Autonomous vehicle control methods, devices, electronic equipment and storage media

Abstract

This disclosure provides a method, apparatus, electronic device, and storage medium for controlling an unmanned vehicle, relating to the field of unmanned driving technology. The method includes: acquiring the trajectory tracking accuracy, stability index, and wheel slip ratio of the unmanned vehicle under different operating conditions; determining the front wheel steering angle and wheel torque of the unmanned vehicle under different operating conditions to track the target vehicle speed and reference trajectory information in a stable state; and controlling the unmanned vehicle's movement based on the determined front wheel steering angle and wheel torque. This disclosure comprehensively considers the vehicle's stability and tracking accuracy under different operating conditions to control the unmanned vehicle's movement, achieving integrated and coordinated control of trajectory tracking and stability to adapt to different operating conditions.

Description

Technical Field

[0001] This disclosure relates to the field of autonomous driving technology, and in particular to an autonomous vehicle control method, device, electronic equipment and storage medium. Background Technology

[0002] With the continuous development of electronic information and communication technologies and the ongoing improvement of related new infrastructure, the electrification, intelligentization, and connectivity of vehicles are accelerating. Compared with traditional vehicles, four-wheel independent drive autonomous electric vehicles have significant advantages in reducing environmental pollution, lowering energy consumption, alleviating traffic congestion, improving road utilization, reducing vehicle accidents, and enhancing vehicle safety. They provide solutions to meet people's needs for safe, comfortable, and convenient daily life, as well as their demands for efficient, energy-saving, and intelligent travel.

[0003] Autonomous vehicles are complex systems, primarily comprising four core technologies: environmental perception, behavioral decision-making, motion planning, and trajectory tracking control. Trajectory tracking control, based on road environment information and reference trajectory information obtained from the perception and planning layer, combined with the vehicle's own state, controls the vehicle's steering, acceleration, or braking to track the reference trajectory and target speed while ensuring stable vehicle operation. Therefore, as the functional module that controls the vehicle's actual actions, trajectory tracking control is crucial for ensuring the vehicle's stability, safety, and comfort.

[0004] Currently, most trajectory tracking control schemes for autonomous vehicles are based on conventional operating conditions with fixed weights at low to medium speeds on good road surfaces. They rarely consider both stability and desired speed control simultaneously, or they separate stability control from trajectory tracking control. This leads to a situation where, once the vehicle's operating conditions change, the controller struggles to comprehensively consider the weighting and priority of multiple control objectives such as stability and tracking accuracy. This results in a significant decrease in trajectory tracking accuracy and may even cause dangerous situations such as skidding and instability, making it difficult to adapt to complex and changing real-world road traffic environments. Therefore, designing a coordinated trajectory tracking and stability control method for autonomous vehicles (e.g., four-wheel-drive autonomous electric vehicles) under different operating conditions to improve their trajectory tracking accuracy, stability, and adaptability is a pressing technical problem in this field.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] This disclosure provides a method, device, electronic device, and storage medium for controlling unmanned vehicles, which at least to some extent overcomes the technical problem in related technologies of the difficulty in coordinating and controlling the trajectory tracking accuracy and stability of unmanned vehicles under different operating conditions.

[0007] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0008] According to one aspect of this disclosure, an autonomous vehicle control method is provided, the method comprising: acquiring trajectory tracking accuracy, stability index, and wheel slip ratio of the autonomous vehicle under different operating conditions; determining the front wheel steering angle and wheel torque of the autonomous vehicle in a stable state while tracking a target vehicle speed and reference trajectory information under different operating conditions based on the trajectory tracking accuracy, stability index, and wheel slip ratio of the autonomous vehicle under different operating conditions; and controlling the autonomous vehicle to drive based on the determined front wheel steering angle and wheel torque.

[0009] According to another aspect of this disclosure, an unmanned vehicle control device is also provided, comprising: a state information acquisition module for acquiring the trajectory tracking accuracy, stability index, and wheel slip ratio of the unmanned vehicle under different operating conditions; a control quantity determination module for determining the front wheel steering angle and wheel torque of the unmanned vehicle in a stable state while tracking the target vehicle speed and reference trajectory information under different operating conditions, based on the trajectory tracking accuracy, stability index, and wheel slip ratio of the unmanned vehicle under different operating conditions; and a control module for controlling the unmanned vehicle to drive based on the determined front wheel steering angle and wheel torque.

[0010] According to another aspect of this disclosure, an electronic device is also provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform any of the above-described autonomous vehicle control methods by executing the executable instructions.

[0011] According to another aspect of this disclosure, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the unmanned vehicle control method described in any of the preceding claims.

[0012] The autonomous vehicle control method, apparatus, electronic device, and computer-readable storage medium provided in the embodiments of this disclosure acquire the trajectory tracking accuracy, stability index, and wheel slip ratio of the autonomous vehicle under different operating conditions. Then, based on these parameters, the front wheel steering angle and wheel torque of the autonomous vehicle in a stable state, tracking the target vehicle speed and reference trajectory information, are determined. This allows for the control of the autonomous vehicle's movement based on the determined front wheel steering angle and wheel torque. Through these embodiments, the vehicle's stability and tracking accuracy under different operating conditions can be comprehensively considered to control its movement, achieving integrated and coordinated control of trajectory tracking and stability to adapt to different operating conditions.

