Vehicle lateral and longitudinal coordinated control method, device, vehicle and storage medium

Abstract

The application discloses a vehicle lateral-longitudinal cooperative control method and device, a vehicle and a storage medium. The vehicle lateral-longitudinal cooperative control method comprises the following steps: calculating feedforward acceleration and feedforward steering wheel rotation angle according to vehicle parameter information, a target trajectory, pose information and information; fusing a predicted state error obtained based on a hysteresis controller and a current actual state error obtained by sampling a target vehicle at a current time; performing MPC feedback calculation based on the fused state error to obtain feedback acceleration and feedback steering wheel rotation angle; and performing lateral-longitudinal control on the target vehicle according to the feedback acceleration and the feedback steering wheel rotation angle and the feedforward acceleration and the feedforward steering wheel rotation angle. The feedback acceleration and the feedback steering wheel rotation angle with system time lag and the feedforward acceleration and the feedforward steering wheel rotation angle jointly act on the vehicle system, the pure lag characteristic in the system is avoided, and the stability of the system is further improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and more specifically, to a method, device, vehicle, and storage medium for coordinated lateral and longitudinal control of a vehicle. Background Art

[0002] Currently, Model Predictive Control (MPC) for both lateral and longitudinal directions is one of the main algorithms in the field of autonomous driving. MPC control primarily uses a prediction model to predict system outputs over a period of time (i.e., the prediction domain and the control domain). Under various constraints, it solves the objective function to obtain a sequence of controlled variables within the control domain. The first value in the control domain is then applied to the controlled object as the current output value. The above process is repeated in the next cycle, completing each constrained optimization problem in a rolling manner, thereby achieving continuous control of the vehicle's lateral and longitudinal directions.

[0003] Because most control systems have time lag in their dynamic response and feedback processes, the controlled variable takes time to react to the controlled object, and the feedback error used in the control process is from a previous period. This increases the control system's rise time and overshoot, affecting control performance and stability. When the ratio of time lag to the system time constant is greater than 0.5, the system will experience oscillation and instability. Summary of the Invention

[0004] In view of the above problems, the present application proposes a vehicle lateral and longitudinal coordinated control method, device, vehicle and storage medium.

[0005] In a first aspect, an embodiment of the present application provides a method for coordinated lateral and longitudinal control of a vehicle, the method comprising: obtaining vehicle parameter information, a target trajectory, and position information and state information of a target vehicle during travel along the target trajectory; calculating a feedforward acceleration and a feedforward steering wheel angle based on the vehicle parameter information, the target trajectory, the position information, and the state information; obtaining a predicted state error based on a hysteresis controller, and fusing the predicted state error with the current actual state error based on the predicted state error and a current actual state error sampled from the target vehicle at the current moment to obtain a fused state error; performing MPC feedback calculation based on the fused state error to obtain feedback acceleration and feedback steering wheel angle, wherein a prediction model during the MPC feedback calculation is a prediction model updated based on the difference between the predicted state error and the current actual state error; calculating a target acceleration and a target steering wheel angle based on the feedback acceleration and the feedback steering wheel angle and the feedforward acceleration and the feedforward steering wheel angle; and performing lateral and longitudinal control of the target vehicle based on the target acceleration and the target steering wheel angle.

[0006] In the second aspect, an embodiment of the present application provides a vehicle lateral and longitudinal coordinated control device, the device comprising: an information acquisition module for acquiring vehicle parameter information, a target trajectory, and position information and state information of the target vehicle during its travel along the target trajectory; a feedforward data acquisition module for calculating the feedforward acceleration and the feedforward steering wheel angle based on the vehicle parameter information, the target trajectory, the position information, and the state information; a fused state error acquisition module for obtaining a predicted state error based on a hysteresis controller, and comparing the predicted state error with the actual state error obtained by sampling the target vehicle at the current moment. The current actual state error is fused to obtain a fused state error; a feedback data acquisition module is used to perform MPC feedback calculation based on the fused state error to obtain feedback acceleration and feedback steering wheel angle, wherein the prediction model during the MPC feedback calculation is a prediction model updated based on the difference between the predicted state error and the current actual state error; a target data acquisition module is used to calculate the target acceleration and target steering wheel angle according to the feedback acceleration and feedback steering wheel angle and the feedforward acceleration and feedforward steering wheel angle; a lateral and longitudinal control module is used to perform lateral and longitudinal control of the target vehicle according to the target acceleration and target steering wheel angle.

[0007] In a third aspect, an embodiment of the present application provides a vehicle comprising: one or more processors; a memory; and one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to execute the vehicle lateral and longitudinal coordinated control method provided in the first aspect above.

