Vehicle control method and device, storage medium, electronic equipment and vehicle

Abstract

The invention relates to a vehicle control method and device, a storage medium, electronic equipment and a vehicle. The control precision and stability of the vehicle are improved. The method comprises the steps that pose information of a vehicle at a target moment and pose information corresponding to each prediction point are determined to serve as first pose information, and the prediction points are determined from prediction paths corresponding to the vehicle; on a reference driving path corresponding to the vehicle, multiple reference points are determined, pose information corresponding to each reference point is determined to serve as second pose information, and the multiple reference points comprise a first reference point corresponding to the target moment and a preset number of second reference points after the target moment; each reference point has a corresponding prediction point on the prediction path; constructing an LQR optimization model according to the first pose information and the second pose information; according to the LQR optimization model, determining a target control parameter of the next moment of the target moment; and at the next moment of the target moment, the vehicle is controlled according to the target control parameters.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle control, and in particular, to a vehicle control method, device, storage medium, electronic equipment and vehicle. Background Art

[0002] In intelligent driving scenarios, in order to prevent the vehicle from deviating from the lane or colliding with obstacles, the intelligent driving control system generally has high requirements for the lateral control of the vehicle. The commonly used control methods are PID (Proportion Integral Differential) control, pure tracking control, LQR (Linear quadratic regulator) control, and MPC (Model Prediction Control). Among them, PID control and pure tracking control have low accuracy, and MPC consumes more computing power, so LQR is more commonly used. However, there is control instability in the related LQR-based control scheme. Summary of the invention

[0003] The purpose of the present disclosure is to provide a vehicle control method, device, storage medium, electronic device and vehicle to improve the control accuracy and stability of the vehicle.

[0004] In order to achieve the above objective, according to a first aspect of the present disclosure, a vehicle control method is provided, the method comprising:

[0005] Determine the posture information of the vehicle at the target time and the posture information corresponding to each of the plurality of prediction points as the first posture information, wherein the prediction points are determined from the prediction path corresponding to the vehicle;

[0006] Determine a plurality of reference points on a reference driving path corresponding to the vehicle, and determine the posture information corresponding to each reference point as the second posture information, wherein the plurality of reference points include a first reference point corresponding to the target moment and a preset number of second reference points after the target moment, and each of the reference points has a corresponding prediction point on the prediction path;

[0007] Constructing an LQR optimization model according to the first posture information and the second posture information;

[0008] Determining the target control parameter at the next moment of the target moment according to the LQR optimization model;

[0009] At a moment next to the target moment, the vehicle is controlled according to the target control parameter.

[0010] Optionally, the position information of the vehicle at the target time includes the coordinates of the vehicle at the target time;

[0011] Determining a plurality of reference points on the reference driving path of the vehicle includes:

[0012] Determine a projection point of the coordinates of the vehicle at the target time on the reference driving path as the first reference point;

[0013] Taking the position of the first reference point as the starting position and the preset distance as the spacing between adjacent reference points, the preset number of second reference points are determined one by one on the reference driving path.

[0014] Optionally, the reference driving path corresponds to a path equation, and the posture information includes coordinates and heading angles;

[0015] The determining of the posture information corresponding to each reference point includes:

[0016] Determine the coordinates corresponding to each of the reference points according to the reference driving path;

[0017] For each of the reference points, the heading angle corresponding to the reference point is determined according to the coordinates corresponding to the reference point and the path equation.

[0018] Optionally, the heading angle θ corresponding to the i-th reference point Ri is determined by the following formula: Ri :

[0019] θ Ri =arctan(f′(x Ri ))

[0020] Wherein, 0≤i≤preset number, R0 is the first reference point, f(x Ri ) is the path equation, f′(x Ri ) is the derivative of the path equation.

[0021] Optionally, the LQR optimization model includes an objective cost function;

[0022] The target cost function is determined by:

[0023] Get the pose cost coefficient and control cost coefficient;

[0024] Generate a first cost function according to the first pose information, the second pose information and the pose cost coefficient;

[0025] Generate a second cost function according to the preset control parameters and the control cost coefficient;

[0026] The sum of the first cost function and the second cost function is determined as the target cost function.

[0027] Optionally, the target cost function F is generated according to the following formula:

[0028]

[0029] in, is the first cost function, is the second cost function; i is the predicted pose information for the i-th prediction point, X Ri is the second pose information of the i-th reference point, X R0 is the second posture information of the first reference point, N is the preset number, Q is the diagonal matrix generated according to the posture cost coefficient; r is the control cost coefficient, κ i is the control parameter of the i-th prediction point.

