Vehicle trajectory tracking control method, device and equipment under extreme working condition and medium

Abstract

Embodiments of the present application provide a vehicle trajectory tracking control method, device, equipment and medium under extreme conditions, the method comprising: when detecting that the vehicle enters the limit control mode, determining the reference trajectory information of the reference driving trajectory of the vehicle; optimizing the first reference information in the reference trajectory information according to the target optimization function constructed in advance, determining the optimized target trajectory information, the target optimization function comprising the constraint on the target control amount of the vehicle and the change of the center of mass side slip angle; determining the target control amount of the vehicle according to the target trajectory information and the second reference information in the reference trajectory information, in combination with the preset nonlinear dynamics model. By using the method, the improved nonlinear optimization method is used, that is, the constraint on the target control amount of the vehicle and the change of the center of mass side slip angle is used as the target optimization function to optimize the reference trajectory, so that the planned trajectory can be tracked as much as possible to meet the engineering needs on the premise of maintaining the stability of the vehicle body.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and in particular to a method, device, equipment and medium for vehicle trajectory tracking control under extreme working conditions. Background Technology

[0002] Autonomous vehicle planning and control functions utilize environmental perception results, vehicle position and posture, and the vehicle's motion characteristics to plan a safe, convenient, and comfortable vehicle trajectory. They then control drive, braking, and steering commands to achieve stable vehicle operation. Currently, common planning and control methods are only applicable to steady-state vehicle operation conditions, such as driving on high-friction surfaces where tire slippage or rotation does not occur and the vehicle does not tilt. Ensuring the stability of autonomous driving planning and control functions under extreme conditions is a crucial challenge that must be addressed in the popularization of autonomous driving products and technologies. Judging from the current implementation status of numerous research projects, discussions on autonomous vehicle control under specific application contexts of extreme conditions are still incomplete. Summary of the Invention

[0003] This invention provides a vehicle trajectory tracking control method, device, equipment, and medium under extreme working conditions, which realizes the tracking of the planned trajectory as much as possible while maintaining vehicle stability, speeding up the solution process and meeting engineering needs.

[0004] In a first aspect, embodiments of the present invention provide a vehicle trajectory tracking control method under extreme operating conditions, comprising:

[0005] When the vehicle is detected to have entered the extreme control mode, the reference trajectory information of the vehicle's reference driving trajectory is determined.

[0006] The first reference information in the reference trajectory information is optimized according to the pre-constructed target optimization function to determine the optimized target trajectory information. The target optimization function includes constraints on the target control quantity of the vehicle and the change of the center of gravity sideslip angle.

[0007] Based on the target trajectory information and the second reference information in the reference trajectory information, and combined with a preset nonlinear dynamics model, the target control quantity of the vehicle is determined, so as to perform trajectory tracking control of the vehicle based on the target control quantity.

[0008] Secondly, embodiments of the present invention provide a vehicle trajectory tracking control device under extreme operating conditions, comprising:

[0009] The reference information determination module is used to determine the reference trajectory information of the vehicle's reference driving trajectory when the vehicle is detected to have entered the limit control mode.

[0010] The target information determination module is used to optimize the first reference information in the reference trajectory information according to the pre-constructed target optimization function, and determine the optimized target trajectory information. The target optimization function includes constraints on the target control quantity of the vehicle and the change of the center of gravity sideslip angle.

[0011] The control quantity determination module is used to determine the target control quantity of the vehicle based on the target trajectory information and the second reference information in the reference trajectory information, combined with a preset nonlinear dynamic model, so as to perform trajectory tracking control of the vehicle based on the target control quantity.

[0012] Thirdly, embodiments of the present invention also provide an electronic device, comprising:

[0013] At least one processor; and

[0014] A memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the vehicle trajectory tracking control method under extreme conditions as described in the first aspect embodiment.

[0016] Fourthly, embodiments of the present invention also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the vehicle trajectory tracking control method under extreme conditions as described in the first aspect embodiment.

[0017] This invention provides a vehicle trajectory tracking control method, device, equipment, and medium under extreme operating conditions. The method includes: when a vehicle is detected to have entered an extreme control mode, determining reference trajectory information of the vehicle's reference driving trajectory; optimizing first reference information in the reference trajectory information according to a pre-constructed target optimization function to determine optimized target trajectory information, wherein the target optimization function includes constraints on the vehicle's target control quantity and the change in the center of gravity sideslip angle; and determining the vehicle's target control quantity based on the target trajectory information and second reference information in the reference trajectory information, combined with a pre-defined nonlinear dynamics model, to perform trajectory tracking control on the vehicle based on the target control quantity. This technical solution, from a practical engineering perspective, fully considers the desired reference trajectory information and utilizes an improved nonlinear optimization method, namely, using the vehicle's target control quantity and the constraints on the change in the center of gravity sideslip angle as the target optimization function to optimize the reference trajectory, thereby achieving the goal of tracking the planned trajectory as much as possible while maintaining vehicle stability, accelerating the solution speed, and meeting engineering needs.

[0018] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a vehicle trajectory tracking control method under extreme working conditions provided in Embodiment 1 of the present invention.

[0021] Figure 2 This is a flowchart illustrating another vehicle trajectory tracking control method under extreme working conditions provided in Embodiment 2 of the present invention.