[0008] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a program code is stored. The program code can be called by a processor to execute the vehicle lateral and longitudinal coordinated control method provided in the first aspect above.

[0009] The solution provided in the present application incorporates system time delay collected by a hysteresis controller into the prediction model, and updates the model equations through the predicted state error with system time delay so that the model equations of the prediction model can adapt to the constantly changing model parameters. In addition, the prediction model also calculates the feedback acceleration and feedback steering wheel angle in the feedback process based on the fused state error of the predicted state error with system time delay and the actual state error, and acts together with the feedforward acceleration and feedforward steering wheel angle of the feedforward system on the vehicle system to obtain the target acceleration and target steering wheel angle, thereby realizing lateral and longitudinal control of the vehicle system. During the lateral and longitudinal control process, the pure hysteresis characteristics of the system are avoided, and the stability of the system is further improved. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0011] Figure 1 A flowchart of a vehicle lateral and longitudinal cooperative control method provided by an embodiment of the present application is shown.

[0012] Figure 2 A detailed flowchart of step S120 in an embodiment of the present application is shown.

[0013] Figure 3 A detailed flowchart of step S124 in an embodiment of the present application is shown.

[0014] Figure 4 A detailed flowchart of step S130 in an embodiment of the present application is shown.

[0015] Figure 5 An update flowchart of a prediction model in an embodiment of the present application is shown.

[0016] Figure 6 A detailed flowchart of step S140 in an embodiment of the present application is shown.

[0017] Figure 7 A control principle diagram of a vehicle lateral and longitudinal cooperative control method provided by an embodiment of the present application is shown.

[0018] Figure 8 A structure block diagram of a vehicle lateral and longitudinal cooperative control device provided by an embodiment of the present application is shown.

[0019] Figure 9 A structure block diagram of a vehicle for executing a vehicle lateral and longitudinal cooperative control method according to an embodiment of the present application is shown.

[0020] Figure 10 A storage medium for storing or carrying program codes for implementing a vehicle lateral and longitudinal cooperative control method according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0021] In order to make the person skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application.

[0022] To address these issues, the inventors discovered that most control systems exhibit time lag in their dynamic response and feedback processes. This delay causes the controlled variable to react to the controlled object, and the feedback error used in the control process is older than expected. This increases the control system's rise time and overshoot, impacting control performance and stability. When the ratio of this time lag to the system's time constant exceeds 0.5, the system can experience oscillation and instability.

[0023] The inventors have proposed a method, device, vehicle and storage medium for coordinated lateral and longitudinal control of a vehicle. In the present application, the system time lag collected by the hysteresis controller is added to the prediction model, and the model equation is updated by the predicted state error with the system time lag, so that the model equation of the prediction model can adapt to the constantly changing model parameters. In addition, the prediction model also calculates the feedback acceleration and feedback steering wheel angle in the feedback process based on the predicted state error with the system time lag and the fused state error of the actual state error, and acts on the vehicle system together with the feedforward acceleration and feedforward steering wheel angle of the feedforward system to obtain the target acceleration and target steering wheel angle to achieve lateral and longitudinal control of the vehicle system. In the lateral and longitudinal control process, the pure hysteresis characteristics of the system are avoided, and the stability of the system is further improved.

[0024] See also Figure 1 , Figure 1 A flow chart of a vehicle lateral and longitudinal coordinated control method according to an embodiment of the present application is shown. In a specific embodiment, the vehicle lateral and longitudinal coordinated control method is applied to Figure 8 The vehicle lateral and longitudinal coordinated control device 300 and the vehicle 100 equipped with the vehicle lateral and longitudinal coordinated control device 300 are shown.

[0025] The following will target Figure 1 The process shown in FIG. 1 is described in detail. The vehicle lateral and longitudinal coordinated control method may specifically include the following steps:

[0026] Step S110: Acquire vehicle parameter information, a target trajectory, and position information and state information of the target vehicle while traveling along the target trajectory.

[0027] Vehicle parameter information includes but is not limited to wheelbase, mass, distance from the front and rear axles to the center of mass, and lateral stiffness of the wheels on one side of the front and rear axles.

[0028] The target trajectory refers to the trajectory of the vehicle traveling toward the destination. The target trajectory may be obtained by navigation based on the destination, or may be selected by the user without the aid of a navigation tool. The determination of the target trajectory is not specifically limited herein.

[0029] Step S120: Calculating a feedforward acceleration and a feedforward steering wheel angle based on the vehicle parameter information, the target trajectory, the posture information, and the state information.