[0013] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0014] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0015] Figure 1 This diagram illustrates a flowchart of an unmanned vehicle control method according to an embodiment of the present disclosure.

[0016] Figure 2 This diagram illustrates a specific implementation architecture of an autonomous vehicle control method according to an embodiment of the present disclosure.

[0017] Figure 3 This diagram illustrates a longitudinal PID motion controller according to an embodiment of the present disclosure.

[0018] Figure 4 This diagram illustrates a vehicle-path relative position model according to an embodiment of the present disclosure.

[0019] Figure 5 This diagram illustrates a seven-degree-of-freedom dual-track vehicle dynamics model according to an embodiment of the present disclosure.

[0020] Figure 6 This diagram illustrates a tire model according to an embodiment of the present disclosure.

[0021] Figure 7 This diagram illustrates a stability index design based on the tire slip angle phase plane in an embodiment of the present disclosure.

[0022] Figure 8 This diagram illustrates an adaptive adjustment curve for the weights of vehicle trajectory tracking accuracy, maneuverability, and stability in an embodiment of this disclosure.

[0023] Figure 9 This diagram illustrates an adaptive adjustment curve for the weighting of four-wheel slip ratio deviation in an embodiment of this disclosure.

[0024] Figure 10 This diagram illustrates a control device for an unmanned vehicle according to an embodiment of the present disclosure.

[0025] Figure 11 This diagram illustrates a structural block diagram of an electronic device according to an embodiment of the present disclosure;

[0026] Figure 12 A schematic diagram of a computer-readable storage medium is shown in an embodiment of the present disclosure. Detailed Implementation

[0027] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0028] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0029] The specific implementation methods of the embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0030] To address the issue of coordinating trajectory tracking accuracy and stability control of vehicles under different operating conditions, this disclosure provides an autonomous vehicle control method applicable to, but not limited to, trajectory tracking and stability coordination control of four-wheel independently driven electric autonomous vehicles.

[0031] It should be noted that the autonomous vehicle control method provided in this disclosure can be executed by any electronic device with computing power. In some embodiments, the autonomous vehicle control method provided in this disclosure can be executed by an on-board control device; in other embodiments, the autonomous vehicle control method provided in this disclosure can be executed by a remote control device; in still other embodiments, the autonomous vehicle control method provided in this disclosure can also be implemented by the on-board control device and the remote control device through interaction.

[0032] Figure 1 This diagram illustrates a flowchart of an autonomous vehicle control method according to an embodiment of the present disclosure, such as... Figure 1 As shown, the autonomous vehicle control method provided in this embodiment includes the following steps:

[0033] S102, obtain the trajectory tracking accuracy, stability index and wheel slip rate of the unmanned vehicle under different operating conditions.

[0034] It should be noted that the autonomous vehicle in this disclosure embodiment can be, but is not limited to, a four-wheel independently driven electric autonomous vehicle. The various embodiments of this disclosure use a four-wheel independently driven electric autonomous vehicle as an example for illustration. In related technologies, the control of autonomous vehicles considers stability control and trajectory tracking control separately. Once the vehicle's operating conditions change, it may lead to a decrease in trajectory tracking accuracy or dangerous situations such as sideslip and instability. The four-wheel slip ratio deviation can reflect the rolling state of the wheels. In this disclosure embodiment, by obtaining the trajectory tracking accuracy, stability index, and wheel slip ratio of the autonomous vehicle under different operating conditions, these control objectives can be integrated to achieve control of the autonomous vehicle.

[0035] S104. Based on the trajectory tracking accuracy, stability index, and wheel slip ratio of the unmanned vehicle under different operating conditions, determine the front wheel steering angle and wheel torque of the unmanned vehicle when it is driving in a stable state by tracking the target vehicle speed and reference trajectory information under different operating conditions.

[0036] In specific implementation, the above S104 can be achieved through the following steps: construct an objective function with the goals of trajectory tracking accuracy, stability index and wheel slip rate; determine the constraints of the objective function; and determine the front wheel steering angle and wheel torque of the unmanned vehicle in a stable state to track the target vehicle speed and reference trajectory information under different operating conditions based on the objective function and constraints.

[0037] In some embodiments, the objective function is:

[0038]

[0039] in,

[0040] u = [δ f T fl T fr T rl T rr s v s r ] T (2)

[0041]

[0042]

[0043] Where J represents the value of the objective function; k represents time; N p Represents the prediction time domain; η k η represents the actual output at time k; k ref Represents the reference output at time k; u k V represents the actual control quantity at time k; k u represents the reference control quantity at time k; k-1 The actual control quantity at time k-1 is represented; Q, S, R, and P represent weighting coefficients; δ f Indicates the front wheel steering angle; T fl Indicates the longitudinal drive or braking torque of the left front wheel; T fr Indicates the longitudinal drive or braking torque of the right front wheel; T rl Indicates the longitudinal drive or braking torque of the left rear wheel; T rr Indicates the longitudinal driving or braking torque of the right rear wheel; s v and s r V represents a slack variable; y yaw rate represents lateral velocity; r represents yaw rate; e represents lateral velocity. y Indicates lateral displacement deviation; Indicates the deviation of the heading angle; e wfl Indicates the deviation of the slip ratio of the left front wheel; e wfr Indicates the slip ratio deviation of the right front wheel; e wrl Indicates the deviation of the left rear wheel slip ratio; e wrr This indicates the slip ratio deviation of the right rear wheel; the first element in V represents the reference front wheel steering angle, with a value of 0; T all Indicates the total driving or braking torque; F zf Indicates the vertical load on the front axle; F zr This indicates the vertical load on the rear axle.