[0022] Figure 3 This is a schematic diagram of the structure of a vehicle trajectory tracking control device under extreme working conditions provided in Embodiment 3 of the present invention;

[0023] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "original," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] Example 1

[0027] Figure 1 This is a flowchart illustrating a vehicle trajectory tracking and control method under extreme operating conditions provided in Embodiment 1 of the present invention. This method is applicable to situations requiring vehicle trajectory tracking and control under extreme operating conditions. The method can be executed by a vehicle trajectory tracking and control device under extreme conditions, which can be implemented in hardware and / or software and can be configured in an electronic device. For example... Figure 1 As shown, the vehicle trajectory tracking control method under extreme conditions provided in this embodiment may specifically include the following steps:

[0028] S101. When the vehicle is detected to have entered the limit control mode, determine the reference trajectory information of the vehicle's reference driving trajectory.

[0029] Extreme operating conditions refer to situations where the vehicle becomes unstable, such as tire lock-up on slippery roads in snowy weather. The goal under extreme operating conditions is to achieve vehicle stability control, preventing excessive sideslip and yaw rates. The values ​​provided by the path planning layer may not necessarily meet the vehicle's dynamic requirements under these extreme conditions. While the output values ​​from the path planning layer can be understood as a desired outcome, from a vehicle control perspective, a car is a mechanical structure, and achieving stability, especially under unstable conditions, may not meet this expectation. Therefore, it is necessary to achieve trajectory tracking while ensuring vehicle stability.

[0030] In this embodiment, when the vehicle is detected to be in an extreme operating condition, the vehicle is controlled to enter the extreme control mode. The method for detecting whether the vehicle is in an extreme operating condition is not specifically limited. For example, it can be determined by identifying the road surface adhesion coefficient to identify whether the road surface is wet, dry, or icy, and thus determine whether the vehicle needs to enter the extreme operating condition; alternatively, it can be determined automatically based on the obstacle and the vehicle's condition. No specific limitations are imposed here.

[0031] Specifically, the vehicle's reference trajectory can be understood as the desired trajectory pre-stored within the vehicle. Reference trajectory information can be understood as information about this desired trajectory, such as the curvature of the reference path, reference speed, reference center of mass sideslip angle, reference heading angle, and reference yaw angle. Specifically, when the vehicle enters the limit control mode, the reference trajectory information is determined. Some of this reference trajectory information can be directly obtained from the planning layer, while others require solving for relevant parameters based on the equilibrium point formula under the vehicle model. For example, assuming the vehicle model used is a single-track three-degree-of-freedom model, the equilibrium point under the three-degree-of-freedom model can be determined according to the definition of the equilibrium point, and the relevant reference trajectory information can be obtained.

[0032] S102. Optimize the first reference information in the reference trajectory information according to the pre-constructed target optimization function to determine the optimized target trajectory information. The target optimization function includes constraints on the target control quantity of the vehicle and the change of the centroid sideslip angle.

[0033] Considering that when a vehicle is in or about to enter a state of extreme instability, the planning results of the upstream path planning layer will cause the vehicle state to change rapidly for the extreme instability controller. Maintaining vehicle stability is the primary control task under extreme conditions, followed by tracking the planned trajectory as closely as possible. To ensure that drift control is not affected by the desired path under such control conditions, this embodiment proposes a reference trajectory optimization method. This method considers constraints on vehicle dynamics, actuator torque and power constraints, and constraints on the rate of change of control variables. Based on an optimal problem, it optimizes the vehicle's reference trajectory in one step before sending it to the controller for drift control tracking.

[0034] In this embodiment, the vehicle model used is a single-track three-degree-of-freedom model. According to the statement of d'Alembert's principle, during the vehicle's movement, the active force, constraint force, and inertial force acting on the vehicle's center of gravity are in balance with each other, that is, the longitudinal force, lateral force, and yaw moment at the center of gravity are in balance. Thus, the following vehicle dynamics equations are derived:

[0035] ;

[0036] ;

[0037] ;

[0038] In the formula, Let yaw rate be the vehicle's angular velocity. The derivative of the vehicle's yaw rate. The angular velocity of the center of mass deflection. For vehicle speed, For the derivative of vehicle speed, This is the distance from the vehicle's center of gravity to the front axle. This is the distance from the vehicle's center of gravity to the rear axle. The lateral force on the front wheel of the monorail model. The lateral force is from the rear wheel. For the longitudinal force of the rear wheel, For the front wheel steering angle, For the heading angular velocity, For the quality of the car, For the car to be around the center of gravity Moment of inertia of the shaft , Given the vehicle's known parameters, For state variables, To control the quantity.

[0039] In this embodiment, the vehicle's state variables can be represented as: ,in, The x-coordinate of the centroid The ordinate of the centroid is y. The sideslip angle is the angle of the center of mass. The yaw rate is angular velocity. For car speed, For heading angle, The front wheel steering angle is controlled by... , Let be the derivative of the front wheel steering angle with respect to time. Let be the derivative of the longitudinal force on the rear wheel with respect to time. The derivative of the state variable with respect to time can be expressed as:

[0040]

[0041] The meanings of each letter in the formula are as described above and will not be repeated here. In addition, if the meaning of a letter involved in the embodiment has been described, its meaning will not be repeated when the letter is mentioned below.

