Automobile and preview control method, device and storage medium thereof

Abstract

The present application belongs to the field of intelligent driving, and proposes a method, device and storage medium for preview control of a car and the same. The method includes: determining a first state equation between the posture of the vehicle without preview and the actual reference trajectory, and determining a second state equation between the vehicle state information and the actual reference trajectory when preview is present; determining a third state equation for describing the vehicle-road preview error based on the first state equation and the second state equation; determining a feedback gain based on the deviation between the vehicle state information and the actual reference trajectory; and determining a preview control instruction for the vehicle based on the feedback gain and the third state equation. In addition to considering the current reference point position, changes in state information within a period of time in the future are also considered, thereby enabling better vehicle control, reducing the sense of frustration caused by the sharp change of the actual reference trajectory during vehicle driving, and improving the comfort of vehicle driving.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving, and in particular to a vehicle and its preview control method, device and storage medium. Background Art

[0002] With the development of intelligent technology, autonomous driving technology has also advanced rapidly. Path tracking, a key executive-layer control technology in intelligent driving, significantly impacts the control accuracy of autonomous vehicles. Excellent path tracking technology significantly improves the safety and comfort of intelligent vehicles.

[0003] Current path-following technologies typically employ a preview-based motion control approach. This approach uses the pose of a preview point ahead of the vehicle as controller input, ensuring good adaptability to changes in the curvature of the reference trajectory. However, this preview control approach only considers the deviation between the current state and the reference state. When the reference trajectory changes dramatically, this can easily cause a sense of jerkiness in the following vehicle, hindering driving comfort. Summary of the Invention

[0004] In view of this, the embodiments of the present application provide a vehicle and its preview control method, device and storage medium to solve the problem in the prior art that when performing preview control on a car, if the reference trajectory changes drastically, it is easy to cause a sense of frustration in the following vehicle, which is not conducive to enhancing the comfort of vehicle driving.

[0005] A first aspect of an embodiment of the present application provides a preview control method for an automobile, the method comprising:

[0006] Determining a first state equation between a vehicle pose without preview and an actual reference trajectory, and determining a second state equation between the vehicle state information and the actual reference trajectory with preview;

[0007] Determining a third state equation for describing a vehicle-road preview error based on the first state equation and the second state equation;

[0008] determining a feedback gain according to a deviation between the vehicle state information and an actual reference trajectory;

[0009] A preview control instruction of the vehicle is determined according to the feedback gain and the third state equation.

[0010] In conjunction with the first aspect, in a first possible implementation of the first aspect, determining a second state equation between the vehicle state information and the actual reference trajectory when previewing includes:

[0011] determining a second state equation between the longitudinal acceleration of the vehicle and the longitudinal acceleration of the previewed actual reference trajectory;

[0012] Determining a third state equation for describing a vehicle-road preview error based on the first state equation and the second state equation includes:

[0013] The first state equation and the second state equation are combined to obtain a third state equation for describing a vehicle-road preview error.

[0014] In conjunction with the first aspect, in a second possible implementation of the first aspect, determining a second state equation between the vehicle state information and the actual reference trajectory when previewing includes:

[0015] Determining a first auxiliary reference trajectory according to the actual reference trajectory and the state information of the vehicle;

[0016] Determine a longitudinal position error equation between the vehicle position and the actual reference trajectory in the second state equation according to the first auxiliary reference trajectory;

[0017] A speed error equation between the vehicle speed in the second state equation and the actual reference trajectory is determined according to the first auxiliary reference trajectory.

[0018] In combination with the first possible implementation manner or the second possible implementation manner of the first aspect, in a third possible implementation manner of the first aspect, determining the feedback gain based on the deviation between the state information of the vehicle and the actual reference trajectory includes:

[0019] Determine the matrix P through the Riccati equation;

[0020] The feedback gain is determined according to the matrix P.

[0021] In combination with the third possible implementation of the first aspect, in a fourth possible implementation of the first aspect,

[0022] Determining a preview control instruction of the vehicle according to the feedback gain and the third state equation includes:

[0023] A longitudinal acceleration control command of the vehicle is determined according to the feedback gain and the third state equation.