[0030] Optionally, the posture information includes coordinates and heading angles, and the posture cost coefficient includes a position cost coefficient and a heading cost coefficient;

[0031] Q is generated according to the following formula:

[0032] Q = diag[q xy ,q xy ,q θ ]

[0033] Among them, q xy is the position cost coefficient, q θ is the heading cost coefficient.

[0034] Optionally, the LQR optimization model also includes a target constraint condition, which includes a first constraint condition for constraining a generation method of the predicted path and a second constraint condition for constraining X0 to be the first posture information.

[0035] Optionally, the posture information includes coordinates and heading angles;

[0036] The first constraint condition is generated in the following way:

[0037] Construct a kinematic model with posture information as state quantity and preset control parameters as control quantity;

[0038] According to a preset distance, the kinematic model is discretized to obtain a system equation for generating the posture information of a second prediction point according to the posture information of the first prediction point, wherein the second prediction point is a next prediction point separated from the first prediction point by a preset distance;

[0039] The system equation is linearized to obtain a processed target equation as the first constraint condition.

[0040] Optionally, the system equation is:

[0041]

[0042] Among them, X i is the predicted pose information for the i-th prediction point, Δs is the preset distance, θ i is the heading angle corresponding to the i-th predicted point.

[0043] Optionally, the linearization process is Taylor expansion;

[0044] The objective equation is:

[0045] X i+1 =A i X i +B i κ i +C i

[0046] Among them, A i , B i , C i Determined by:

[0047]

[0048]

[0049]

[0050] Among them, θ Ri is the heading angle corresponding to the i-th reference point.

[0051] Optionally, determining the target control parameter at the next moment of the target moment according to the LQR optimization model includes:

[0052] Determine a feedback gain matrix that can minimize the target cost function while satisfying the target constraint condition;

[0053] Determine a target control parameter at a next moment of the target moment according to the first posture information and the feedback gain matrix.

[0054] Optionally, the determining of a feedback gain matrix that enables the target cost function to obtain a minimum value when the target constraint condition is satisfied includes:

[0055] The objective cost function is solved by using a dynamic programming solution to obtain the feedback gain matrix.

[0056] Optionally, determining the target control parameter at the next moment of the target moment according to the first posture information and the feedback gain matrix includes:

[0057] The target control parameter κ is determined according to the following formula:

[0058] κ=-K i X i -L i

[0059] Among them, K i , L i is the feedback gain matrix, and:

[0060]

[0061]

[0062]

[0063]

[0064] q i =-QX Ri

[0065] Among them, P N =Q, F N =q N .

[0066] Optionally, the target control parameter is steering curvature;

[0067] The controlling the vehicle according to the target control parameter includes:

[0068] determining a front wheel steering angle corresponding to the steering curvature;

[0069] The vehicle is controlled according to the front wheel steering angle.

[0070] According to a second aspect of the present disclosure, there is provided a vehicle control device, the device comprising:

[0071] A first determination module is used to determine the posture information of the vehicle at the target time and the posture information corresponding to each of the plurality of prediction points as the first posture information, wherein the prediction points are determined from the prediction path corresponding to the vehicle;

[0072] A second determination module is used to determine a plurality of reference points on a reference driving path corresponding to the vehicle, and determine the posture information corresponding to each reference point as the second posture information, wherein the plurality of reference points include a first reference point corresponding to the target moment and a preset number of second reference points after the target moment, and each of the reference points has a corresponding prediction point on the predicted path;

[0073] A construction module, configured to construct an LQR optimization model according to the first posture information and the second posture information;

[0074] A third determination module, used to determine the target control parameter at the next moment of the target moment according to the LQR optimization model;

[0075] A control module is used to control the vehicle according to the target control parameter at a moment next to the target moment.

[0076] According to a third aspect of the present disclosure, a non-temporary computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the steps of the vehicle control method described in the first aspect of the present disclosure are implemented.

[0077] According to a fourth aspect of the present disclosure, there is provided an electronic device, including:

[0078] a memory having a computer program stored thereon;

[0079] A processor is used to execute the computer program in the memory to implement the steps of the vehicle control method described in the first aspect of the present disclosure.