[0042] The objective function is to minimize the rate of change of the control variable at each stage and to maximize the speed at which the centroid sideslip angle changes from the starting point to the ending point. Based on this objective function, a target optimization function is constructed. The first reference information is then optimized based on this objective optimization function. In this embodiment, the optimized trajectory information is denoted as the target trajectory information. The first reference information mainly refers to the radius and speed of the reference path. Alternatively, the target trajectory information can be understood as including the radius and speed of the optimized driving trajectory.

[0043] S103. Based on the target trajectory information and the second reference information in the reference trajectory information, and combined with the preset nonlinear dynamic model, determine the target control quantity of the vehicle, so as to perform trajectory tracking control of the vehicle based on the target control quantity.

[0044] The target trajectory information includes the radius and speed of the optimized driving trajectory, while the second reference information in the reference trajectory information refers to information such as the expected centroid sideslip angle determined based on the expected reference driving trajectory.

[0045] The nonlinear programming problem in the prior art can be expressed as:

[0046] ;

[0047] ;

[0048] ;

[0049] ;

[0050] in, This is the physical upper limit of the front wheel steering angle. This is the physical lower limit of the front wheel steering angle. This represents the physical lower limit of the longitudinal force on the rear axle. The uppermost constraint represents the physical upper limit of the longitudinal force on the rear axle, while the bottommost constraint represents the stability constraint of the controller. For the desired yaw acceleration, parameters Let be the desired heading angular velocity. Experiments show that in this optimization problem, once the desired state derivative exceeds the feasible region of the controller, the solution time is difficult to guarantee due to inequality constraints, making it unsuitable for practical engineering needs. A better approach is to project the desired state derivative along a straight line determined by the control law onto the feasible state derivative boundary to maximize the satisfaction of the desired dynamic derivative for the current state.

[0051] To further explain, based on the feasible state derivative of the three-degree-of-freedom vehicle model, due to the underactuated coupling effect of the system, the feasible state derivative forms a surface in three-dimensional space, and at the desired angular velocity... and expected yaw acceleration Given a given condition, some parts of the surface have two... To achieve zero dynamic stability without speed control, it is necessary to make... and On a surface above the dividing line, this part is and When determined, there is a unique This represents the unique desired control quantity pair. Due to the limitation of the control quantity, there are three feasible state derivatives. —The directional angular velocity, yaw acceleration, and the spatial distribution of acceleration form a curved surface.

[0052] The dividing line is:

[0053]

[0054] By constraining the nonlinear inversion to the upper surface of the tangent space above this boundary, the velocity becomes stable, and the nonlinear inversion becomes a single solution.

[0055] Therefore, we consider using the following improved method to replace the original nonlinear optimization problem:

[0056] ;

[0057] ;

[0058] ;

[0059] ;

[0060] Through this projection, the problem of not being able to simultaneously satisfy the equations for the two state derivatives of nonlinear inversion can be transformed into a compromise result that satisfies both equations. Furthermore, the constraint of the boundary line is included in the process of projection onto the boundary, transforming a constrained optimization problem into a geometric solution problem, thus accelerating the convergence speed. Substituting the formulas for front axle lateral force, rear axle lateral force, front axle sideslip angle, and front and rear axle vertical loads into the nonlinear programming problem yields the target control variables. The target control variables refer to the target front wheel steering angle and the target rear wheel longitudinal force. Controlling the vehicle based on these target control variables allows for tracking the planned trajectory as closely as possible while ensuring vehicle stability.

[0061] This invention provides a vehicle trajectory tracking control method under extreme operating conditions. The method includes: when a vehicle is detected to have entered an extreme control mode, determining reference trajectory information for the vehicle's reference driving trajectory; optimizing the first reference information in the reference trajectory information according to a pre-constructed target optimization function to determine optimized target trajectory information, the target optimization function including constraints on the vehicle's target control quantity and the change in the center of gravity sideslip angle; and determining the vehicle's target control quantity based on the target trajectory information and the second reference information in the reference trajectory information, combined with a pre-defined nonlinear dynamics model, to perform trajectory tracking control of the vehicle based on the target control quantity. This method, from a practical engineering perspective, fully considers the desired reference trajectory information and utilizes an improved nonlinear optimization method, using the vehicle's target control quantity and the constraints on the change in the center of gravity sideslip angle as the target optimization function to optimize the reference trajectory. This achieves the goal of tracking the planned trajectory as much as possible while maintaining vehicle stability, accelerating the solution speed and meeting engineering requirements.

[0062] Example 2

[0063] Figure 2 This is a flowchart illustrating another vehicle trajectory tracking control method under extreme conditions provided in Embodiment 2 of the present invention. This embodiment is a further optimization of the above embodiment. In this embodiment, the definition of "determining the reference trajectory information of the reference driving trajectory of the vehicle" is further optimized, the definition of "optimizing the first reference information in the reference trajectory information according to the pre-constructed target optimization function to determine the optimized target trajectory information" is further optimized, and the definition of "determining the target control quantity of the vehicle according to the target trajectory information and the second reference information in the reference trajectory information, combined with a preset nonlinear dynamic model" is further optimized.