[0024] In conjunction with the first aspect, in a fifth possible implementation of the first aspect, determining a second state equation between the vehicle state information and the actual reference trajectory when previewing includes:

[0025] Determine a lateral distance error equation between the position of the vehicle in the second state equation when previewing and the actual reference trajectory;

[0026] Determine a heading angle error equation between the vehicle's driving direction and the actual reference trajectory in the second state equation when previewing;

[0027] Determining a third state equation for describing a vehicle-road preview error based on the first state equation and the second state equation includes:

[0028] The lateral distance error and the heading angle error are used as output variables in the state space to obtain a third state equation for describing the vehicle-road preview error.

[0029] In conjunction with the fifth possible implementation manner of the first aspect, in a sixth possible implementation manner of the first aspect, determining the preview control instruction of the vehicle according to the feedback gain and the third state equation includes:

[0030] A longitudinal acceleration control command of the vehicle is determined according to the feedback gain and the third state equation.

[0031] A second aspect of an embodiment of the present application provides a preview control device for an automobile, the device comprising:

[0032] a first state equation determining unit, configured to determine a first state equation between a vehicle posture without preview and an actual reference trajectory, and to determine a second state equation between the vehicle state information and the actual reference trajectory with preview;

[0033] a third state equation determining unit, configured to determine a third state equation for describing a vehicle-road preview error based on the first state equation and the second state equation;

[0034] a feedback gain determination unit, configured to determine a feedback gain according to a deviation between the state information of the vehicle and an actual reference trajectory;

[0035] A preview control unit is used to determine a preview control instruction of the vehicle according to the feedback gain and the third state equation.

[0036] A third aspect of an embodiment of the present application provides a vehicle, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in any one of the first aspects when executing the computer program.

[0037] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of the first aspects are implemented.

[0038] Compared with the prior art, the embodiments of the present application have the following beneficial effects: when determining the state equation, in addition to considering the first state equation of the state between the vehicle and the reference point, the embodiments of the present application also include a second state equation between the previewed vehicle state information and the actual reference trajectory; based on the first state equation and the second state equation, a third state equation for describing the vehicle-road preview error is determined; based on the deviation between the vehicle state information and the actual reference trajectory, a feedback gain is determined; based on the feedback gain and the third state equation, a preview control instruction of the vehicle is determined; in addition to considering the current reference point position, changes in state information such as position and speed in the future are also previewed, thereby achieving better vehicle control, reducing the sense of frustration caused by the sudden change of the actual reference trajectory during vehicle driving, and helping to improve the comfort of vehicle driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0040] Figure 1 This is a schematic diagram of the implementation flow of a preview control method for a vehicle provided in an embodiment of the present application;

[0041] Figure 2 This is a schematic diagram of a vehicle-road preview error model provided by an embodiment of the present application;

[0042] Figure 3 This is a schematic diagram of a two-degree-of-freedom mechanical model of a vehicle provided in an embodiment of the present application;

[0043] Figure 4 This is a schematic diagram of a road profile model provided in an embodiment of the present application;

[0044] Figure 5 This is a schematic diagram of a vehicle-road preview error model provided by an embodiment of the present application;

[0045] Figure 6 1 is a schematic diagram of a preview control device for a vehicle provided in an embodiment of the present application;

[0046] Figure 7 It is a schematic diagram of a vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0048] In order to illustrate the technical solution described in this application, specific embodiments are provided below.

[0049] With the rapid development of automotive technology, cars have become an essential means of transportation. Path tracking, as a key executive-layer control technology in autonomous driving systems, is crucial for the safety and comfort of intelligent vehicles. Current autonomous driving technologies typically use the position and posture of pre-set preview time points as control input to adapt to changes in the curvature of the reference trajectory. However, this preview control approach only considers the deviation between the current state and the reference state. If the reference trajectory undergoes drastic changes, such as sharp turns or sudden changes in acceleration, it can easily cause the following vehicle to experience a sense of frustration, hindering driving comfort.