[0044] In some embodiments of this disclosure, the constraints on the optimization of the objective function may include:

[0045] Constraints on front wheel steering angle:

[0046] δ fmin ≤δ f ≤δ fmax (5)

[0047] Constraints on the longitudinal driving or braking torque of the four tires:

[0048]

[0049] Constraints on the front wheel steering angle increment:

[0050] -Δδ fmax ≤Δδ f ≤Δδ fmax (7)

[0051] Constraints on the longitudinal driving or braking torque increment of the four tires:

[0052] -ΔT ij,max ≤ΔT ij ≤ΔT ij,max (8)

[0053] Safety phase plane constraint conditions:

[0054] M|ξ k |≤E+s k (9)

[0055] in,

[0056] M = [M1|0 2×8 ], E = [α] sat r sat ],

[0057] s k ≥0, and s k =su k ,s=[0 2×5 |I 2×2 ];

[0058] Where, δ f Indicates the front wheel steering angle; δ fmin δ represents the minimum front wheel steering angle. fmax T represents the maximum value of the front wheel steering angle. ij The longitudinal driving or braking torque of the four wheels is represented by ij = fl, fr, rl, rr, which represent the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively; T max This indicates the maximum driving or braking torque output by the motor; F represents the maximum longitudinal force on all four wheels.yij This represents the lateral force on the four wheels; R represents the maximum lateral force on all four wheels. e Indicates the effective radius of the wheel; Δδ f Indicates the front wheel steering angle increment; Δδ fmax ΔT represents the maximum value of the front wheel steering angle increment. ij ΔT represents the longitudinal driving or braking torque increment of the four wheels. ij,max ξ represents the maximum value of the longitudinal driving or braking torque increment of the four wheels; M represents the construction matrix related to the safety phase plane constraints; k Represents the discrete state quantity; v y yaw rate represents lateral velocity; r represents yaw rate; e represents lateral velocity. y Indicates lateral displacement deviation; Indicates the deviation of the heading angle; e wfl Indicates the deviation of the slip ratio of the left front wheel; e wfr Indicates the slip ratio deviation of the right front wheel; e wrl Indicates the deviation of the left rear wheel slip ratio; e wrr Indicates the slip ratio deviation of the right rear wheel; α f Indicates the front axle wheel slip angle; α r Represents the rear axle wheel slip angle; E represents the curvature factor; v x Indicates longitudinal vehicle speed; l r α represents the distance from the vehicle's center of gravity to the rear axle. sat Indicates the saturation value of the rear wheel slip angle; β sat Indicates the centroid sideslip angle; r sat Indicates yaw rate; s k s represents the slack variable; s represents the coefficient; u k I represents the discrete control quantity; I represents the identity matrix.

[0059] In some embodiments, the weighting coefficient of the output quantity is adaptively adjusted using the following formula:

[0060]

[0061] in,

[0062]

[0063] Where Q represents the matrix symbol for the output weight coefficients; Indicates lateral velocity v y Weighting coefficients; Q r The weighting coefficient representing the yaw rate r; Indicates the lateral displacement deviation e y Weighting coefficients; Indicates heading angle deviation Weighting coefficients; The weighting coefficients representing the slip ratio deviations of the four wheels are ij = fl, fr, rl, rr, representing the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively; ε represents the stability index; κ ij The slip ratios of the four wheels are: s1 = 400; s2 = 5; s3 = 400; s4 = 0.65; s5 = 500; s6 = 0.65; s7 = 500; s8 = 0.67; a1 = 1.5; a2 = 1.5; b1 = 1.2; b2 = 1.2; a'1 = 30; b'1 = 3; c'1 = 0.5; range = 0.15.

[0064] It should be noted that the values ​​of s1, s2, s3, s4, s5, s6, s7, s8, a1, a2, b1, b2, a'1, b'1, c'1, and range in this embodiment are a set of reference values ​​given based on the debugging experience of the system itself. Due to the different structural parameters of the controlled object, in actual use, those skilled in the art can make adjustments according to the characteristics of their own system in order to achieve better control results.

[0065] In some embodiments, the weighting coefficient of the control quantity is adaptively adjusted using the following formula:

[0066]

[0067] in,

[0068]

[0069] The weighting coefficient of the control increment is adaptively adjusted using the following formula:

[0070]

[0071] Where R represents the matrix symbol for the control quantity weighting coefficient; The weighting coefficient representing the front wheel steering angle; The weighting coefficient representing the longitudinal driving or braking torque of the left front wheel; The weighting coefficient representing the longitudinal driving or braking torque of the right front wheel; The weighting coefficient representing the longitudinal driving or braking torque of the left rear wheel; The weighting coefficient representing the longitudinal driving or braking torque of the right rear wheel; and This represents the weighting coefficient corresponding to the increment of the slack variable; S represents the matrix symbol for the weighting coefficient of the increment of the control variable. The weighting coefficient representing the increase in front wheel steering angle; The weighting coefficient representing the increase in longitudinal driving or braking torque of the left front wheel; The weighting coefficient representing the increase in longitudinal driving or braking torque of the right front wheel; The weighting coefficient representing the increase in longitudinal driving or braking torque of the left rear wheel; The weighting coefficients represent the increase in longitudinal driving or braking torque of the right rear wheel; a'2 = 15; b'2 = 3; c'2 = 0.6; rangeR = 300.