[0064] like Figure 2 As shown in the figure, this embodiment 2 provides a vehicle trajectory tracking control method under extreme working conditions, which specifically includes the following steps:

[0065] S201. Obtain the reference radius and reference speed of the reference driving trajectory.

[0066] Specifically, the reference curvature and reference speed can be determined based on the geometry of the reference driving trajectory. Once the reference curvature is determined, the reference radius of the reference driving trajectory is also determined.

[0067] S202. Determine the equilibrium point formula under the vehicle model based on the preset vehicle model.

[0068] Specifically, a nonlinear dynamic model is established. In this embodiment, the vehicle model is a single-track three-degree-of-freedom model. According to the statement of d'Alembert's principle, during vehicle movement, the active force, constraint force, and inertial force acting on the vehicle's center of gravity are in balance, that is, the longitudinal force, lateral force, and yaw moment at the center of gravity are in balance. Thus, the following vehicle dynamic equations are derived:

[0069] ;

[0070] ;

[0071] ;

[0072] In the formula, The yaw rate is angular velocity. The derivative of the vehicle's yaw rate. The angular velocity of the center of mass deflection. For car speed, For the velocity derivative, This is the distance from the car's center of gravity to the front axle. This is the distance from the car's center of gravity to the rear axle. The lateral force on the front wheel of the monorail model. The lateral force is from the rear wheel. For the longitudinal force of the rear wheel, For the front wheel steering angle, For the heading angular velocity, For the quality of the car, For the car to be around the center of gravity Moment of inertia of the shaft. For state variables, , Given the vehicle's known parameters, To control the quantity.

[0073] In the process of solving for the equilibrium point, let the following variable be defined: the drift radius of the drift equilibrium point is... The vehicle's reference speed is The vehicle's reference center of gravity sideslip angle is Reference front wheel steering angle is The reference longitudinal force of the rear wheel is Reference to the lateral forces of the front and rear wheels is , According to the definition of equilibrium point, by setting the first-order differential term in the above formula to 0, the equilibrium point under the three-state model can be obtained, i.e.

[0074] ;

[0075] ;

[0076] ;

[0077] The meanings of each letter in the formula are as described above and will not be repeated here. In addition, if the meaning of a letter involved in the embodiment has been described, its meaning will not be repeated when the letter is mentioned below.

[0078] S203. Substitute the reference radius and reference speed into the equilibrium point formula to obtain the reference centroid sideslip angle of the reference driving trajectory.

[0079] There are five unknown variables in the above equation: , , , , To find the equilibrium point, three equations need to be solved, and constraints must be applied to two of the variables. In the engineering applications of autonomous driving, the upstream of the control system is the planning module. The output of the planning module includes not only the geometric information of the path but also the desired velocity information. The geometric information of the path determines the desired curvature, i.e. Given, combined with the desired speed, that is This is a preset value. Additionally, vehicle stability has specific requirements regarding the sideslip angle, meaning it is assumed that... The required value.

[0080] Based on the previous equation, we can deduce that:

[0081] ;

[0082] ;

[0083] ;

[0084] in, Given the vehicle height, the desired vehicle speed and sideslip angle are input into the nonlinear vehicle dynamics model to solve for the steady-state drift equilibrium point. , The control law corresponding to the steady-state equilibrium control point is equivalent to knowing the reference centroid sideslip angle of the reference driving trajectory.

[0085] S204. Use the reference radius, reference velocity, and reference centroid sideslip angle as reference trajectory information.

[0086] Specifically, the reference radius, reference velocity, and reference centroid sideslip angle are used as reference trajectory information.

[0087] S205. Determine the first equilibrium point corresponding to the starting point of the reference trajectory and the second equilibrium point corresponding to the ending point of the reference trajectory in the first reference trajectory information.

[0088] For example, suppose and For a finite number of N sampling time intervals, the quasi-static equilibrium points corresponding to the start and end points of the reference path are... This is designated as the first equilibrium point. This is denoted as the second equilibrium point.

[0089] S206. Based on the first equilibrium point, perform integration processing using the set integration algorithm to determine at least one target equilibrium point that satisfies the minimum objective optimization function.

[0090] The target equilibrium point is located between the first and second equilibrium points, and is the equilibrium point corresponding to the set sampling time. The integration algorithm is the fourth-order Runge-Kutta method.

[0091] When an autonomous vehicle is in or about to enter a state of extreme instability, the planning results from the upstream path planning module will cause the vehicle state to change rapidly for the extreme instability controller. Maintaining vehicle stability is the primary control task for an autonomous vehicle under extreme conditions, followed by tracking the planned trajectory as closely as possible. Under such control tasks, to prevent drift control from being affected by the desired path, this embodiment proposes a reference trajectory optimization method. This optimization method considers constraints on vehicle dynamics, actuator torque and power constraints, and constraints on the rate of change of control variables. Based on an optimal problem, it optimizes the vehicle's reference trajectory in one step before sending it to the controller for drift control tracking.