[0050] Based on the above problems, the present application proposes a preview control method for a car, such as Figure 1 As shown, the method includes:

[0051] In S101, a first state equation between the vehicle posture without preview and the actual reference trajectory is determined, and a second state equation between the vehicle state information and the actual reference trajectory is determined with preview.

[0052] In the embodiments of the present application, the preview refers to searching for a preview point in a road environment or an actual reference trajectory while the vehicle is traveling. Based on the determined preview point, the displacement between the current vehicle position and the preview point on the actual reference trajectory can be calculated.

[0053] The preview control in the embodiment of the present application includes longitudinal preview control and lateral preview control, wherein the longitudinal direction is the vehicle's forward direction, and the lateral direction is the direction perpendicular to the vehicle's forward direction on the vehicle's driving plane.

[0054] During longitudinal preview control, a vehicle-road error model without preview can be constructed based on the kinematic formula.

[0055] Among them, based on the position error and speed error as state quantities, the constructed state equation can be expressed as:

[0056]

[0057] in, a represents the acceleration of the vehicle, a ref is the acceleration of the reference point in the actual reference trajectory, represents the derivative of x with respect to time, v represents the speed of the vehicle, v ref is the velocity of the reference point in the actual reference trajectory, e v is the speed error between the vehicle's speed and the speed of the reference point in the actual reference trajectory, a ref is the acceleration of the reference point, e a is the acceleration error between the vehicle acceleration and the acceleration of the reference point in the actual reference trajectory, e s is the position error between the vehicle's position and the reference point, represents the time derivative of the position error, represents the time derivative of the velocity error.

[0058] In order to facilitate the calculation of the model and improve the control efficiency, a discretized model can be used to model the preview information of the actual reference trajectory (for example, a road). The time interval of the discrete sampling is T, the discrete sampling distance interval is vT, v is the vehicle speed, the preview time is hT, and h is the number of samples in the preview period. The first state equation after discretization is obtained:

[0059] x k+1 =A d x k +B d a k +E d a ref (2)

[0060] Among them, x k+1 Indicates the state quantity corresponding to time k+1, A d 、B d 、E d The vector matrix representing the discrete state equation. By discretizing the state equation without preview, and comparing it to other preview calculation algorithms, the system's computational complexity can be effectively reduced by discretely calculating the vehicle's acceleration command.

[0061] The first state equation described above is constructed using position error and velocity error as state quantities. In practical applications, this is not limited to position error and velocity error as state quantities. The first state equation can also include expressions for acceleration, throttle, and brake. For example, the state quantity expressions in the first state equation can include expressions for the acceleration state quantity and the throttle, as well as expressions for acceleration and brake.

[0062] When determining the second state equation, the present application adopts a time window of predetermined length as the preview window. By performing discrete sampling in the preview window, a second state equation constructed by multiple sampling points is obtained. The second state equation sampled in the present application may include a state equation based on longitudinal acceleration preview of the road (actual reference trajectory), a state equation based on longitudinal position preview, a state equation based on longitudinal slope preview, a state equation based on longitudinal speed preview, and a state equation based on longitudinal acceleration change rate preview, etc. The state equation based on longitudinal acceleration preview and the state equation based on longitudinal position preview are introduced and explained below.

[0063] When determining the second state equation based on acceleration preview, we can assume that the preview window length is h (indicating that the preview window can be sampled h times, with the time interval between each sampling being T), the current time is k, and the preview window starts at the current time k and ends at time k+h. Then, the acceleration state equation (second state equation, road preview model, or acceleration preview model) based on the actual reference trajectory determined within this preview window can be expressed as:

[0064] a r (k+1)=A a a r (k)+Fa r_h+1 (k) (3)

[0065] Among them, A a is the state matrix of the road preview model, F is the input matrix of the road preview model, a r_h+1 is the input variable of the road model, and a r0 =a ref , a r (k+1) represents the longitudinal acceleration sampled on the actual reference trajectory in the preview window determined at time k+1, a r (k) represents the longitudinal acceleration sampled on the actual reference curve within the preview window determined at time k (i.e., the starting time of the preview window, i.e., the current time), a r_h+1 (k) represents the longitudinal acceleration at the h+1th sampling moment of the actual reference trajectory determined at time k.