[0080] According to a fifth aspect of the present disclosure, a vehicle is provided, comprising the electronic device described in the fourth aspect of the present disclosure.

[0081] Through the above technical solution, the posture information of the vehicle at the target time and the posture information corresponding to each of the multiple predicted points on the predicted path are determined as the first posture information, and multiple reference points and their respective second posture information are determined on the reference driving path corresponding to the vehicle. According to the first posture information and the second posture information, an LQR optimization model is constructed to determine the target control parameters for the next moment of the target time, and then the vehicle is controlled according to the target control parameters at the next moment of the target time. Among them, the reference points determined from the reference driving path include both the first reference point corresponding to the target time and the preset number of second reference points after the target time, and these reference points have corresponding predicted points on the predicted path. Then, the LQR optimization model is constructed using these predicted points and reference points, which is equivalent to adding a preview mechanism to the LQR algorithm. When determining the target control parameters, both the current path tracking error and the path tracking error in the future are considered. Thus, better control parameters can be obtained to avoid sharp turns and ensure the driving stability of the vehicle.

[0082] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0084] Figure 1 is a flow chart of a vehicle control method provided according to an embodiment of the present disclosure;

[0085] Figure 2 is an exemplary schematic diagram of a reference driving path and a predicted path in the vehicle control method provided by the present disclosure;

[0086] Figure 3 is a block diagram of a vehicle control device provided according to an embodiment of the present disclosure;

[0087] Figure 4 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0088] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0089] Figure 1 FIG. 1 is a flow chart of a vehicle control method provided according to an embodiment of the present disclosure. Figure 1As shown, the method provided by the present disclosure may include steps 11 to 15.

[0090] In step 11, the posture information of the vehicle at the target time and the posture information corresponding to each of the multiple prediction points are determined as the first posture information.

[0091] For example, the target time may be the current time, and accordingly, the first position information is the actual position information of the vehicle at the current time. For another example, the target time may be a time after the current time.

[0092] The prediction point can be determined from the predicted path corresponding to the vehicle. The predicted path corresponding to the vehicle usually includes multiple prediction points, which can be prediction points generated by predicting the path of the vehicle for a series of moments after the target moment. Therefore, the first position information includes the position information of the vehicle at the target moment and a series of moments after the target moment.

[0093] Optionally, the pose information may include coordinates and heading angles. For example, in a two-dimensional rectangular coordinate system constructed by an x-axis and a y-axis, the coordinates in the pose information include an x-axis coordinate and a y-axis coordinate.

[0094] In step 12, a plurality of reference points are determined on a reference driving path corresponding to the vehicle, and the posture information corresponding to each reference point is determined as the second posture information.

[0095] The reference driving path may be directly obtained from other modules (eg, an upstream path planning module, etc.). For example, the reference driving path may be represented by a curve equation.

[0096] The multiple reference points include a first reference point corresponding to the target moment and a preset number of second reference points after the target moment. In fact, the second reference point is a number of future reference points, which are equivalent to preview points in the present disclosure. Moreover, each reference point has a corresponding prediction point on the prediction path.

[0097] In a possible implementation, in step 12, determining a plurality of reference points on a reference driving path of the vehicle may include the following steps:

[0098] Determine the projection point of the coordinates of the vehicle at the target time on the reference driving path as the first reference point;

[0099] Taking the position of the first reference point as the starting position and the preset distance as the spacing between adjacent reference points, a preset number of second reference points are determined one by one on the reference driving path.

[0100] Among them, the preset distance is used as the spacing between adjacent reference points, and these spacings can be equal to each other, or different from each other, or partially the same and partially different, and the present disclosure does not make strict limitations on this.

[0101] As described above, the position information may include coordinates, and thus, the position information of the vehicle at the target time includes the coordinates of the vehicle at the target time.

[0102] By determining the projection point of the coordinates of the vehicle at the target time on the reference driving path, a reference point corresponding to the target time on the reference driving path, that is, the first reference point, can be located. The above-mentioned projection point can be determined by a conventional method of determining the projection point of a point to a curve, that is, making the line connecting the point and the projection point perpendicular to the tangent line of the projection point on the curve.

[0103] After the first reference point is determined, other reference points on the reference driving path, i.e., second reference points, may be determined one by one starting from the first reference point with a preset distance as the interval between adjacent reference points. For example, the reference driving path may be as follows: Figure 2 As shown in M0, M1 is the first reference point. The next reference point M2 (second reference point) of M1 can be determined on the reference driving path with a preset distance as the interval, and then the next reference point M2 of M2 can be determined on the reference driving path with a preset distance as the interval, until the number of second reference points reaches the preset number.