[0092] state variables ,in, The x-coordinate of the centroid The ordinate of the centroid is y. The sideslip angle is the angle of the center of mass. The yaw rate is angular velocity. For car speed, For heading angle, For the front wheel steering angle, control quantity , Let be the derivative of the front wheel steering angle with respect to time. Let be the derivative of the longitudinal force on the rear wheel with respect to time. The derivative of the state variable with respect to time is:

[0093]

[0094] The objective function is to minimize the rate of change of the control variable at each stage and to maximize the speed at which the centroid sideslip angle changes from the initial value to the final value. A certain trade-off is made between these two objectives. and This represents the corresponding trade-off matrix. The objective optimization function consists of the rate of change of the objective control variable, the rate of change of the centroid sideslip angle, and the trade-off matrix. The first constraint of the objective optimization function includes the physical constraints for obtaining the front wheel steering angle, the rear wheel driving force constraints, the driving torque constraints, and the power constraints. Specifically, the mathematical description of the trajectory optimization problem is:

[0095] ;

[0096] ;

[0097] ;

[0098] ;

[0099] ;

[0100] ;

[0101] ;

[0102] in: and For a finite number of N sampling time intervals, the quasi-static equilibrium points corresponding to the start and end points of the reference path are... This is for integrating the state variables using the fourth-order Runge-Kutta method. The equilibrium point corresponding to the Kth sampling time. The equilibrium point corresponding to the (K+1)th sampling time. and For the corresponding trade-off matrix, This is the control quantity corresponding to the k-th sampling time. The centroid sideslip angle corresponding to the k-th sampling time. This represents the average centroid sideslip angle. Let be the change in the centroid sideslip angle from the starting point to the ending point of the reference path. The above function can be denoted as the objective function, and the above constraint condition can be denoted as the first constraint condition. To control the lower boundary of the hard constraint, parameters To control the upper boundary of hard constraints.

[0103] Specifically, based on the first equilibrium point, integration is performed using the established integration algorithm to determine multiple objective equilibrium points that minimize the objective optimization function.

[0104] S207. Determine the optimized target trajectory information based on each target equilibrium point, the first equilibrium point, and the second equilibrium point.

[0105] The target trajectory information refers to the optimized driving trajectory radius and the optimized vehicle speed. Specifically, after obtaining each equilibrium point, the optimized driving trajectory can be determined by fitting each equilibrium point. Based on the optimized driving trajectory, the optimized driving trajectory radius and the optimized vehicle speed can be determined.

[0106] Furthermore, based on each target equilibrium point, the first equilibrium point, and the second equilibrium point, the optimized target trajectory information is determined, including:

[0107] a1) Fit each target equilibrium point, the first equilibrium point, and the second equilibrium point to determine the optimized target trajectory.

[0108] Specifically, after obtaining each target equilibrium point, the first equilibrium point of the starting point, and the second equilibrium point of the ending point, a fitting can be performed based on each equilibrium point to obtain the fitted trajectory, which is recorded as the target trajectory.

[0109] b1) Determine the optimized target trajectory radius based on the geometry of the target trajectory.

[0110] Specifically, the curvature of the target trajectory can be determined based on its geometry. The curvature is the reciprocal of the trajectory radius. In other words, the trajectory radius can be obtained from the curvature of the target trajectory, and is denoted as the target trajectory radius.

[0111] c1) Determine the optimized target velocity based on the trajectory displacement between each target equilibrium point and the sampling time.

[0112] Specifically, since each target equilibrium point is the equilibrium point corresponding to each time sampling point, the trajectory displacement between the target equilibrium points can be obtained. By dividing this displacement by the sampling time, the optimized vehicle speed can be determined and denoted as the target speed.

[0113] d1) Use the optimized target trajectory radius and target velocity as target trajectory information.

[0114] Specifically, the optimized target trajectory radius and target velocity are used as target trajectory information.

[0115] S208. Determine the target yaw rate by dividing the target velocity in the target control quantity by the target trajectory radius.

[0116] Specifically, the target yaw rate can be obtained by dividing the target velocity in the target control variable by the target trajectory radius.

[0117] S209. Determine the target heading angular velocity based on the target yaw rate and the reference centroid sideslip rate in the second reference information.

[0118] The kinematic relationship between the heading angular velocity and the sideslip angular velocity of the center of mass is as follows:

[0119] The heading angular velocity can be obtained by combining the yaw rate with the desired reference center of mass sideslip angle.

[0120] Under quasi-static conditions, the target's heading angular velocity can be obtained by solving the above formula.

[0121] S210. Substitute the target yaw rate, target heading rate, reference center of mass sideslip rate, front wheel lateral force, and rear wheel lateral force into the nonlinear dynamic model to determine the control quantity that satisfies the second constraint condition as the target control quantity. The target control quantity includes the target front wheel steering angle and the target rear wheel longitudinal force.

[0122] Consider the following existing nonlinear programming problem:

[0123] ;

[0124] ;

[0125] ;

[0126] ;

[0127] in, This is the physical upper limit of the front wheel steering angle. This is the physical lower limit of the front wheel steering angle. This represents the physical lower limit of the longitudinal force on the rear axle. The uppermost constraint represents the physical upper limit of the longitudinal force on the rear axle, while the bottommost constraint represents the stability constraint of the controller. The reference yaw acceleration for the closest point on the trajectory, parameters The reference heading angular velocity is used. Experiments show that in this optimization problem, once the desired state derivative exceeds the feasible region of the controller, the solution time is difficult to guarantee due to inequality constraints, making it unsuitable for practical engineering needs. A better approach is to project the desired state derivative along a straight line determined by the control law onto the feasible state derivative boundary to maximize the satisfaction of the desired dynamic derivative for the current state.