[0066] When determining the second state equation based on the longitudinal position preview model, it can be assumed that the longitudinal speed of the vehicle is planned to be uniform, then the acceleration of the reference point at the kth moment in the non-preview state space is is 0. The first state equation can be expressed as:

[0067] x k+1 =A d x k +B d a k (4)

[0068] like Figure 2 A schematic diagram of a vehicle-road longitudinal preview error model provided in an embodiment of the present application is shown as follows: Figure 2 As shown, the following path determined based on the first state equation is set as the auxiliary reference trajectory, as shown in Figure 2 The auxiliary reference trajectory is a straight line whose slope is the reference point velocity of the actual reference trajectory at time k. The starting point of the auxiliary reference trajectory is the position s of the actual reference trajectory at the current moment r_k After introducing the auxiliary reference trajectory, the longitudinal position error at each moment can be expressed as:

[0069]

[0070] Where (k+i|k) represents the state quantity at time k+i predicted based on the state quantity at time k, where i = 0, 1, ..., h; Yes(k+i|k) is the position error (in meters) between the vehicle position at time k+i and the position sampled on the actual reference trajectory at time k+i; s(k+i|k) is the position of the vehicle at time k+i predicted based on the vehicle position at time k (in meters); e s (k+i|k) is the position error between the vehicle at time k+i and the auxiliary reference trajectory predicted based on the state at time k (unit: m). The auxiliary reference trajectory can be calculated based on the vehicle-road error state equation without preview. r0 , s r1 ,…,s rh is the position information of the road preview observed from the actual reference trajectory at time k (unit: m), that is, the reference point position of the actual reference trajectory observed at time k at the sampling time; S nr0 , S nr1 ,…,S nrh is the road preview information measured from the auxiliary reference trajectory observed at time k (unit: m), where s nr0 The value of is 0. The schematic diagram of the above variables is as follows Figure 2 shown.

[0071] The speed error e between the vehicle and the reference trajectory at each moment v It can be expressed as:

[0072]

[0073] Among them, v r0 represents the reference velocity of the actual reference trajectory at time k.

[0074] According to formulas (5) and (6), the speed error between the vehicle and the road (actual reference trajectory) at each moment, that is, the second state equation can be expressed as:

[0075]

[0076] Where Y ev (k+i|k) represents the velocity error between the vehicle and the actual reference trajectory at time k+i.

[0077] It can be understood that in addition to the above-mentioned second state equation of preview acceleration and second state equation of preview position, it can also include a second state equation for the slope of the preview road, a second state equation for the speed of the preview actual reference trajectory, and a second state equation for the rate of change of the speed of the preview actual reference trajectory, etc.

[0078] In S102 , a third state equation for describing a vehicle-road preview error is determined based on the first state equation and the second state equation.

[0079] When the second state equation is a state equation based on the acceleration preview model, that is, the second state equation is the longitudinal acceleration preview model determined by formula (3), the second state equation (formula (3)) can be combined with the first state equation (formula 2) to obtain a new state space, that is, the third state equation is:

[0080]

[0081] Among them, zeros(E d The number of rows, h) represents the matrix positions filled with no numbers, that is, the zero matrix.

[0082] When the second state is a state equation based on the longitudinal position preview model, that is, when the second state equation is Formula 5, the (2+h+1)-dimensional state space model of the vehicle and the road can be obtained by combining the first state equation (Formula (4)), the position error equation (5), and the second state equation (Formula (7)):

[0083]

[0084] in, a(k) represents the acceleration at time k.

[0085] In S103 , a feedback gain is determined according to a deviation between the vehicle state information and an actual reference trajectory.

[0086] When the third state equation is formula (8), the cost function of the optimal control method determined based on formula (8) is:

[0087]

[0088] Among them, Q is the weight matrix of the state quantity, and R is the weight of the acceleration.

[0089] When the third state equation is formula (9), according to the output variable Y of the state space model shown in formula (9), the cost function of the optimal control problem is constructed as follows:

[0090]

[0091] Q is the weight matrix of the state quantity, and R is the weight of the acceleration.