[0030] See also Figure 2 , Figure 2 The following is a schematic diagram of a specific implementation process of step S120, which includes steps S122 to S124, which are described in detail as follows:

[0031] Step S122: performing curvature compensation calculation on the curvature corresponding to the current position of the target vehicle on the target trajectory based on the wheelbase, the mass, the distance from the front and rear axles to the center of mass, the cornering stiffness of the wheels on one side of the front and rear axles, and the speed of the center of mass of the target vehicle to obtain the feedforward steering wheel angle;

[0032] Feedforward steering wheel angle δ ff The calculation formula is:

[0033]

[0034] Where L is the wheelbase of the target vehicle, in meters; K is the curvature of the target vehicle on the target trajectory; m is the mass of the target vehicle, in kg; l f and l r is the distance from the front and rear axles to the center of mass of the target vehicle, in meters; c f and c r is the lateral stiffness of the front and rear axle wheels of the target vehicle, in N / rad; v x The speed of the target vehicle's center of mass, in m / s 2 , according to formula (1), the feedforward steering wheel angle of the vehicle at the current moment is obtained.

[0035] Step S124: performing slope compensation calculation on the current slope of the target vehicle according to the position information of the target vehicle to obtain the feedforward acceleration.

[0036] The terrain of the target vehicle can be determined based on the target vehicle's posture information. Different terrains may affect the vehicle's acceleration. For example, based on the target vehicle's posture information, it is determined that the vehicle is in an uphill state. Due to the effect of gravity, the acceleration of the target vehicle is less than the reference acceleration of the target trajectory. At this time, the slope compensation is negative, and the feedforward acceleration is less than the reference acceleration on the target trajectory.

[0037] See also Figure 3 The acquisition of the feedforward acceleration specifically includes steps S1242 to S1246, which are described in detail as follows:

[0038] Step S1242: Obtain a reference acceleration of the target vehicle while traveling along the target trajectory.

[0039] The reference acceleration during the target trajectory can be the maximum acceleration on the road corresponding to the target trajectory, or the reference acceleration specified by the user for the target trajectory, or the current acceleration of the target vehicle, which is not specifically limited here.

[0040] Step S1244: Calculate the component of the acceleration due to gravity in the direction of travel of the target vehicle at the current slope as the acceleration for slope compensation.

[0041] When the target vehicle is traveling on the current slope, if the component of the gravity acceleration in the direction of the current slope is positive, then the reference acceleration is compensated positively. If the component of the gravity acceleration in the direction of the current slope is negative, then the reference acceleration is compensated negatively.

[0042] The calculation formula for slope compensation acceleration is: a pitch =g*sin(θ)(2)

[0043] Where g is the acceleration due to gravity and θ is the current slope of the target vehicle. According to formula (2), the acceleration of the slope compensation of the current slope due to gravity can be obtained.

[0044] Step S1246: Calculate the sum of the reference acceleration of the target trajectory and the acceleration of the slope compensation to obtain the feedforward acceleration.

[0045] The feedforward acceleration fully considers the driving state of the target vehicle on the target trajectory and the slope of the target trajectory, making the calculation of the feedforward acceleration closer to the actual driving situation.

[0046] The feedforward acceleration and feedforward steering wheel angle are mainly related to the target trajectory and the vehicle's own parameters. In feedforward control, the corresponding feedforward acceleration and feedforward steering wheel angle are obtained according to different vehicle parameters and different driving trajectories, so that the parameters in the feedforward control can achieve an adaptive effect and enhance the robustness of the control system.

[0047] Step S130: Obtain a predicted state error based on the hysteresis controller, and fuse the predicted state error with the current actual state error based on the predicted state error and the current actual state error obtained by sampling the target vehicle at the current moment to obtain a fused state error.

[0048] The predicted state error obtained based on the hysteresis controller specifically includes: obtaining the system time lag of the target vehicle at a current moment based on the hysteresis controller; and obtaining the predicted state error based on the system time lag.

[0049] See also Figure 4 , Figure 4 The specific implementation flow diagram of step S130 is shown. Step S130 specifically includes steps S132 to S138, which are described in detail as follows:

[0050] Step S132: Obtain the measurement noise covariance and process noise covariance of the target vehicle.

[0051] The measurement noise covariance and process noise covariance can be calculated using the noise measurement tool.

[0052] Step S134: based on the Kalman gain, the optimal estimation variance and the process noise covariance of the prediction model at the previous moment, the optimal estimation variance at the current moment is obtained.