[0128] To further explain, based on the feasible state derivative of the three-degree-of-freedom vehicle model, due to the underactuated coupling effect of the system, the feasible state derivative forms a surface in three-dimensional space, and at the desired angular velocity... and expected yaw acceleration Given a given condition, some parts of the surface have two... To achieve zero dynamic stability without speed control, it is necessary to make... and On a surface above the dividing line, this part is and When determined, there is a unique This represents the unique desired control quantity pair. Due to the limitation of the control quantity, there are three feasible state derivatives. —The directional angular velocity, yaw acceleration, and the spatial distribution of acceleration form a curved surface.

[0129] The dividing line is:

[0130]

[0131] By constraining the nonlinear inversion to the upper surface of the tangent space above this boundary, the velocity becomes stable, and the nonlinear inversion becomes a single solution.

[0132] Therefore, we consider using the following improved method to replace the original nonlinear optimization problem:

[0133] ;

[0134] ;

[0135] ;

[0136] ;

[0137] Through this projection, the problem of not being able to simultaneously satisfy the equations for the two state derivatives of the nonlinear inversion can be transformed into a compromise result that satisfies both equations. Furthermore, the boundary constraint is included in the process of projection onto the boundary, transforming a constrained optimization problem into a geometric solution problem and accelerating the convergence speed. The above constraint condition can be denoted as the second constraint condition, and the control quantity is solved based on the aforementioned nonlinear dynamic model.

[0138] Substituting the formulas for front axle lateral force, rear axle lateral force, front axle sideslip angle, and front and rear axle vertical loads into the nonlinear programming problem yields the control variables. .

[0139] Furthermore, the front wheel lateral force is determined by the tire magic formula, while the rear wheel lateral force is determined by the front wheel vertical load, the rear wheel vertical load, and the tire adhesion coefficient, which is determined by the vehicle's current state and the visual prediction model.

[0140] The lateral force on the front axle is calculated using the tire magic formula:

[0141] ,

[0142] in, , The coefficient of friction of the tire. B represents the vertical load on the front axle, and C represents the tire model parameters. The derivative of the front wheel slip angle. The front wheel slip angle, .

[0143] During the drift phase, the rear wheel is in a saturated state. The vertical load on the rear wheel is: The lateral force on the rear wheel is:

[0144] ;

[0145] The calculation method for the vertical load on the front and rear wheels is as follows: .

[0146] In the above technical solution, the tire adhesion coefficient adopts an existing mature estimation algorithm, especially the road adhesion coefficient estimation based on visual fusion. The estimation method not only depends on the current vehicle state, but also integrates the estimation results predicted by the visual image. Obtaining the road adhesion coefficient in this way can help us know in advance that we are about to enter a slippery or other extreme road surface, which has certain advantages for the judgment of extreme conditions of autonomous driving and drift control.

[0147] This embodiment details the steps for determining the reference trajectory information of the vehicle's reference driving trajectory, the steps for determining the optimized target trajectory information based on the first reference information in the reference trajectory information according to a pre-constructed target optimization function, and the steps for determining the target control quantity of the vehicle based on the target trajectory information and the second reference information in the reference trajectory information, combined with a pre-defined nonlinear dynamic model. From a practical engineering perspective, this technical solution fully utilizes road surface adhesion coefficient information and desired path information, proposes an improved form of nonlinear optimization problem, and provides a theoretical basis. From a practical engineering perspective, it transforms the constrained optimization problem into a geometric solution problem, accelerating the convergence speed and solving the problem that in the original optimization problem, when the desired state derivative exceeds the feasible region of the controller, the solution time is difficult to guarantee due to inequality constraints. Under the premise of ensuring vehicle stability, trajectory tracking under extreme conditions is achieved.

[0148] Example 3

[0149] Figure 3 This is a schematic diagram of a vehicle trajectory tracking and control device under extreme operating conditions provided in Embodiment 3 of the present invention. This device is applicable to situations where vehicle trajectories are tracked and controlled under extreme operating conditions. This vehicle trajectory tracking and control device under extreme operating conditions can be configured in electronic devices, such as… Figure 3 As shown, the device includes: a reference information determination module 31, a target information determination module 32, and a control quantity determination module 33; wherein,

[0150] The reference information determination module 31 is used to determine the reference trajectory information of the vehicle's reference driving trajectory when the vehicle is detected to have entered the limit control mode.

[0151] The target information determination module 32 is used to optimize the first reference information in the reference trajectory information according to the pre-built target optimization function, and determine the optimized target trajectory information. The target optimization function includes constraints on the target control quantity of the vehicle and the change of the center of gravity sideslip angle.

[0152] The control quantity determination module 33 is used to determine the target control quantity of the vehicle based on the target trajectory information and the second reference information in the reference trajectory information, combined with a preset nonlinear dynamic model, so as to perform trajectory tracking control of the vehicle based on the target control quantity.