[0092] In order to solve the minimum value of cost J in formula (10) and formula (11), we can first use the Riccati equation to find the matrix P:

[0093] P=A T PA-A T PB(R+B T PB) -1 B T PA+C T QC

[0094] in,

[0095] The feedback gain K is calculated based on the P matrix obtained by solving the Riccati equation. K is the (4+h+1)-dimensional optimal control gain vector and can be expressed as:

[0096] K=(R+B T PB) -1 B T PA (12)

[0098] In S104 , a preview control instruction of the vehicle is determined according to the feedback gain and the third state equation.

[0099] The longitudinal acceleration command can be expressed as: U=-K*X(k), where K is the feedback gain and X(k) varies according to the determination method of the second state equation.

[0100] This application combines the first state equation of the vehicle-road error model without preview and the road (actual reference trajectory) preview model (including a preview model based on longitudinal position or a preview model based on longitudinal acceleration) to construct a state space model that can describe the vehicle-road preview error, namely the third state equation, and constructs an optimal control problem that minimizes the vehicle-road preview error based on the third state equation, determines the cost function corresponding to the third state equation, and determines the control parameters corresponding to the acceleration command based on the cost function. That is, in addition to considering the difference between the current vehicle position and the longitudinal position of the reference point, and the difference between the current vehicle's longitudinal speed and the longitudinal speed of the reference point in the actual reference trajectory, this application also considers changes in the position, speed and other states within a period of time in the future, so that the vehicle can be more effectively prepared for changes in the actual reference trajectory in advance, thereby reducing the sense of frustration and enhancing ride comfort.

[0101] Furthermore, this application discretizes the continuous, no-preview vehicle-road error model. The discretized first state equation is then combined with the road preview model to establish a third state equation that describes the vehicle-road preview error. Based on this third state equation, a linear quadratic optimal control problem is formulated for the preview error and control input. This discrete computation reduces system resource usage and improves computational response efficiency.

[0102] In a possible implementation, the present application can also be applied to lateral preview scenarios based on the third state equation determined by the first state equation and the second state equation to control the vehicle's steering, respond in advance based on the actual reference trajectory, avoid sharp turns, and increase vehicle comfort during driving. The specific implementation steps are as follows:

[0103] To facilitate the calculation of the deviation between the vehicle's predicted trajectory and the desired path, it is necessary to describe the vehicle's motion and the desired path in a unified coordinate system. To this end, an inertial coordinate system fixed to the road surface at the current moment is selected to mathematically model the vehicle and the desired path.

[0104] like Figure 3 The figure shows a schematic diagram of a two-degree-of-freedom mechanical model of a vehicle provided by an embodiment of the present application. As shown in the figure, it is assumed that the desired path is an arc of a fixed radius, and the vehicle tracks the actual reference trajectory (desired path) at a constant speed. In order to facilitate the study of the path tracking problem, the following can be used: Figure 3 The lateral error (or lateral error) between the vehicle and the desired path is shown as follows: y and heading angle error e ψ To describe the deviation between the vehicle and the road at the current moment. y , lateral error change rate and heading angle error e ψ， Heading angle error change rate As the state quantity, the vehicle state equation with the deviation between the vehicle posture and the desired path as the state quantity is:

[0105]

[0106] Formula (13) is based on the current lateral error e y and heading angle error e ψ The state-space model represents the current deviation between the vehicle's posture and the desired path. This model allows for the construction of a path-following LQR controller, whose control objective is to minimize the weighted sum of the current vehicle-road deviation and steering control. To achieve optimal preview control for path tracking, the lateral profile of the desired path, i.e., the lateral direction of the vehicle, can be modeled.

[0107] To facilitate modeling, a discretized model can be used to model the lateral profile of the desired path. This assumes that the vehicle, through perception, V2X, or planning modules, can preview the lateral profile of the desired path ahead (i.e., lateral information in front of the vehicle). Specifically, this can be the lateral offset of the desired path relative to the X-axis in the inertial coordinate system.