[0053] The optimal estimate variance calculation formula at the current moment is:

[0054]

[0055] Among them, K g (k) is the Kalman gain at the previous moment, Q kf is the process noise covariance of the system Kalman fusion.

[0056] Step S136: performing calculation based on the optimal estimation variance and the measurement noise covariance at the current moment to obtain the Kalman gain of the prediction model at the current moment.

[0057] The Kalman gain calculation formula is:

[0058] Among them, R kf is the measurement noise covariance.

[0059] Step S138: According to the Kalman gain at the current moment, the predicted state error and the current actual state error are fused to obtain the fused state error.

[0060] In the embodiments of this application, Kalman fusion is used as the fusion method. Kalman fusion (KF) is a recursive predictive filtering algorithm. KF provides an efficient and computable method for estimating the past and current states of a signal, and even its future state. The fusion method can also be modified by the user and is not limited here.

[0061] The state error equation after fusion is:

[0062] X(k|k)=X(k-τ|k-τ-1)+K g (k)*(Z(k)-X(k-τ|k-τ-1)) (5)

[0063] In formula (5), k is the current time, τ is the system delay, and K g (k) is the Kalman gain corresponding to the current moment, Z(k) is the current actual state error, and X(k-τ|k-τ-1) is the predicted state error.

[0064] Through Kalman fusion, the predicted state error with system time delay is fused with the actual state error, so that the fused state error fully takes into account the time delay problem existing in the vehicle system, thereby improving the control accuracy of the vehicle's lateral and longitudinal systems.

[0065] Step S140: Performing MPC feedback calculation based on the fused state error to obtain feedback acceleration and feedback steering wheel angle, wherein the prediction model during the MPC feedback calculation is a prediction model updated based on the difference between the predicted state error and the current actual state error.

[0066] See also Figure 5 , Figure 5 A schematic diagram of the update process of the prediction model in an embodiment of the present application is shown. The prediction model may be a vehicle dynamics error model. The update of the prediction model specifically includes the following steps:

[0067] Step S210: Obtain a continuity state equation based on the prediction model at the previous moment and the state matrix corresponding to the target vehicle at the current moment.

[0068] The state matrix is:

[0069]

[0070] Among them, the e in matrix (1) cg is the lateral deviation, is the rate of change of lateral deviation, the unit of lateral deviation is m; θ e is the heading angle deviation, is the rate of change of heading angle deviation; e x is the distance error, is the speed error, the distance error unit is m, the speed error is m / s; I z The moment of inertia of the vehicle, in kg.m 2 .

[0071] The continuity state equation is obtained through matrix (6), which is:

[0072]

[0073] By calculating formula (7), the state quantity after the system delay update can be obtained.

[0074] Step S220: discretize the continuity state equation according to the control period to obtain a discretized prediction model.

[0075] The continuous state equation is discretized according to the control period to obtain the discretized prediction model:

[0076] x(k+1)=A d x(k)+Bdu(k)+C d (8)

[0077] The discretization method can be bilinear discretization, and the specific discretization formula is as follows:

[0078] A d =(I-0.5A*T s ) -1 (I+0.5A*T s )

[0079] B d =B*T s

[0080] C d =C*T s

[0081] Where I is the identity matrix and Ts is the control period in seconds.

[0082] Step S230: updating the discretized prediction model according to the Kalman gain at the current moment and the difference between the predicted state error and the current actual state error.

[0083] According to the Kalman gain at the current moment and the difference between the predicted state error and the current actual state error, an updated discretized prediction model is obtained. The prediction model equation is:

[0084] X(k+1|k)=A d X(k-1|k-1)+B d U(k)+C d +K g (k)(Z(k)-X(k-τ|k-τ-1)) (9)

[0085] The prediction model is corrected according to the difference between the predicted state error and the current actual state error, which can adapt to the changing model parameters and make the updated model closer to the real vehicle model.

[0086] See also Figure 6 , Figure 6 The specific steps for obtaining the feedback acceleration and the feedback steering wheel angle in the embodiment of the present application are shown and detailed as follows:

[0087] Step S142: Obtain the MPC objective equation and the constraint information of the prediction model.

[0088] The MPC objective equation is:

[0089]

[0090] Where N is the prediction and control time domain, unit is s; Q is the state weight matrix; R is the control weight matrix; x r For reference status, the default value is 0.

[0091] According to the state equation: x(k+1)=A d x(k)+B d u(k) constrains the state quantity and control quantity in the state equation. Since C d This has been taken into account in the curvature feedforward compensation, so the equality constraint does not apply to C. d Further constraints.