[0104] After determining the above-mentioned multiple reference points, the posture information of each reference point can be determined as the second posture information. As described above, the posture information may include coordinates and heading angles. At the same time, the reference driving path may correspond to a path equation, for example, the above-mentioned curve equation. Based on this, determining the posture information corresponding to each reference point may include the following steps:

[0105] According to the reference driving path, determine the coordinates corresponding to each reference point;

[0106] For each reference point, the heading angle corresponding to the reference point is determined according to the coordinates and path equation corresponding to the reference point.

[0107] After the reference points on the reference driving path are determined in the above manner, the coordinates of each reference point can be directly obtained. Then, the heading angle of each reference point is determined respectively.

[0108] For example, the heading angle θ corresponding to the i-th reference point Ri can be determined by the following formula: Ri :

[0109] θ Ri =arctan(f′(x Ri ))

[0110] Where 0≤i≤preset number, R0 is the first reference point, f(x Ri ) is the path equation, f′(x Ri ) is the derivative of the path equation.

[0111] Back to Figure 1 ,In step 13, an LQR optimization model is constructed based on the first pose information and the second pose information.

[0112] Among them, the LQR optimization model may include a target cost function, and the purpose of the present disclosure is to determine the control parameters that can minimize the target cost function. Usually, the target cost function is accompanied by constraints, so the purpose of the present disclosure is actually to determine the control parameters that can minimize the target cost function while satisfying the constraints (the present disclosure is the target constraints).

[0113] By way of example, the control parameter may be the steering curvature.

[0114] Optionally, the target cost function can be determined by:

[0115] Get the pose cost coefficient and control cost coefficient;

[0116] Generate a first cost function according to the first pose information, the second pose information and the pose cost coefficient;

[0117] Generate a second cost function according to preset control parameters and control cost coefficients;

[0118] The sum of the first cost function and the second cost function is determined as the target cost function.

[0119] Among them, the posture cost coefficient and the control cost coefficient can be set according to the needs of the actual scene. If the posture information includes coordinates and heading angles, the posture cost coefficient may include a position cost coefficient and a heading cost coefficient, and the sum of the position cost coefficient and the heading cost coefficient maintains a stable value, that is, if the position cost coefficient is increased, the heading cost coefficient is correspondingly reduced.

[0120] By setting the cost coefficient, the control of the vehicle can be balanced between accuracy and stability. For example, if a higher heading cost coefficient is set, the vehicle's heading will be more accurate during the control process, and accordingly, the position deviation will be slightly larger. For another example, if a higher control cost coefficient is set, the steering control of the vehicle will be smoother during the control process, and accordingly, the accuracy will be reduced.

[0121] Optionally, the target cost function F may be generated according to the following formula:

[0122]

[0123] in, is the first cost function, is the second cost function; X i is the predicted pose information for the i-th prediction point, X Ri is the second pose information of the i-th reference point, X R0 is the second pose information of the first reference point, N is the preset number, Q is the diagonal matrix generated according to the pose cost coefficient; r is the control cost coefficient, κ i is the control parameter of the i-th prediction point.

[0124] As mentioned above, a vehicle may correspond to a predicted path including a predicted point. The predicted path may be Figure 2 As shown in E0, each reference point has a corresponding prediction point on the prediction path, for example, reference point M2 corresponds to prediction point E2, and reference point M4 corresponds to prediction point E4.

[0125] For example, Q can be generated according to the following formula:

[0126] Q = diag[q xy ,q xy ,q θ ]

[0127] Among them, q xy is the position cost coefficient, q θ is the heading cost coefficient.

[0128] Optionally, the LQR optimization model may further include a target constraint condition, which may include a first constraint condition for constraining a generation method of the predicted path and a second constraint condition for constraining X0 to be the first pose information.

[0129] Optionally, the first constraint condition can be generated in the following way:

[0130] Construct a kinematic model with posture information as state quantity and preset control parameters as control quantity;

[0131] According to the preset distance, the kinematic model is discretized to obtain a system equation for generating the posture information of the second prediction point according to the posture information of the first prediction point, where the second prediction point is the next prediction point separated from the first prediction point by the preset distance;

[0132] The system equations are linearized to obtain the processed target equation as the first constraint.