[0108] like Figure 4 As shown in the schematic diagram of the road profile model, let the discrete sampling time interval of the preview information be T, the corresponding sampling distance interval be uT (u is the vehicle speed, assumed to be constant), the preview time be hT, O be the origin of the road surface inertial coordinate system, the X-axis be fixed on the ground, and the model of the expected path be constructed in a manner similar to a shift register. Then, the (h+1)-dimensional discrete state equation of the expected path lateral profile model can be established as:

[0109] y r (k+1)=Dy r (k)+Ey ri (k) (14)

[0110] Where D is the state matrix of the desired path lateral profile model, E is the input matrix of the model, and y ri is the input variable of the model, namely the new road side profile information observed at the farthest end of the preview.

[0111] In order to construct the optimal preview control problem for path tracking, a pre-established vehicle model with the current deviation between the vehicle posture and the desired path as the state quantity can be combined with the desired path lateral profile model to construct a state space model that can describe the vehicle's predicted trajectory and the deviation from the desired path. Therefore, the present application proposes a method for constructing an auxiliary reference trajectory to realize the use of the current error state quantity between the vehicle and the actual reference trajectory and the desired path lateral profile state quantity to represent the vehicle-road preview error (the deviation between the vehicle's predicted trajectory and the desired path), thereby establishing a state space model that can describe the vehicle-road preview error. Based on this model, the optimal control problem is then established and the analytical form of the optimal control rate is obtained.

[0112] When constructing the optimal preview control, the continuous state equation of the vehicle model can be discretized according to the sampling time T, and the following can be obtained:

[0113]

[0114] Among them, A cd 、B cd and B rd A in the continuous state equation of the vehicle model c 、B c and B r The matrix after discretization.

[0115] In actual engineering, the road profile information can be obtained through vehicle-mounted observation equipment. r It can be measured based on the vehicle coordinate system. Figure 5 The schematic diagram of the vehicle-road preview error model shown in the figure can be used to establish a road lateral profile model in the inertial coordinate system X1O1Y1 that coincides with the vehicle coordinate system. Combining this road lateral profile model with the discrete vehicle model, we can obtain:

[0116]

[0117] Construct the output equation of the state space and make the output vector represent the preview error of the vehicle-road:

[0118] From the vehicle dynamics model with the current deviation as the state shown in formula (13), it can be seen that the current deviation state contains the lateral motion information of the vehicle, so the current deviation state can be used to represent the predicted trajectory of the vehicle. To this end, this application proposes a method for constructing an auxiliary reference trajectory. The auxiliary reference trajectory is a straight line, whose origin is the reference point of the desired path at the current moment, and whose heading angle is the heading angle of the desired path at the current moment, as shown in Figure 1. Figure 5 O in n X n Assume that the path to be tracked by the vehicle model shown in formula (15) is the auxiliary reference trajectory, that is, e y and eψ represents the error between the vehicle and the auxiliary reference trajectory, then the lateral error between the vehicle and the desired path at each moment can be expressed as:

[0119] Y y (k+m|k)=e y (k+m|k)-y nrm ,m=0,1,2,...,h (17)

[0120] Where (k+m|k) represents the state quantity at time k+m predicted and calculated based on the state quantity at time k; Y y (k+m|k) is the lateral error between the vehicle and the desired path at time k+m (m); e y (k+m|k) is the lateral error between the vehicle and the auxiliary reference trajectory at time k+m (m); y r0 ,y r1 ,…,y rh is the lateral profile information of the expected path observed at time k (m), which is observed in the road inertial coordinate system X1O1Y1; nr0 ,y nr1 ,…,y nrh is the modified expected path lateral profile information (m), which is based on the auxiliary reference trajectory. des is the heading angle of the auxiliary reference trajectory (rad), that is, the heading angle of the desired path at the current moment. Assuming that the observation sampling time interval T of the desired path is very small, the tangent value of the heading angle of the desired path at each moment can be expressed as:

[0121] The heading angle error e between the vehicle and the auxiliary reference trajectory at each moment ψ It can be expressed as:

[0122] e ψ (k+m|k)=ψ(k+m|k)-ψ des ,m=0,1,2,...,h (19)