[0072] It should be noted that the values ​​of a'2, b'2, c'2 and rangeR in this embodiment are a set of reference values ​​given based on the debugging experience of the system itself. Due to the different structural parameters of the controlled object, in actual use, those skilled in the art can make adjustments according to the characteristics of their own system in order to achieve better control results.

[0073] In some embodiments, the weighting coefficients of the slack variables are adaptively adjusted using the following formula:

[0074] P = [0 1×5 σ v P σ r P (15)

[0075] Where P represents the matrix symbol; Represents the slack variable s v Weighting coefficients; Represents the slack variable s r The weighting coefficients.

[0076] S106 controls the driving of the unmanned vehicle based on the determined front wheel angle and wheel torque.

[0077] It should be noted that the control quantities for controlling the autonomous vehicle's movement are mainly the front wheel steering angle and wheel torque. The wheel torque here includes the longitudinal driving or braking torque of each wheel. The front wheel steering angle and wheel torque used in S106 above are determined by comprehensively considering the autonomous vehicle's trajectory tracking accuracy, stability indicators, and wheel slip ratio under different operating conditions. Therefore, the autonomous vehicle control method provided in this embodiment can ensure that the vehicle tracks the target speed and reference trajectory in a stable state under different operating conditions, satisfying both the vehicle's stability requirements and its trajectory tracking accuracy.

[0078] As can be seen from the above, the autonomous vehicle control method provided in this disclosure obtains the trajectory tracking accuracy, stability index, and wheel slip ratio of the autonomous vehicle under different operating conditions. Then, based on these parameters, it determines the front wheel steering angle and wheel torque of the autonomous vehicle to track the target vehicle speed and reference trajectory information in a stable state under different operating conditions. This allows for the control of the autonomous vehicle's movement based on the determined front wheel steering angle and wheel torque. Through this disclosure, the vehicle's stability and tracking accuracy under different operating conditions can be comprehensively considered to control the autonomous vehicle's movement, achieving integrated and coordinated control of trajectory tracking and stability to adapt to different operating conditions.

[0079] In some embodiments, the autonomous vehicle control method provided in this disclosure may further include the following steps: acquiring the target speed and reference trajectory information of the autonomous vehicle; determining the front wheel steering angle of the autonomous vehicle to track the target speed and reference trajectory information based on the target speed and reference trajectory information; determining the total driving or braking torque required for the autonomous vehicle to track the target speed based on the target speed and actual speed of the autonomous vehicle; distributing the total driving or braking torque required for the autonomous vehicle to track the target speed to each wheel according to the vertical load distribution of the front and rear axles of the autonomous vehicle, thereby obtaining the longitudinal driving or braking torque of each wheel; and constraining the longitudinal driving or braking torque of each wheel according to the wheel slip ratio of the autonomous vehicle under different operating conditions.

[0080] Furthermore, in some embodiments, the total driving or braking torque required for the autonomous vehicle to track the target vehicle speed can be determined by the following formula:

[0081] T des =k1(v x -v xdes )+k2∫(v x -v xdes )dt (16)

[0082] Among them, T des Indicates the total driving or braking torque; v x Indicates actual vehicle speed; v xdes k1 represents the target vehicle speed; k2 represents the proportional coefficient; k2 represents the integral coefficient.

[0083] Furthermore, in some embodiments, the autonomous vehicle control method provided in this disclosure can limit the total driving or braking torque required for the autonomous vehicle to track the target vehicle speed through the following steps: obtaining the maximum driving or braking torque output by the motor on the autonomous vehicle; and limiting the total driving or braking torque required for the autonomous vehicle to track the target vehicle speed based on the maximum driving or braking torque output by the motor on the autonomous vehicle.

[0084] In some embodiments, the total driving or braking torque required for the autonomous vehicle to track the target vehicle speed is limited using the following formula:

[0085]

[0086] Among them, T all T represents the total driving or braking torque after amplitude limiting; des T represents the total driving or braking torque before the amplitude limiting process; max This indicates the maximum driving or braking torque output by a single motor; n represents the number of motors that output driving or braking torque.

[0087] In some embodiments, the autonomous vehicle control method provided in this disclosure may further include the following steps: obtaining the lateral displacement deviation and heading angle deviation of the autonomous vehicle tracking the target vehicle speed and reference trajectory information under different operating conditions; and determining the trajectory tracking accuracy of the autonomous vehicle tracking the target vehicle speed and reference trajectory information under different operating conditions based on the lateral displacement deviation and heading angle deviation of the autonomous vehicle tracking the target vehicle speed and reference trajectory information under different operating conditions.