[0092] The constraint information of the prediction model is: x min ≤x(k)≤x max ,u min ≤u(k)≤u max , x(0)=x0. Where, x min is the minimum constraint of the state matrix, x max is the maximum constraint of the state matrix, u min is the minimum constraint of control, u max is the maximum constraint of the control, and x0 is the state at the initial moment.

[0093] According to the state quantity, the constraint information of the state quantity, the control quantity, and the constraint information of the control quantity, the state quantity and the control quantity are further constrained so that the state quantity under the constraint and the control quantity under the constraint are input into the objective function to obtain the optimal control boundary, so that the vehicle moves under the current control quantity.

[0094] Step S144: performing MPC feedback calculation based on the fused state error, the updated prediction model, the MPC objective equation, and the constraint information to obtain the feedback acceleration and the feedback steering wheel angle.

[0095] The feedback acceleration and the feedback steering wheel angle are calculated based on the fused state error, the updated prediction model, the MPC objective equation and the constraint information, so that the system time lag obtained by the hysteresis controller is added to the MPC feedback process, thereby avoiding the pure hysteresis characteristics in the system and further improving the stability of the system.

[0096] Step S150: Calculating a target acceleration and a target steering wheel angle according to the feedback acceleration and the feedback steering wheel angle and the feedforward acceleration and the feedforward steering wheel angle.

[0097] The target acceleration is the sum of the feedforward acceleration and the feedback acceleration, and the target steering wheel angle is the sum of the feedback steering wheel angle and the feedforward steering wheel angle.

[0098] Step S160: Controlling the target vehicle in the lateral and longitudinal directions according to the target acceleration and the target steering wheel angle.

[0099] The corresponding solver is called to calculate the target steering wheel angle and target acceleration at time k, and input them to the controlled vehicle to make it move under the current control variable. Then the above steps are repeated at time k+1, and in this way, the constrained optimization problem is implemented in a rolling manner, thereby achieving continuous control of the controlled object. The solver can be an OSQP solver, and the specific type of solver is not specifically limited here.

[0100] In feedforward control, the feedforward acceleration is calculated using the slope compensation method, and the feedforward steering wheel angle is calculated using the curvature compensation method. The feedforward lateral and longitudinal data are obtained based on the feedforward acceleration and feedforward steering wheel angle.

[0101] According to the feedback control amount and the feedforward transverse and longitudinal data, the target transverse and longitudinal data of the vehicle are obtained, and the vehicle is controlled by the target transverse and longitudinal data.

[0102] In an embodiment of the present application, the system time lag collected by the hysteresis controller is added to the prediction model, and the model equation is updated by the predicted state error with system time lag, so that the model equation of the prediction model can adapt to the constantly changing model parameters. In addition, the prediction model also calculates the feedback acceleration and feedback steering wheel angle in the feedback process based on the predicted state error with system time lag and the fused state error of the actual state error, and acts on the vehicle system together with the feedforward acceleration and feedforward steering wheel angle of the feedforward system to obtain the target acceleration and target steering wheel angle to achieve lateral and longitudinal control of the vehicle system. During the lateral and longitudinal control process, the pure hysteresis characteristics in the system are avoided, and the stability of the system is further improved.

[0103] See also Figure 7 , Figure 7A control principle schematic diagram of a vehicle longitudinal and lateral cooperative control method provided by the embodiment of the application is shown. In the feedforward control, the slope information corresponding to the current time of the vehicle is analyzed by acquiring the planning trajectory of the vehicle, the vehicle position information and the parameter information of the vehicle, the slope compensation, i.e., the speed, is calculated according to the feedforward longitudinal calculation formula, and the feedforward acceleration in the feedforward control is obtained together according to the target acceleration of the trajectory planning. The feedforward steering wheel angle in the feedforward control is obtained according to the current trajectory curvature of the vehicle and the feedforward lateral calculation formula.

[0104] In the MPC feedback control process, the system time lag is acquired through the hysteresis controller, the predicted state error with the system time lag is fused with the actual error through Kalman fusion, and the fused state error is acquired. The fused state error is input into the prediction model updated according to the predicted state error and the current actual state error, and the feedback acceleration and the feedback steering wheel angle are acquired. The feedback acceleration and the feedback steering wheel angle corresponding to each time are obtained by rolling solution according to the different predicted state errors at each time. In the scheme of the application, the MPC prediction model is a vehicle dynamics error model.

[0105] The target steering wheel angle is obtained according to the feedforward steering wheel angle and the feedback steering wheel angle, and the target acceleration is obtained according to the feedforward acceleration and the feedback acceleration, so as to control the controlled vehicle, solve the system instability phenomenon caused by the system time lag, improve the control precision of the vehicle, and enhance the robustness of the control system.