[0123] Where ψ(k+m|k) is the heading angle of the vehicle at time k+m (rad); since the auxiliary reference trajectory is a straight line, its heading angle ψ des is a constant; e ψ (k+m|k) is the heading angle error (rad) between the vehicle and the auxiliary reference trajectory at time k+m. From equations (17), (18), and (19), it can be seen that the heading angle error between the vehicle and the desired path at each moment can be expressed as the augmented state quantity y nr0 ,y nr1 ,…,y nrh Related expressions. The same applies to the lateral error change rate and the heading error change rate.

[0124] Taking the vehicle-road preview lateral error and preview heading angle error as the output variables of the state space, the (4+h+1)-dimensional state space model of the vehicle-road can be established as follows:

[0125]

[0126] The expression of Y is the output equation, and the dimension of C is a×(4+h+1). a<(4+h+1), and the specific value can be set according to the state quantity of interest. Based on the output variable Y of the state space model shown in Equation (20), the cost function of the optimal control problem is constructed as:

[0127]

[0128] Where Q is the weight matrix of the vehicle-road error, which is a semi-positive matrix; R is the weight matrix of the control input, which is a positive definite matrix. Since there is only a single input of the front wheel angle, R can be degenerated into a positive real number.

[0129] The optimal control cost function J constructed in this application can be a linear quadratic optimal control problem (LQR), and the analytical form of the optimal solution can be obtained through LQR related theories.

[0130] For the optimal control cost function J constructed by formula (21), its optimal steering control δ * (steering angle change rate) can be expressed as:

[0131]

[0132] Among them, K is the (4+h+1)-dimensional optimal control gain vector, and the first 4 dimensions of K are b is the feedback gain vector of the error state between the vehicle and the desired path at the current moment, and the h+1 dimension K p is the preview gain vector of the modified desired path lateral profile state. The feedback gain K can be obtained using the Riccati equation.

[0133] This application uses the error between the vehicle and the desired path (actual reference trajectory) as the basic state quantity for path tracking calculation, which can effectively improve control performance. Furthermore, it can make changes in advance based on future changes in the actual reference trajectory, effectively avoiding sharp turns and helping to enhance vehicle driving comfort. Furthermore, compared to continuous preview calculations, this application uses discrete calculations to reduce computing resources and improve control response efficiency.

[0134] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0135] Figure 6 A schematic diagram of a preview control device for a car provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the device includes:

[0136] a state equation acquisition unit 301 for acquiring a first state equation of the vehicle without preview, and acquiring a second state equation of the vehicle with preview, wherein the second state equation includes state information of an actual reference trajectory within a predetermined time period after a current time;

[0137] a state equation combining unit 302, configured to combine the first state equation and the second state equation to obtain a third state equation for describing a vehicle-road preview error;

[0138] A cost function determining unit 303 is configured to construct a cost function for minimizing a vehicle-road preview error based on the third state equation;

[0139] The acceleration determination unit 304 is configured to determine an acceleration instruction for the vehicle according to the cost function.

[0140] Figure 3 The preview control device of the car shown is Figure 1 The car preview control method shown corresponds to this.

[0141] Figure 4 Schematic diagram of a vehicle provided in one embodiment of the present application. Figure 4 As shown, vehicle 4 in this embodiment includes a processor 40, a memory 41, and a computer program 42 stored in memory 41 and executable on processor 40, such as a vehicle preview control program. When processor 40 executes computer program 42, it implements the steps of each of the aforementioned vehicle preview control method embodiments. Alternatively, when processor 40 executes computer program 42, it implements the functions of each module / unit in each of the aforementioned device embodiments.

[0142] For example, the computer program 42 may be divided into one or more modules / units, which are stored in the memory 41 and executed by the processor 40 to implement the present application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 42 in the vehicle 4.

[0143] The vehicle may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will appreciate that Figure 4It is only an example of vehicle 4 and does not constitute a limitation of vehicle 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the vehicle may also include input and output devices, network access devices, buses, etc.