[0088] In some embodiments, the autonomous vehicle control method provided in this disclosure may further include the following steps: acquiring the lateral speed, yaw rate, and front and rear axle wheel sideslip angles of the autonomous vehicle under different operating conditions; and determining the stability index of the autonomous vehicle tracking the target speed and reference trajectory information under different operating conditions based on the lateral speed, yaw rate, and front and rear axle wheel sideslip angles of the autonomous vehicle under different operating conditions.

[0089] In some embodiments, the stability index of an autonomous vehicle tracking target speed and reference trajectory information under different operating conditions is determined by the following formula:

[0090]

[0091] Where, R1=max(|α f,sat |,|α r,sat | indicates the larger of the front wheel saturated sideslip angle and the rear wheel saturated sideslip angle; (α f,s ,α r,s () indicates the coordinates of the saddle point closer to the origin; This indicates the distance from the current position of the autonomous vehicle to the origin.

[0092] In some embodiments, the method further includes: determining lateral velocity, yaw rate, lateral displacement deviation, heading angle deviation, wheel slip ratio, and front and rear axle wheel sideslip angles as state variables, and establishing a state-space expression; and adaptively adjusting the weighting coefficients of lateral velocity, yaw rate, and tracking deviation based on the vehicle stability state of the unmanned vehicle under different operating conditions.

[0093] Furthermore, in some embodiments, the state-space expression is established as follows:

[0094]

[0095] in,

[0096]

[0097] in, ξ represents the rate of change of the state variable; u represents the control variable; f() represents the functional relationship between the rate of change of the state variable and the state variable and the control variable; η represents the output variable; h() represents the functional relationship between the output variable and the state variable; α f Indicates the front axle wheel slip angle; α r This indicates the wheel slip angle of the rear axle.

[0098] Furthermore, in some embodiments, the weighting coefficients of lateral vehicle speed, yaw rate, and tracking deviation can be adaptively adjusted through the following steps: if the autonomous vehicle is in a stable state, the weighting coefficient of lateral vehicle speed is decreased, and the weighting coefficients of yaw rate and tracking deviation are increased; if the autonomous vehicle is in an unstable state, the weighting coefficient of lateral vehicle speed is increased, and the weighting coefficients of yaw rate and tracking deviation are decreased.

[0099] Figure 2 This disclosure illustrates a specific implementation architecture of an autonomous vehicle control method in an embodiment. This architecture can be applied, but is not limited to, trajectory tracking and stability coordination control of four-wheel independent drive electric autonomous vehicles under different operating conditions, such as... Figure 2 As shown, the overall idea is: first, based on the target vehicle speed v obtained from the upper-level motion planning layer... xref and reference trajectory information Y ref and In addition to real-time vehicle status information, a PID controller is designed to track changes in the target vehicle speed to obtain the required total driving torque T. allThis refers to the driver's total torque requirement. Then, the total driving torque is distributed according to the vertical load distribution between the front and rear axles to obtain the reference four-wheel torque value T. ref Subsequently, an integrated controller based on MPC was designed. Lateral displacement deviation and heading angle deviation, representing tracking accuracy, lateral velocity and yaw rate, describing the vehicle's yaw dynamics, and four-wheel slip ratio deviation, representing tire rolling state, were selected as state variables. Furthermore, to further ensure vehicle stability, front and rear axle tire slip angles were chosen as state variables. A stability evaluation index designed based on the phase plane of the front and rear wheel slip angles was used to judge the vehicle's stability state in real time. A weighted adaptive adjustment strategy for trajectory tracking accuracy, handling, and stability objectives based on this stability evaluation index was designed. According to real-time tire slip ratio information, a hyperbolic function was introduced to adjust the weights of the four-wheel slip ratio deviations accordingly, determining the corresponding steering angle and torque to ensure stable vehicle operation while tracking the reference trajectory and target speed.

[0100] In practice, the following steps may be included:

[0101] 1) Establish a PI longitudinal motion controller to calculate the total driving or braking torque required for the autonomous vehicle to track the target vehicle speed and reference trajectory information:

[0102] like Figure 3 As shown, based on the deviation between the current vehicle speed (actual vehicle speed) and the desired vehicle speed (target vehicle speed), the total driving torque required to track the desired vehicle speed is calculated using the proportional-integral principle. The specific calculation formula is shown in formula (16), which will not be elaborated here.

[0103] Next, based on the maximum drive or braking torque T that a single motor can output... max The total driving or braking torque is limited to obtain the actual total driving or braking torque T. all for:

[0104]

[0105] Subsequently, based on the load distribution between the front and rear axles, the total driving / braking torque T is... all The distribution is made so that the left and right wheels are evenly distributed, and the control quantity V is used as a reference, as shown in the above formula (4).