[0133] For example, the kinematic model can be constructed as follows:

[0134]

[0135] Among them, x and y are coordinates, θ is the heading angle, and κ is the preset control parameter.

[0136] After the kinematic model is constructed, the kinematic model is discretized according to a preset distance to obtain a system equation. Optionally, the derivation of the discretization process may use a trapezoidal integral formula.

[0137] For example, the system equation after discretization can be:

[0138]

[0139] Among them, X i is the predicted pose information for the i-th prediction point, Δs is the preset distance, θ i is the heading angle corresponding to the i-th predicted point.

[0140] In the present disclosure, the above-mentioned system equations of LQR are derived using a kinematic model, which can improve the lateral control accuracy and reduce the tracking error of the reference driving path. Moreover, compared with the lateral stiffness of the front and rear tires, the vehicle mass, and the yaw moment of inertia about the center of mass required for the construction of the dynamic model, which are difficult to obtain accurately and are easily affected by the working conditions, the parameters required by the kinematic model do not change with the working conditions. Therefore, it is easier to maintain uniformity in subsequent control and improve control accuracy. In addition, the kinematic model can also describe large steering conditions and has a wider range of applications.

[0141] At the same time, the system equations are discretized using a preset distance so that the system equations obtained after discretization are independent of the vehicle speed, which is conducive to achieving control decoupling of the vehicle's lateral and longitudinal directions, and making the path tracking effect in the low-speed range (e.g., 0-30km / h) not affected by the vehicle speed. Among them, the discretization using the trapezoidal integral formula is also conducive to increasing control accuracy.

[0142] After obtaining the above system equation, linearization can be performed through Taylor expansion. For example, the target equation after Taylor expansion can be:

[0143] X i+1 =A i X i +B i κ i +C i

[0144] Among them, A i , B i , C i Determined by:

[0145]

[0146]

[0147]

[0148] Among them, θ Ri is the heading angle corresponding to the i-th reference point.

[0149] In step 14, the target control parameters at the next moment of the target moment are determined according to the LQR optimization model.

[0150] In a possible implementation, step 14 may include the following steps:

[0151] Determine a feedback gain matrix that can minimize the target cost function while satisfying the target constraint conditions;

[0152] According to the first pose information and the feedback gain matrix, the target control parameters at the next target moment are determined.

[0153] Optionally, a dynamic programming solution method may be used to solve the target cost function to obtain a feedback gain matrix.

[0154] And, the target control parameter κ can be determined according to the following formula:

[0155] κ=-K i X i -L i

[0156] Among them, K i , L i is the feedback gain matrix, and:

[0157]

[0158]

[0159]

[0160]

[0161] q i =-QX Ri

[0162] Among them, P N =Q, F N =q N .

[0163] In step 15, at a moment next to the target moment, the vehicle is controlled according to the target control parameter.

[0164] In a possible implementation manner, if the target control parameter is the turning curvature, step 15 may include the following steps:

[0165] determining a front wheel steering angle corresponding to the steering curvature;

[0166] The vehicle is controlled according to the front wheel steering angle.

[0167] For example, the front wheel steering angle δ may be determined according to the following formula:

[0168] δ=arctan(κL)

[0169] Where κ is the turning curvature and L is the wheelbase of the vehicle.

[0170] Through the above technical solution, the posture information of the vehicle at the target time and the posture information corresponding to each of the multiple predicted points on the predicted path are determined as the first posture information, and multiple reference points and their respective second posture information are determined on the reference driving path corresponding to the vehicle. According to the first posture information and the second posture information, an LQR optimization model is constructed to determine the target control parameters for the next moment of the target time, and then the vehicle is controlled according to the target control parameters at the next moment of the target time. Among them, the reference points determined from the reference driving path include both the first reference point corresponding to the target time and the preset number of second reference points after the target time, and these reference points have corresponding predicted points on the predicted path. Then, the LQR optimization model is constructed using these predicted points and reference points, which is equivalent to adding a preview mechanism to the LQR algorithm. When determining the target control parameters, both the current path tracking error and the path tracking error in the future are considered. Thus, better control parameters can be obtained to avoid sharp turns and ensure the driving stability of the vehicle.