[0144] The processor 40 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0145] The memory 41 may be an internal storage unit of the vehicle 4, such as a hard drive or memory of the vehicle 4. The memory 41 may also be an external storage device of the vehicle 4, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the vehicle 4. Furthermore, the memory 41 may include both an internal storage unit of the vehicle 4 and an external storage device. The memory 41 is used to store the computer program and other programs and data required by the vehicle. The memory 41 may also be used to temporarily store data that has been output or is about to be output.

[0146] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0147] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0148] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0149] In the embodiments provided in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0150] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0151] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0152] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, which can also be completed by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0153] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A preview control method for an automobile, characterized in that: The method comprises: Determining a first state equation between the vehicle's posture without preview and the actual reference trajectory, and determining a second state equation between the vehicle's state information and the actual reference trajectory with preview; Determining a third state equation for describing a vehicle-road preview error based on the first state equation and the second state equation; determining a feedback gain according to a deviation between the vehicle state information and an actual reference trajectory; determining a preview control instruction of the vehicle according to the feedback gain and the third state equation; The preview control includes longitudinal preview control and lateral preview control. During the longitudinal preview control, determining a second state equation between the vehicle state information and the actual reference trajectory when previewing is performed includes: Determining a first auxiliary reference trajectory according to the actual reference trajectory and the state information of the vehicle; Determine a longitudinal position error equation between the vehicle position and the actual reference trajectory in the second state equation according to the first auxiliary reference trajectory; Determine a speed error equation between the vehicle speed in the second state equation and the actual reference trajectory according to the first auxiliary reference trajectory; Determining a feedback gain according to a deviation between the vehicle state information and an actual reference trajectory includes: Determine the matrix P through the Riccati equation; Determine the feedback gain according to the matrix P; During the lateral preview control, determining a second state equation between the vehicle state information and the actual reference trajectory during preview includes: Determine a lateral distance error equation between the position of the vehicle in the second state equation when previewing and the actual reference trajectory; Determine a heading angle error equation between the vehicle's driving direction and the actual reference trajectory in the second state equation when previewing; Determining a third state equation for describing a vehicle-road preview error based on the first state equation and the second state equation includes: The lateral distance error and the heading angle error are used as output variables in the state space to obtain a third state equation for describing the vehicle-road preview error.

2. The method according to claim 1, characterized in that Determining a preview control instruction of the vehicle according to the feedback gain and the third state equation includes: A longitudinal acceleration control command of the vehicle is determined according to the feedback gain and the third state equation.

3. A preview control device for an automobile, characterized in that: The device comprises: a first state equation determination unit, configured to determine a first state equation between the vehicle's posture without preview and the actual reference trajectory, and to determine a second state equation between the vehicle's state information and the actual reference trajectory when preview is present; the preview control includes longitudinal preview control and lateral preview control; during the longitudinal preview control, the unit is configured to determine a first auxiliary reference trajectory based on the actual reference trajectory and the vehicle's state information; determine a longitudinal position error equation between the vehicle's position in the second state equation and the actual reference trajectory based on the first auxiliary reference trajectory; determine a speed error equation between the vehicle's speed in the second state equation and the actual reference trajectory based on the first auxiliary reference trajectory; during the lateral preview control, the unit is configured to determine a lateral distance error equation between the vehicle's position in the second state equation when preview is present and the actual reference trajectory; and determine a heading angle error equation between the vehicle's driving direction in the second state equation when preview is present and the actual reference trajectory; a third state equation determining unit, configured to determine a third state equation for describing a vehicle-road preview error based on the first state equation and the second state equation, and to use the lateral range error and the heading angle error as output variables of a state space during the lateral preview control to obtain the third state equation for describing the vehicle-road preview error; a feedback gain determination unit, configured to determine a feedback gain based on a deviation between the vehicle state information and an actual reference trajectory, and, during the longitudinal preview control, to determine a matrix P using the Riccati equation; and to determine the feedback gain based on the matrix P; A preview control unit is used to determine a preview control instruction of the vehicle according to the feedback gain and the third state equation.

4. A vehicle comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 2 are implemented.

5. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 2 are implemented.

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