[0106] 2) Establish an integrated controller based on MPC to achieve coordinated control of trajectory tracking and stability:

[0107] Combination Figure 4 The vehicle-path relative position model shown and Figure 5 The seven-DOF dual-track vehicle dynamics model shown below provides a detailed explanation of the process for establishing the prediction model:

[0108] A1. Establish the state-space expression:

[0109] The derivatives of lateral displacement and heading angle deviation are calculated based on the vehicle-path relative position model. The calculation formulas are as follows:

[0110]

[0111]

[0112] The calculation formulas for lateral vehicle speed, yaw rate, deviation of four-wheel slip ratio, and sideslip angle change rate based on the seven-degree-of-freedom vehicle dynamics model are as follows:

[0113]

[0114]

[0115] Where Mz is the external yaw moment generated by the longitudinal forces of the four wheels around the vehicle's center of gravity, specifically expressed as:

[0116]

[0117] Where κ(ρ) is the road curvature; m is the vehicle mass; I z Let v be the moment of inertia of the vehicle about the z-axis. x v y These represent the longitudinal and lateral vehicle speeds, respectively; r is the yaw rate; δ is the front wheel steering angle; and F... xij ,F yij These represent the longitudinal force and lateral force of the tire, respectively, where the subscripts ij = fl, fr, rl, rr represent the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively; l a l b denoted as , respectively, are the distances from the center of gravity to the front and rear axles, and c is the vehicle track width.

[0118] The derivative of the four-wheel slip ratio deviation is:

[0119]

[0120] The desired tire rotational angular velocity is:

[0121]

[0122] The desired tire rotational angular acceleration is:

[0123]

[0124] Where, ζ r =-ζ l =1;

[0125] The actual angular acceleration of the tire is:

[0126]

[0127] Where, ω ij T is the actual angular velocity of the tire. ij For the torque of four wheels, I z R is the moment of inertia. e The effective radius of the wheel;

[0128] The slip angle of four tires can be written as:

[0129]

[0130] Where, ζ f =-ζ r =1,δ f =δ,δ r =0;

[0131] Differentiating the tire slip angle, we get:

[0132]

[0133] in,

[0134]

[0135] After the above formula derivation, the state-space expression shown in formula (19) can be established.

[0136] To ensure stable vehicle operation while better tracking of the reference trajectory, the determined state variables are as shown in the above formula (20); the selected control variables are as shown in the above formula (2); the selected output variables are as shown in the above formula (3); and the selected disturbance variables are as shown in formula (22).

[0137]

[0138] Among them, a x For longitudinal acceleration, The longitudinal forces of the four wheels are estimated.

[0139] A2. Linearize the state-space expression to obtain a linear time-varying system:

[0140] The force applied to the tires plays a crucial role in the vehicle's handling stability and ride comfort. In the embodiments of this disclosure... Figure 6 The UniTire tire model shown describes the forces acting on a tire under different operating conditions. This model can uniformly express the longitudinal slip and lateral slip characteristics of a tire under both pure and combined operating conditions. Specifically, the expression is as follows:

[0141]

[0142] in, F is the dimensionless total tangential force. x and F y These represent the actual longitudinal and lateral forces, respectively; E is the curvature factor; μ is the directional friction coefficient; F z For vertical loads; φ x φ y φ and φ represent the relative longitudinal, lateral, and combined slip ratios, respectively, and are defined as:

[0143]

[0144] Among them, K x K y These are the longitudinal slip and lateral stiffness of the tire; F z It is a vertical load; μ x μ y These are the longitudinal and lateral friction coefficients, S. x and S y These are the tire's longitudinal slip ratio and lateral slip ratio, calculated using the following formulas:

[0145]

[0146] Among them, R e V is the effective rolling radius; sx and V sy These represent the longitudinal and lateral slip velocities, respectively.

[0147] Substituting the aforementioned tire forces into the state-space equations yields a nonlinear vehicle dynamics model, which serves as the prediction model.

[0148] To meet the real-time requirements of the controller in practical applications, the model (state-space expression) must be appropriately linearized. Based on the Taylor expansion formula, a Taylor expansion is performed at the operating point (ξ0(k), u0(k), w0(k)), retaining only the first term and ignoring all higher-order terms, resulting in the following linear time-varying system:

[0149]

[0150] Where A(t) represents the coefficient matrix of the state variables, B(t) represents the coefficient matrix of the control variables, and D(t) represents the coefficient matrix of the disturbance variables;

[0151] A3. Discretize the state-space expression using the first-order difference quotient method to obtain the discretized state-space expression as follows:

[0152] ξ(k+1)=A(k)ξ(k)+B(k)u(k)+D(k)w(k) (39)

[0153] In the formula, ξ(k) represents the discrete state quantity, u(k) represents the discrete control quantity, A(k) represents the discrete state quantity coefficient matrix, satisfying A(k)=I+A(t)T, B(k) and D(k) represent the discrete control quantity and disturbance quantity coefficient matrices, respectively, satisfying B(k)=B(t)T and D(k)=D(t)T; where I represents the identity matrix; T represents the sampling period.

[0154] Due to the large dimension of the system matrix, in order to reduce the huge amount of computation in manual differentiation and reduce the error rate, the Jacobian function of MATLAB is used to solve the coefficient matrices A(k), B(k), and D(k) of state variables, control variables, and disturbance variables.

[0155] A4. Calculate the expected reference output:

[0156] Using the current vehicle speed and the front wheel steering angle determined by the controller at the previous moment as inputs, the desired yaw rate is calculated through a two-degree-of-freedom vehicle model as follows:

[0157]

[0158] In the formula, r des Let r0 and r be the desired yaw rate. max These are the target yaw rate and its maximum value, respectively, expressed as follows:

[0159]

[0160]

[0161] Where μ is the road surface adhesion coefficient; C r For rear wheel stiffness; C f For front wheel stiffness; l f The distance from the vehicle's center of gravity to the front axle; r This is the distance from the vehicle's center of gravity to the rear axle.