[0171] Figure 3 is a block diagram of a vehicle control device provided according to an embodiment of the present disclosure. Figure 3 As shown, the device 30 comprises:

[0172] A first determination module 31 is used to determine the posture information of the vehicle at the target time and the posture information corresponding to each of the plurality of prediction points as the first posture information, wherein the prediction points are determined from the prediction path corresponding to the vehicle;

[0173] A second determination module 32 is used to determine a plurality of reference points on a reference driving path corresponding to the vehicle, and determine the posture information corresponding to each reference point as the second posture information, wherein the plurality of reference points include a first reference point corresponding to the target moment and a preset number of second reference points after the target moment, and each of the reference points has a corresponding prediction point on the predicted path;

[0174] A construction module 33, configured to construct an LQR optimization model according to the first posture information and the second posture information;

[0175] A third determination module 34 is used to determine the target control parameter at the next moment of the target moment according to the LQR optimization model;

[0176] The control module 35 is used to control the vehicle according to the target control parameter at a moment next to the target moment.

[0177] Optionally, the position information of the vehicle at the target time includes the coordinates of the vehicle at the target time;

[0178] The second determining module 32 includes:

[0179] A first determination submodule, configured to determine a projection point of the coordinates of the vehicle at the target time on the reference driving path as the first reference point;

[0180] The second determination submodule is used to determine the preset number of second reference points one by one on the reference driving path, taking the position of the first reference point as the starting position and the preset distance as the spacing between adjacent reference points.

[0181] Optionally, the reference driving path corresponds to a path equation, and the posture information includes coordinates and heading angles;

[0182] The second determining module 32 includes:

[0183] A third determination submodule, configured to determine the coordinates corresponding to each of the reference points according to the reference driving path;

[0184] The fourth determination submodule is used to determine, for each of the reference points, a heading angle corresponding to the reference point according to the coordinates corresponding to the reference point and the path equation.

[0185] Optionally, the heading angle θ corresponding to the i-th reference point Ri is determined by the following formula: Ri :

[0186] θ Ri =arctan(f′(x Ri ))

[0187] Wherein, 0≤i≤preset number, R0 is the first reference point, f(x Ri ) is the path equation, f′(x Ri ) is the derivative of the path equation.

[0188] Optionally, the LQR optimization model includes an objective cost function;

[0189] The target cost function is determined by:

[0190] Get the pose cost coefficient and control cost coefficient;

[0191] Generate a first cost function according to the first pose information, the second pose information and the pose cost coefficient;

[0192] Generate a second cost function according to the preset control parameters and the control cost coefficient;

[0193] The sum of the first cost function and the second cost function is determined as the target cost function.

[0194] Optionally, the target cost function F is generated according to the following formula:

[0195]

[0196] in, is the first cost function, is the second cost function; i is the predicted pose information for the i-th prediction point, X Ri is the second pose information of the i-th reference point, X R0 is the second posture information of the first reference point, N is the preset number, Q is the diagonal matrix generated according to the posture cost coefficient; r is the control cost coefficient, κ i is the control parameter of the i-th prediction point.

[0197] Optionally, the posture information includes coordinates and heading angles, and the posture cost coefficient includes a position cost coefficient and a heading cost coefficient;

[0198] Q is generated according to the following formula:

[0199] Q = diag[q xy ,q xy ,q θ ]

[0200] Among them, q xy is the position cost coefficient, q θ is the heading cost coefficient.

[0201] Optionally, the LQR optimization model also includes a target constraint condition, which includes a first constraint condition for constraining a generation method of the predicted path and a second constraint condition for constraining X0 to be the first posture information.

[0202] Optionally, the posture information includes coordinates and heading angles;

[0203] The first constraint condition is generated in the following way:

[0204] Construct a kinematic model with posture information as state quantity and preset control parameters as control quantity;

[0205] According to a preset distance, the kinematic model is discretized to obtain a system equation for generating the posture information of a second prediction point according to the posture information of the first prediction point, wherein the second prediction point is a next prediction point separated from the first prediction point by a preset distance;

[0206] The system equation is linearized to obtain a processed target equation as the first constraint condition.

[0207] Optionally, the system equation is:

[0208]

[0209] Among them, X i is the predicted pose information for the i-th prediction point, Δs is the preset distance, θ i is the heading angle corresponding to the i-th predicted point.

[0210] Optionally, the linearization process is Taylor expansion;

[0211] The objective equation is:

[0212] X i+1 =A i X i +B i κ i +C i

[0213] Among them, A i , B i , C i Determined by:

[0214]

[0215]

[0216]

[0217] Among them, θ Ri is the heading angle corresponding to the i-th reference point.