[0106] Please refer to Figure 8, which shows a structural block diagram of a vehicle transverse and longitudinal cooperative control device 300 provided in an embodiment of the present application. The vehicle transverse and longitudinal cooperative control device 300 is applied to the vehicle 100, and the vehicle transverse and longitudinal cooperative control device 300 includes: an information acquisition module 310, for acquiring vehicle parameter information, target trajectory, and position information and state information of the target vehicle during the driving process along the target trajectory; a feedforward data acquisition module 320, for calculating the feedforward acceleration and feedforward steering wheel angle according to the vehicle parameter information, the target trajectory, the position information and the state information; a fused state error acquisition module 330, for acquiring a predicted state error based on a hysteresis controller, and based on the predicted state error and the current actual state error obtained by sampling the target vehicle at the current moment, the predicted state error is converted into the actual state error. It is fused with the current actual state error to obtain a fused state error; a feedback data acquisition module 340 is used to perform MPC feedback calculation based on the fused state error to obtain feedback acceleration and feedback steering wheel angle, wherein the prediction model during the MPC feedback calculation is a prediction model updated based on the difference between the predicted state error and the current actual state error; a target data acquisition module 350 is used to calculate the target acceleration and target steering wheel angle according to the feedback acceleration and feedback steering wheel angle and the feedforward acceleration and feedforward steering wheel angle; a lateral and longitudinal control module 360 ​​is used to perform lateral and longitudinal control of the target vehicle according to the target acceleration and target steering wheel angle.

[0107] In some embodiments of the present application, the fused state error acquisition module 330 includes: a system time lag acquisition module, used to obtain the system time lag of the target vehicle at the current moment based on the hysteresis controller; and a predicted state error acquisition module, used to obtain the predicted state error based on the system time lag.

[0108] In some embodiments of the present application, the fused state error acquisition module 330 also includes: a covariance data acquisition module for acquiring the measurement noise covariance and process noise covariance of the target vehicle; an optimal estimation variance acquisition module for acquiring the optimal estimation variance at the current moment based on the Kalman gain, optimal estimation variance and the measurement noise covariance of the prediction model at the previous moment; a Kalman gain acquisition module for calculating according to the optimal estimation variance and the process noise covariance at the current moment to obtain the Kalman gain of the prediction model at the current moment; a fusion module for fusing the predicted state error and the current actual state error according to the Kalman gain at the current moment to obtain the fused state error.

[0109] In some embodiments of the present application, the feedback data acquisition module 340 includes: a continuity state equation acquisition module, which is used to obtain the continuity state equation based on the prediction model of the previous moment and the state matrix corresponding to the target vehicle at the current moment; a discretization module, which is used to discretize the continuity state equation according to the control period to obtain the discretized prediction model; and a model update module, which is used to update the discretized prediction model according to the Kalman gain at the current moment and the difference between the predicted state error and the current actual state error.

[0110] In some specific embodiments, the prediction model is a vehicle dynamics error model.

[0111] In some embodiments of the present application, the feedback data acquisition module 340 includes: an information acquisition module for acquiring the MPC objective equation and the constraint information of the prediction model; a feedback information acquisition module for performing MPC feedback calculation based on the fused state error, the updated prediction model, the MPC objective equation and the constraint information to acquire the feedback acceleration and the feedback steering wheel angle.

[0112] In some embodiments of the present application, the vehicle parameter information includes wheelbase, mass, distance from the front and rear axles to the center of mass, and lateral stiffness of the wheels on one side of the front and rear axles; the status information includes current position information; the feedforward data acquisition module 320 includes: a feedforward steering wheel angle acquisition module, which is used to perform curvature compensation calculation on the curvature corresponding to the current position of the target vehicle on the target trajectory based on the wheelbase, the mass, the distance from the front and rear axles to the center of mass, the lateral stiffness of the wheels on one side of the front and rear axles, and the speed of the center of mass of the target vehicle to obtain the feedforward steering wheel angle; a feedforward acceleration acquisition module, which is used to perform slope compensation calculation on the current slope of the target vehicle based on the posture information of the target vehicle to obtain the feedforward acceleration.

[0113] In some embodiments of the present application, the feedforward steering wheel angle is calculated as follows: Calculate the feedforward steering wheel angle δ ff , where L is the wheelbase of the target vehicle, K is the curvature of the target vehicle on the target trajectory, m is the mass of the target vehicle, l f is the distance from the front axle to the center of mass of the target vehicle, l r is the distance from the rear axle to the center of mass of the target vehicle, c f is the cornering stiffness of the front axle wheel of the target vehicle, c r is the cornering stiffness of the rear axle wheel of the target vehicle, v x is the speed of the target vehicle's center of mass.