[0162] When the sideslip angle β is small, the desired lateral speed is defined as the actual lateral speed of the vehicle. Once the sideslip angle increases and exceeds a certain threshold, the desired lateral speed is set to zero, i.e.:

[0163]

[0164] The centroid sideslip angle threshold is: β max =atan(0.02μg), the expected value of the lateral displacement and heading angle deviation, which characterize the tracking accuracy, is 0, that is:

[0165] To maximize tire rolling speed, the desired rotational speed is set to the speed at which the tires roll perfectly. This controls both vehicle handling stability and wheel slip ratio to prevent excessive wheel spin. Therefore, the expected value for the four-wheel slip ratio deviation is 0, i.e., e. wij,des =0.

[0166] Therefore, the overall reference output is:

[0167]

[0168] Where, η ref Indicates the expected reference output; v ydes Indicates the desired lateral velocity; r des e represents the desired yaw rate. ydes This represents the expected lateral displacement deviation; Indicates the desired heading angle deviation; e wfl,des This indicates the expected deviation in the left front wheel slip ratio; e wfr,des Indicates the expected deviation of the right front wheel slip ratio; e wrl,des This indicates the expected deviation in the left rear wheel slip ratio; e wrr,des This represents the expected deviation in the right rear wheel slip ratio.

[0169] A5. Establish the objective function:

[0170] Assuming the current time is k, to calculate the front wheel angle and four-wheel torque required for the vehicle to quickly and smoothly follow the reference trajectory, an objective function is established as shown in the above formula (1). The first term is a penalty for the deviation between the actual output and the reference output, which is used to ensure the trajectory tracking accuracy and vehicle stability. The second term is a penalty for the deviation between the front wheel angle amplitude and the actual torque output and the reference value, which prevents the angle from being too large and makes the torque decided by the controller under normal working conditions track the driver's expected value as much as possible, so as to better achieve the tracking of the expected vehicle speed. The third term is a penalty for the increment of the control quantity, which ensures the smooth change of the actuator. The fourth term is a penalty for the relaxation variable of the safety phase plane, which ensures the feasibility of the controller solution.

[0171] A6. Consider the relevant constraints:

[0172] First, considering the physical constraints and friction ellipse constraints of the actuator, the front wheel steering angle and the torque range of the four wheels are given, as shown in the above formulas (5) and (6).

[0173] To ensure smooth actuator changes and improve overall vehicle comfort and control performance, the control increment is constrained, and the constraint formulas are shown in formulas (7) and (8) above.

[0174] To ensure vehicle stability, the centroid sideslip angle β is obtained based on the peak sideslip angle. e and yaw rate r e The safety phase plane constraint is designed as shown in the above formula (9), and will not be repeated here.

[0175] A7. Calculation of Vehicle Stability Evaluation Indicators:

[0176] The quantitative stability evaluation index based on the front and rear wheel side slip angle phase plane is adopted, as shown in formula (18).

[0177] Combination Figure 7 From the perspective of stability index design based on the tire slip angle phase plane, region 3 represents the stable region, which is a circle with radius R1 centered at the origin, where R1 = max(|α f,sat |,|α r,sat | indicates the larger value of the saturated sideslip angle of the front or rear wheels. In this region, the vehicle exhibits better stability, with trajectory tracking accuracy and handling as the primary control objectives. Region 2 represents the transition region, which is a circle centered at the origin with a radius of [missing information]. The circle represents a region where the front and rear wheel slip angles of the vehicle have reached significant values, with some exceeding saturation values. Therefore, the vehicle is susceptible to instability when its state is within this region. Region 1 is defined as the unstable region, centered at the origin with a radius of [missing information]. a circle, in which point (α) f,s ,α r,s The coordinates of the saddle point closer to the origin are indicated. When the sideslip angle of the front or rear wheels enters a severely sloping area, the vehicle is prone to losing steering ability or "fishtailing," which is an unstable area.

[0178] This indicates the distance from the current vehicle's actual position to the origin.

[0179] When ε∈(0,1], the vehicle state is stable, and the larger the value of ε, the higher the stability. When ε∈(-1,0], the vehicle state is in the transition region, and the vehicle may become unstable. When ε∈[-2,-1], the vehicle is in the extreme state and is about to become unstable.

[0180] A8. Weight adaptive adjustment strategy based on quantitative stability evaluation index:

[0181] The trajectory tracking accuracy is similar to the vehicle's maneuverability control objective, while maneuverability and stability control objectives are usually contradictory. Therefore, in order to better meet the weight priorities of the vehicle's control objectives such as trajectory tracking accuracy, maneuverability, and stability under different operating conditions, a set of adaptive weight adjustment strategies based on stability evaluation indicators and adaptive weight adjustment of hyperbolic tangent slip ratio deviation based on four-wheel slip ratio are designed, as shown in the above formula (11).