[0218] Optionally, the third determining module 34 includes:

[0219] A fifth determination submodule, used to determine a feedback gain matrix that can minimize the target cost function when the target constraint condition is satisfied;

[0220] The sixth determination submodule is used to determine the target control parameters at the next moment of the target moment according to the first posture information and the feedback gain matrix.

[0221] Optionally, the fifth determination submodule is used to solve the target cost function by using a dynamic programming solution to obtain the feedback gain matrix.

[0222] Optionally, the sixth determination submodule is used to determine the target control parameter κ according to the following formula:

[0223] κ=-K i X i -L i

[0224] Among them, K i , L i is the feedback gain matrix, and:

[0225]

[0226]

[0227]

[0228]

[0229] q i =-QX Ri

[0230] Among them, P N =Q, F N =q N .

[0231] Optionally, the target control parameter is steering curvature;

[0232] The control module 35 comprises:

[0233] a seventh determination submodule, configured to determine a front wheel steering angle corresponding to the steering curvature;

[0234] A control submodule is used to control the vehicle according to the front wheel steering angle.

[0235] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0236] The present disclosure also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the vehicle control method provided in any embodiment of the present disclosure.

[0237] The present disclosure also provides an electronic device, comprising:

[0238] a memory having a computer program stored thereon;

[0239] A processor is used to execute the computer program in the memory to implement the steps of the vehicle control method provided in any embodiment of the present disclosure.

[0240] The present disclosure also provides a vehicle, comprising the electronic device provided by any embodiment of the present disclosure.

[0241] Figure 4 FIG. 7 is a block diagram of an electronic device 700 according to an exemplary embodiment. Figure 4 As shown, the electronic device 700 may include: a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input / output (I / O) interface 704, and a communication component 705.

[0242] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned vehicle control method. The memory 702 is used to store various types of data to support the operation of the electronic device 700, and these data may include, for example, instructions for any application or method used to operate on the electronic device 700, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 702 or sent through the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, and the other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 705 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0243] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned vehicle control method.

[0244] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned vehicle control method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 702 including program instructions, and the above-mentioned program instructions can be executed by the processor 701 of the electronic device 700 to complete the above-mentioned vehicle control method.

[0245] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program executable by a programmable device. The computer program has a code portion for executing the above-mentioned vehicle control method when executed by the programmable device.

[0246] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0247] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0248] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A vehicle control method, characterized in that: The method comprises: Determine the posture information of the vehicle at the target time and the posture information corresponding to each of the plurality of prediction points as the first posture information, wherein the prediction points are determined from the prediction path corresponding to the vehicle; Determine a plurality of reference points on a reference driving path corresponding to the vehicle, and determine the posture information corresponding to each reference point as the second posture information, wherein the plurality of reference points include a first reference point corresponding to the target moment and a preset number of second reference points after the target moment, and each of the reference points has a corresponding prediction point on the prediction path; Constructing an LQR optimization model according to the first posture information and the second posture information; Determining the target control parameter at the next moment of the target moment according to the LQR optimization model; At a moment next to the target moment, the vehicle is controlled according to the target control parameter.

2. The method according to claim 1, characterized in that The position information of the vehicle at the target time includes the coordinates of the vehicle at the target time; Determining a plurality of reference points on the reference driving path of the vehicle includes: Determine a projection point of the coordinates of the vehicle at the target time on the reference driving path as the first reference point; Taking the position of the first reference point as the starting position and the preset distance as the spacing between adjacent reference points, the preset number of second reference points are determined one by one on the reference driving path.

3. The method according to claim 1, characterized in that The reference driving path corresponds to a path equation, and the posture information includes coordinates and heading angles; The determining of the posture information corresponding to each reference point includes: Determine the coordinates corresponding to each of the reference points according to the reference driving path; For each of the reference points, the heading angle corresponding to the reference point is determined according to the coordinates corresponding to the reference point and the path equation.

4. The method according to claim 3, characterized in that Determine the heading angle θ corresponding to the i-th reference point Ri by the following formula Ri : θ Ri =arctan(f′(x Ri )) Wherein, 0≤i≤preset number, R0 is the first reference point, f(x Ri ) is the path equation, f′(x Ri ) is the derivative of the path equation.