[0114] In some embodiments of the present application, the feedforward acceleration acquisition module includes: a reference acceleration acquisition module, used to obtain the reference acceleration of the target vehicle during its travel along the target trajectory; a slope-compensated acceleration acquisition module, used to calculate the component of the gravity acceleration in the direction of travel of the target vehicle on the current slope as the slope-compensated acceleration; and a calculation module, used to calculate the sum of the reference acceleration of the target trajectory and the slope-compensated acceleration to obtain the feedforward acceleration.

[0115] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0116] In several embodiments provided in this application, the coupling between modules may be electrical, mechanical or other forms of coupling.

[0117] In addition, the functional modules in the various embodiments of the present application may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The above-mentioned integrated modules may be implemented in the form of hardware or software functional modules.

[0118] Please refer to Figure 9 , which shows a structural block diagram of a vehicle provided by an embodiment of the present application. The vehicle 100 may include one or more of the following components: a processor 101, a memory 102, and one or more application programs. The one or more application programs may be stored in the memory 102 and configured to be executed by the one or more processors 101, and the one or more programs may be configured to execute the method described in the aforementioned method embodiment.

[0119] The processor 101 may include one or more processing cores. The processor 101 utilizes various interfaces and circuits to connect to various components within the vehicle 100. It executes instructions, programs, code sets, or instruction sets stored in the memory 102, as well as accesses data stored in the memory 102, to perform various functions and process data within the vehicle 100. Optionally, the processor 101 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 101 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing display content; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 101 but may be implemented separately via a communications chip.

[0120] The memory 102 may include random access memory (RAM) or read-only memory (ROM). The memory 102 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 102 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the various method embodiments described below, and the like. The data storage area may also store data created by the vehicle 100 during use (such as a phone book, audio and video data, and chat log data).

[0121] Vehicle 100 includes a vehicle body and the vehicle lateral and longitudinal coordinated control device of the above-described embodiment. The vehicle lateral and longitudinal coordinated control device is disposed within vehicle 100. To implement the vehicle lateral and longitudinal coordinated control process of the present application, in addition to the vehicle lateral and longitudinal coordinated control device, a feedforward device is also included to work together to implement the present application.

[0122] Please refer to Figure 10 , which shows a block diagram of a computer-readable storage medium provided in an embodiment of the present application. The computer-readable storage medium 200 stores program code, which can be called by a processor to execute the method described in the above method embodiment.

[0123] The computer-readable storage medium 200 can be an electronic memory such as a flash memory, an EEPROM (Electrically Erasable Programmable Read-Only Memory), an EPROM, a hard disk, or a ROM. Alternatively, the computer-readable storage medium 200 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 200 has storage space for program code 210 for executing any of the method steps in the above method. These program codes can be read from or written to one or more computer program products. The program code 210 can be compressed, for example, in a suitable form.

[0124] Finally, it should be noted that the above 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. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A vehicle lateral and longitudinal coordinated control method, characterized in that: The method comprises: Acquiring vehicle parameter information, a target trajectory, and position information and state information of the target vehicle during its travel along the target trajectory; Calculating a feedforward acceleration and a feedforward steering wheel angle according to the vehicle parameter information, the target trajectory, the posture information, and the state information; Obtaining a predicted state error based on a hysteresis controller, and fusing the predicted state error with the current actual state error obtained by sampling the target vehicle at the current moment to obtain a fused state error; performing an MPC feedback calculation based on the fused state error to obtain a feedback acceleration and a feedback steering wheel angle, wherein a prediction model during the MPC feedback calculation is a prediction model updated based on a difference between the predicted state error and the current actual state error; Calculating a target acceleration and a target steering wheel angle according to the feedback acceleration and the feedback steering wheel angle and the feedforward acceleration and the feedforward steering wheel angle; The target vehicle is controlled in the transverse and longitudinal directions according to the target acceleration and the target steering wheel angle.

2. The method according to claim 1, characterized in that The obtaining of the predicted state error based on the hysteresis controller includes: Obtaining the system time lag of the target vehicle at a current moment based on the hysteresis controller; The predicted state error is obtained based on the system time lag.