[0153] This invention provides a vehicle trajectory tracking control device under extreme operating conditions, comprising: when a vehicle is detected to have entered an extreme control mode, firstly determining reference trajectory information of the vehicle's reference driving trajectory; then, optimizing the first reference information in the reference trajectory information according to a pre-constructed target optimization function to determine the optimized target trajectory information, the target optimization function including constraints on the vehicle's target control quantity and the change in the center of gravity sideslip angle; finally, determining the vehicle's target control quantity based on the target trajectory information and the second reference information in the reference trajectory information, combined with a preset nonlinear dynamics model, to perform trajectory tracking control of the vehicle based on the target control quantity. The above technical solution, from a practical engineering perspective, fully considers the desired reference trajectory information and utilizes an improved nonlinear optimization method, that is, using the vehicle's target control quantity and the constraints on the change in the center of gravity sideslip angle as the target optimization function to optimize the reference trajectory, thereby achieving the goal of tracking the planned trajectory as much as possible while maintaining vehicle stability, accelerating the solution speed, and meeting engineering needs.

[0154] Optionally, the reference information determination module 31 may specifically include:

[0155] Obtain the reference radius and reference speed of the reference driving trajectory;

[0156] Based on the preset vehicle model, determine the equilibrium point formula under the vehicle model;

[0157] Substituting the reference radius and reference speed into the equilibrium point formula, the reference centroid sideslip angle of the reference driving trajectory is obtained;

[0158] The reference radius, reference velocity, and reference centroid sideslip angle are used as reference trajectory information.

[0159] Optionally, the target information determination module 32 may specifically include:

[0160] The extraction unit is used to determine the first equilibrium point corresponding to the starting point of the reference trajectory and the second equilibrium point corresponding to the ending point of the reference trajectory in the first reference trajectory information.

[0161] The equilibrium point determination unit is used to perform integration processing based on the first equilibrium point and the set integration algorithm to determine at least one target equilibrium point that satisfies the minimum objective optimization function. The target equilibrium point is located between the first equilibrium point and the second equilibrium point, and the target equilibrium point is the equilibrium point corresponding to the set sampling time.

[0162] The trajectory determination unit is used to determine the optimized target trajectory information based on each target equilibrium point, the first equilibrium point, and the second equilibrium point.

[0163] Optionally, the objective optimization function consists of the rate of change of the objective control variable, the rate of change of the centroid sideslip angle, and a tradeoff matrix. The first constraint of the objective optimization function includes the physical constraints of the vehicle's front wheel steering angle, the rear wheel driving force constraints, the driving torque constraints, and the power constraints.

[0164] Optional, trajectory determination unit, specifically used for:

[0165] The target equilibrium points, the first equilibrium point, and the second equilibrium point are fitted to determine the optimized target trajectory;

[0166] The optimized target trajectory radius is determined based on the geometry of the target trajectory;

[0167] The optimized target velocity is determined based on the trajectory displacement between each target equilibrium point and the sampling time.

[0168] The optimized target trajectory radius and target velocity are used as target trajectory information.

[0169] Optionally, the control quantity determination module 33 is specifically used for:

[0170] The target yaw rate is determined by dividing the target velocity in the target control variable by the target trajectory radius.

[0171] The target heading angular velocity is determined based on the target yaw rate and the reference centroid sideslip rate in the second reference information;

[0172] Substitute the target yaw rate, target heading rate, reference center of mass sideslip rate, front wheel lateral force, and rear wheel lateral force into the nonlinear dynamic model to determine the control quantities that satisfy the second constraint condition as the target control quantities. The target control quantities include the target front wheel steering angle and the target rear wheel longitudinal force.

[0173] Optionally, the front wheel lateral force is determined by the tire magic formula, and the rear wheel steering force is determined by the front wheel vertical load, the rear wheel vertical load, and the tire adhesion coefficient, which is determined by the vehicle's current state and the visual prediction model.

[0174] The vehicle trajectory tracking control device under extreme working conditions provided in the embodiments of the present invention can execute the vehicle trajectory tracking control method under extreme working conditions provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0175] Example 4

[0176] Figure 4This is a schematic diagram of an electronic device provided in Embodiment 4 of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0177] like Figure 4 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded from storage unit 48 into the RAM 43. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0178] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0179] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as vehicle trajectory tracking control methods under extreme conditions.

[0180] In some embodiments, the vehicle trajectory tracking control method under extreme operating conditions may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the vehicle trajectory tracking control method under extreme operating conditions described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the vehicle trajectory tracking control method under extreme operating conditions by any other suitable means (e.g., by means of firmware).

[0181] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0182] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0183] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0184] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0185] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0186] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0187] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0188] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A vehicle trajectory tracking control method under extreme operating conditions, characterized in that, include: When the vehicle is detected to have entered the extreme control mode, the reference trajectory information of the vehicle's reference driving trajectory is determined. The first reference information in the reference trajectory information is optimized according to the pre-constructed target optimization function to determine the optimized target trajectory information. The target optimization function includes constraints on the target control quantity of the vehicle and the change of the center of gravity sideslip angle. The first reference information refers to the radius and reference speed of the reference path. Based on the target trajectory information and the second reference information in the reference trajectory information, and combined with a preset nonlinear dynamics model, the target control quantity of the vehicle is determined, so as to perform trajectory tracking control on the vehicle based on the target control quantity; wherein, the second reference information refers to information including at least the desired centroid sideslip angle determined based on the desired reference driving trajectory. The step of optimizing the reference trajectory information according to a pre-constructed target optimization function to determine the optimized target trajectory information includes: Determine the first equilibrium point corresponding to the start point of the reference trajectory and the second equilibrium point corresponding to the end point of the reference trajectory in the reference trajectory information; Based on the first equilibrium point, integration is performed using a set integration algorithm to determine at least one target equilibrium point that satisfies the minimum objective optimization function. The target equilibrium point is located between the first equilibrium point and the second equilibrium point, and the target equilibrium point is the equilibrium point corresponding to a set sampling time. Based on the target equilibrium points, the first equilibrium point, and the second equilibrium point, the optimized target trajectory information is determined.

