the vehicle, and lmin is the minimum set value of the pre-aiming distance.
5. The method of claim 3, wherein the state matrix is calculated by:
state(0,0)＝e1；state(1,0)＝e2；state(2,0)＝e3；state(3,0)＝e4，
wherein, state (0,0) is the state matrix that the distance deviation corresponds, e1 is distance deviation, state (1,0) are the state matrix that the distance deviation corresponds, e2 is the distance deviation change rate, state (2,0) are the state matrix that the distance deviation change rate corresponds, e3 is the course angle deviation, state (3,0) are the state matrix that the course angle deviation corresponds, e4 is the angle deviation change rate, state (4,0) are the state matrix that the angle deviation change rate corresponds.
6. A lateral control apparatus for an autonomous vehicle, comprising:
the acquisition module is used for acquiring the actual coordinates and the current course angle of the vehicle to obtain the current pose and the position information of the target point;
the calculation module is used for calculating the distance deviation, the distance deviation change rate, the course angle deviation and the angle deviation change rate between the current moment and a target point according to the current pose and position information and calculating a state matrix; and
determining a first model parameter matrix and a second model parameter matrix by using a vehicle dynamics model, simultaneously selecting the first weighting matrix and the second weighting matrix to determine an optimal matrix according to a Linear Quadratic Regulator (LQR) algorithm, and controlling a steering actuator of the vehicle to execute a steering control quantity obtained by multiplying the optimal matrix and the state matrix.
7. The apparatus of claim 6, further comprising:
and the determining module is used for determining the vehicle dynamic model according to the front wheel side deflection rigidity, the rear wheel side deflection rigidity, the distance from the front shaft to the gravity center of the vehicle, the distance from the rear shaft to the gravity center of the vehicle, the z-axis rotating inertia of the vehicle and the mass of the whole vehicle.
8. The apparatus of claim 6, wherein the computing module comprises:
the judging unit is used for judging the type of the current road;
the first determining unit is used for determining that the target point is the point closest to the current position on the track if the type is the straight type;
and the second determining unit is used for determining that the target point is a point away from the pre-aiming distance when the actual speed of the vehicle is greater than a preset threshold value if the type is the curve type, and otherwise, determining that the target point is a point away from the pre-aiming distance according to the curvature of the road.
9. The apparatus of claim 8, wherein the calculation formula for determining the distance from the curvature of the road is:
L＝kV+lmin，
wherein k is the linear change proportion of the speed, V is the actual speed of the vehicle, and lmin is the minimum set value of the pre-aiming distance.
The calculation formula of the state matrix is as follows:
state(0,0)＝e1；state(1,0)＝e2；state(2,0)＝3；state(3,0)＝e4，
wherein, state (0,0) is the state matrix that the distance deviation corresponds, e1 is distance deviation, state (1,0) are the state matrix that the distance deviation corresponds, e2 is the distance deviation change rate, state (2,0) are the state matrix that the distance deviation change rate corresponds, e3 is the course angle deviation, state (3,0) are the state matrix that the course angle deviation corresponds, e4 is the angle deviation change rate, state (4,0) are the state matrix that the angle deviation change rate corresponds.
10. A vehicle, characterized by comprising: the lateral control apparatus of an autonomous vehicle as claimed in any of claims 6-9.
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| CN202110510779.1A CN113120001A (en) | 2021-05-11 | 2021-05-11 | Transverse control method and device for automatic driving vehicle and vehicle |
| CN202210210929.1A CN114655248A (en) | 2021-05-11 | 2022-03-04 | Transverse control method and device for automatic driving vehicle and vehicle |
| PCT/CN2022/085370 WO2022237392A1 (en) | 2021-05-11 | 2022-04-06 | Vehicle lateral control method and apparatus, and vehicle |
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