3. The method according to claim 1 or 2, characterized in that The fusing the predicted state error with the current actual state error to obtain a fused state error includes: Obtaining a measurement noise covariance and a process noise covariance of the target vehicle; Obtaining the optimal estimation variance at the current moment based on the Kalman gain, the optimal estimation variance, and the process noise covariance of the prediction model at the previous moment; Calculating based on the optimal estimation variance and the measurement noise covariance at the current moment to obtain the Kalman gain of the prediction model at the current moment; The predicted state error and the current actual state error are fused according to the Kalman gain at the current moment to obtain the fused state error.

4. The method according to claim 1 or 2, characterized in that The updating of the prediction model includes: Obtaining a continuity state equation based on the prediction model at the previous moment and the state matrix corresponding to the target vehicle at the current moment; Discretizing the continuity state equation according to the control period to obtain a discretized prediction model; The discretized prediction model is updated according to the Kalman gain at the current moment and the difference between the predicted state error and the current actual state error.

5. The method according to claim 4, characterized in that The prediction model is a vehicle dynamics error model.

6. The method according to claim 5, characterized in that The performing of MPC feedback calculation based on the fused state error to obtain feedback acceleration and feedback steering wheel angle includes: Obtaining the MPC objective equation and constraint information of the prediction model; An MPC feedback calculation is performed based on the fused state error, the updated prediction model, the MPC objective equation, and the constraint information to obtain the feedback acceleration and the feedback steering wheel angle.

7. The method according to claim 1 or 2, characterized in that The vehicle parameter information includes wheelbase, mass, distance from the front and rear axles to the center of mass, and cornering stiffness of a wheel on one side of the front and rear axles. Calculating the feedforward acceleration and the feedforward steering wheel angle based on the vehicle parameter information, the target trajectory, the position information, and the state information includes: performing curvature compensation calculation on a curvature corresponding to a current position of the target vehicle on the target trajectory based on the wheelbase, the mass, the distance from the front and rear axles to the center of mass, the cornering stiffness of a wheel on one side of the front and rear axles, and the speed of the center of mass of the target vehicle to obtain the feedforward steering wheel angle; A slope compensation calculation is performed on the current slope of the target vehicle according to the position information of the target vehicle to obtain the feedforward acceleration.

8. The method according to claim 7, characterized in that The performing curvature compensation calculation on the curvature corresponding to the current position of the target vehicle on the target trajectory to obtain the feedforward steering wheel angle includes: according to Calculate the feedforward steering wheel angle ,in, is the wheelbase of the target vehicle, is the curvature of the target vehicle on the target trajectory, m is the mass of the target vehicle, l f is the distance from the front axle to the center of mass of the target vehicle, l r is the distance from the rear axle to the center of mass of the target vehicle, c f is the cornering stiffness of the front axle wheel of the target vehicle, c r is the cornering stiffness of the rear axle wheel of the target vehicle, v x is the speed of the target vehicle's center of mass.

9. The method according to claim 7, characterized in that The step of performing slope compensation calculation on the current slope of the target vehicle according to the position information of the target vehicle to obtain the feedforward acceleration includes: Obtaining a reference acceleration of the target vehicle while traveling along the target trajectory; Calculating a component of the acceleration due to gravity in the direction of travel of the target vehicle on the current slope as an acceleration for slope compensation; The sum of the reference acceleration of the target trajectory and the slope-compensated acceleration is calculated to obtain the feedforward acceleration.

10. A vehicle lateral and longitudinal coordinated control device, characterized in that: The device comprises: An information acquisition module is used to obtain vehicle parameter information of a target vehicle, a target trajectory, and position information and state information of the target vehicle during its travel along the target trajectory; a feedforward data acquisition module, configured to calculate a feedforward acceleration and a feedforward steering wheel angle based on the vehicle parameter information, the target trajectory, the position information, and the state information; a fused state error acquisition module, configured to acquire a predicted state error based on a hysteresis controller, and fuse the predicted state error with the current actual state error obtained by sampling the target vehicle at the current moment to obtain a fused state error; a feedback data acquisition module, configured to perform an MPC feedback calculation based on the fused state error to obtain a feedback acceleration and a feedback steering wheel angle, wherein a prediction model during the MPC feedback calculation is a prediction model updated based on a difference between the predicted state error and the current actual state error; a target data acquisition module, configured to calculate a target acceleration and a target steering wheel angle based on the feedback acceleration and the feedback steering wheel angle and the feedforward acceleration and the feedforward steering wheel angle; The transverse and longitudinal control module is used to control the target vehicle in the transverse and longitudinal directions according to the target acceleration and the target steering wheel angle.

11. A vehicle, characterized in that: include: one or more processors; Memory; One or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to execute the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, which can be called by a processor to execute the method according to any one of claims 1 to 9.

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