2. The method according to claim 1, characterized in that, The reference trajectory information for determining the reference driving trajectory of the vehicle includes: Obtain the reference radius and reference speed of the reference driving trajectory; Based on the preset vehicle model, determine the equilibrium point formula under the vehicle model; Substituting the reference radius and reference speed into the equilibrium point formula, the reference centroid sideslip angle of the reference driving trajectory is obtained; The reference radius, reference velocity, and reference centroid sideslip angle are used as the reference trajectory information.

3. The method according to claim 1, characterized in that, The objective optimization function consists of the rate of change of the objective control variable, the rate of change of the centroid sideslip angle, and a tradeoff matrix. The first constraint of the objective optimization function includes the physical constraint of the vehicle's front wheel steering angle, the rear wheel driving force constraint, the driving torque constraint, and the power constraint.

4. The method according to claim 1, characterized in that, Based on the target equilibrium points, the first equilibrium point, and the second equilibrium point, the optimized target trajectory information is determined, including: The target equilibrium points, the first equilibrium point, and the second equilibrium point are fitted together to determine the optimized target trajectory. The optimized target trajectory radius is determined based on the geometry of the target trajectory; The optimized target velocity is determined based on the trajectory displacement between each target equilibrium point and the sampling time. The optimized target trajectory radius and target velocity are used as the target trajectory information.

5. The method according to claim 1, characterized in that, The step of determining the target control quantity of the vehicle based on the target trajectory information and the second reference information in the reference trajectory information, combined with a preset nonlinear dynamics model, includes: The target yaw rate is determined by dividing the target velocity in the target control quantity by the target trajectory radius. The target heading angular velocity is determined based on the target yaw rate and the reference centroid sideslip rate in the second reference information; Substitute the target yaw rate, the target heading rate, the reference center of mass sideslip rate, the front wheel lateral force, and the rear wheel lateral force into the nonlinear dynamic model to determine the control quantity that satisfies the second constraint condition as the target control quantity. The target control quantity includes the target front wheel steering angle and the target rear wheel longitudinal force. The second constraint condition refers to the front wheel steering angle and the rear axle longitudinal force satisfying the following conditions: subject toδmin≤δ≤δmax; Fmin≤Fxr≤Fmax; Wherein, δmax is the physical upper limit of the front wheel steering angle, δmin is the physical lower limit of the front wheel steering angle, Fmin is the physical lower limit of the rear axle longitudinal force, and Fmax is the physical upper limit of the rear axle longitudinal force.

6. The method according to claim 5, characterized in that, The front wheel lateral force is determined by the tire magic formula, and the rear wheel lateral force is determined by the front wheel vertical load, the rear wheel vertical load, and the tire adhesion coefficient, which is determined by the current state of the vehicle and the visual prediction model.

7. A vehicle trajectory tracking and control device under extreme working conditions, characterized in that, include: The reference information determination module is used to determine the reference trajectory information of the vehicle's reference driving trajectory when the vehicle is detected to have entered the limit control mode. The target information determination module is used to optimize the first reference information in the reference trajectory information according to the pre-constructed target optimization function to determine the optimized target trajectory information. The target optimization function includes constraints on the target control quantity of the vehicle and the change of the center of gravity sideslip angle. The first reference information refers to the radius and reference speed of the reference path. The control quantity determination module is used to determine the target control quantity of the vehicle based on the target trajectory information and the second reference information in the reference trajectory information, combined with a preset nonlinear dynamic model, so as to perform trajectory tracking control on the vehicle based on the target control quantity; wherein, the second reference information refers to information including at least the desired centroid sideslip angle determined based on the desired reference driving trajectory. The target information determination module may specifically include: The extraction unit is used to determine the first equilibrium point corresponding to the starting point of the reference trajectory and the second equilibrium point corresponding to the ending point of the reference trajectory in the reference trajectory information. The equilibrium point determination unit is used to perform integration processing based on the first equilibrium point and the set integration algorithm to determine at least one target equilibrium point that satisfies the minimum objective optimization function. The target equilibrium point is located between the first equilibrium point and the second equilibrium point, and the target equilibrium point is the equilibrium point corresponding to the set sampling time. The trajectory determination unit is used to determine the optimized target trajectory information based on each target equilibrium point, the first equilibrium point, and the second equilibrium point.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the vehicle trajectory tracking control method under extreme conditions as described in any one of claims 1-6.

9. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the vehicle trajectory tracking control method under extreme conditions as described in any one of claims 1-6.

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