5. The method according to claim 1, characterized in that The LQR optimization model includes an objective cost function; The target cost function is determined by: Get the pose cost coefficient and control cost coefficient; Generate a first cost function according to the first pose information, the second pose information and the pose cost coefficient; Generate a second cost function according to the preset control parameters and the control cost coefficient; The sum of the first cost function and the second cost function is determined as the target cost function.

6. The method according to claim 5, characterized in that The target cost function F is generated according to the following formula: in, is the first cost function, is the second cost function; i is the predicted pose information for the i-th prediction point, X Ri is the second pose information of the i-th reference point, X R0 is the second posture information of the first reference point, N is the preset number, Q is the diagonal matrix generated according to the posture cost coefficient; r is the control cost coefficient, κ i is the control parameter of the i-th prediction point.

7. The method according to claim 6, characterized in that The posture information includes coordinates and heading angles, and the posture cost coefficient includes a position cost coefficient and a heading cost coefficient; Q is generated according to the following formula: Q=diag[q xy ,q xy ,q θ ] Among them, q xy is the position cost coefficient, q θ is the heading cost coefficient.

8. The method according to claim 6, characterized in that The LQR optimization model also includes a target constraint condition, which includes a first constraint condition for constraining the generation method of the predicted path and a second constraint condition for constraining X0 to be the first posture information.

9. The method according to claim 8, characterized in that The posture information includes coordinates and heading angles; The first constraint condition is generated in the following way: Construct a kinematic model with posture information as state quantity and preset control parameters as control quantity; According to a preset distance, the kinematic model is discretized to obtain a system equation for generating the posture information of a second prediction point according to the posture information of the first prediction point, wherein the second prediction point is a next prediction point separated from the first prediction point by a preset distance; The system equation is linearized to obtain a processed target equation as the first constraint condition.

10. The method according to claim 9, characterized in that The system equation is: Among them, X i is the predicted pose information for the i-th prediction point, Δs is the preset distance, θ i is the heading angle corresponding to the i-th predicted point.

11. The method according to claim 10, characterized in that The linearization process is Taylor expansion; The objective equation is: X i+1 =A i X i +B i k i +C i Among them, A i , B i , C i Determined by: Among them, θ Ri is the heading angle corresponding to the i-th reference point.

12. The method according to claim 11, characterized in that Determining the target control parameter at the next moment of the target moment according to the LQR optimization model includes: Determine a feedback gain matrix that can minimize the target cost function while satisfying the target constraint condition; Determine a target control parameter at a next moment of the target moment according to the first posture information and the feedback gain matrix.

13. The method according to claim 12, characterized in that The determining of a feedback gain matrix that enables the target cost function to obtain a minimum value when the target constraint condition is satisfied includes: The objective cost function is solved by using a dynamic programming solution to obtain the feedback gain matrix.

14. The method according to claim 12, characterized in that The step of determining the target control parameter at the next moment of the target moment according to the first posture information and the feedback gain matrix includes: The target control parameter κ is determined according to the following formula: κ=-K i X i -L i Among them, K i , L i is the feedback gain matrix, and: q i =-QX Ri Among them, P N =Q, F N =q N .

15. The method according to claim 1, characterized in that The target control parameter is the steering curvature; The controlling the vehicle according to the target control parameter includes: determining a front wheel steering angle corresponding to the steering curvature; The vehicle is controlled according to the front wheel steering angle.

16. A vehicle control device, characterized in that: The device comprises: A first determination module is used to determine the posture information of the vehicle at the target time and the posture information corresponding to each of the plurality of prediction points as the first posture information, wherein the prediction points are determined from the prediction path corresponding to the vehicle; A second determination module is used to determine a plurality of reference points on a reference driving path corresponding to the vehicle, and determine the posture information corresponding to each reference point as the second posture information, wherein the plurality of reference points include a first reference point corresponding to the target moment and a preset number of second reference points after the target moment, and each of the reference points has a corresponding prediction point on the predicted path; A construction module, configured to construct an LQR optimization model according to the first posture information and the second posture information; A third determination module, used to determine the target control parameter at the next moment of the target moment according to the LQR optimization model; A control module is used to control the vehicle according to the target control parameter at a moment next to the target moment.

17. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the vehicle control method described in any one of claims 1 to 15 are implemented.

18. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the vehicle control method according to any one of claims 1 to 15.

19. A vehicle, characterized in that: An electronic device comprising the electronic device described in claim 18.

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