[0182] Combination Figure 8 From the adaptive weighting graph of tracking accuracy, handling, and stability, when ε≥0.2, the vehicle is in a stable state, with trajectory tracking accuracy and vehicle handling as the main control objectives. Therefore, the weight of lateral speed is relatively small, while the weights of yaw rate and tracking deviation are relatively large. When ε<0.2, as ε decreases, the vehicle's stability gradually deteriorates. Therefore, the weight of lateral speed is gradually increased, while the weights of yaw rate and tracking deviation are correspondingly decreased, thus achieving adaptive adjustment. In this embodiment, a hyperbolic function is introduced to adaptively adjust the weighting of the four-wheel slip ratio deviation, combined with... Figure 9 As shown by the curve, when the slip ratio is small, a small weight is assigned; as the slip ratio increases, the weight is increased rapidly to constrain the slip ratio within a small range, prevent wheel slippage, and further improve vehicle driving safety.

[0183] Therefore, the final output weights are:

[0184]

[0185] The control quantity weights are adaptively adjusted based on the torque increment, as shown in the above formula (13); the control quantity weights are shown in the above formula (12); the control increment weights are shown in the above formula (14); and the slack variable weights are shown in the above formula (15).

[0186] A9. Optimization solution of the objective function:

[0187] Based on the objective function of formula (4), and taking into account both actuator constraints and safety phase plane constraints, the solution is optimized using the qpOASESsolver, i.e.:

[0188]

[0189] Solving the above objective function yields a series of control input increments and slack variables in the control time domain:

[0190]

[0191] Where Δu represents the control increment; This represents the increment of the control quantity at time t; This represents the increment of the control quantity at time t+1; Represents t+N c The control increment at time -1; σ v and σ r This represents a slack variable.

[0192] The final control quantity is obtained by summing the first element of the above control sequence with the control quantity corresponding to the previous time step.

[0193] A10. Repeat steps A1-A9 at time t+1, and continue this rolling optimization to achieve tracking of the reference trajectory.

[0194] As can be seen from the above, the unmanned vehicle control method provided in this embodiment adopts an integrated control method based on MPC, which comprehensively considers the trajectory tracking accuracy, vehicle stability control objectives, and four-wheel slip ratio control objectives. By using stability evaluation indicators based on the front and rear wheel side slip angle phase plane design, the vehicle's operating state is judged, and the weights of multiple control objectives of MPC are adaptively adjusted in real time accordingly. Finally, the corresponding steering angle and four-wheel torque are determined, realizing integrated coordinated control of trajectory tracking and stability to adapt to different operating conditions.

[0195] Compared with existing trajectory tracking controllers, the autonomous vehicle control method provided in this disclosure has the following advantages:

[0196] 1) A coordinated control method for trajectory tracking and stability of four-wheel independent drive unmanned electric vehicles for different operating conditions can ensure that the vehicle has high stability while tracking the reference trajectory.

[0197] 2) An integrated controller for different operating conditions was designed based on the model predictive control algorithm. It can comprehensively consider the trajectory tracking accuracy, vehicle stability and four-wheel slip ratio control objectives under different operating conditions, as well as actuator physical constraints, friction ellipse constraints and phase plane safety constraints, and decide on the relatively optimal control quantity, which has good comprehensive control performance.

[0198] 3) Based on the quantitative stability evaluation index, a set of weight adaptive adjustment strategies for different control objectives was designed to coordinate the weight priority of trajectory tracking accuracy and vehicle handling objectives and vehicle stability objectives under different operating conditions. A hyperbolic function was introduced to adaptively adjust the weight of the four-wheel slip ratio deviation to prevent tire slippage and further improve vehicle safety performance. It has good adaptability and robustness to different operating conditions.

[0199] 4) Based on the yaw rate and center of gravity sideslip angle corresponding to the rear wheel sideslip angle saturation, phase plane safety constraints are designed to further ensure vehicle stability; and by introducing relaxation variables, it is ensured that the controller can still find a feasible solution under extreme conditions, that is, the vehicle is allowed to temporarily exceed the safety constraint boundary slightly, so as to ensure the overall vehicle control performance and prevent vehicle loss of control due to controller solution failure.

[0200] Based on the same inventive concept, this disclosure also provides an unmanned vehicle control device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the method embodiments described above, the implementation of this device embodiment can refer to the implementation of the method embodiments described above, and repeated details will not be repeated.

[0201] Figure 10 This diagram illustrates a control device for an unmanned vehicle according to an embodiment of the present disclosure, such as... Figure 10 As shown, the device includes: a status information acquisition module 1001, a control quantity determination module 1002, and a control module 1003.

[0202] The system includes a state information acquisition module 1001, which acquires the trajectory tracking accuracy, stability index, and wheel slip ratio of the unmanned vehicle under different operating conditions; a control quantity determination module 1002, which determines the front wheel angle and wheel torque of the unmanned vehicle under different operating conditions based on the trajectory tracking accuracy, stability index, and wheel slip ratio of the unmanned vehicle to track the target vehicle speed and reference trajectory information in a stable state; and a control module 1003, which controls the unmanned vehicle to drive based on the determined front wheel angle and wheel torque.

[0203] It should be noted that the aforementioned status information acquisition module 1001, control quantity determination module 1002, and control module 1003 correspond to S102 to S106 in the method embodiment. The examples and application scenarios implemented by these modules and their corresponding steps are the same, but they are not limited to the content disclosed in the above method embodiment. It should also be noted that these modules, as part of the apparatus, can be executed in a computer system, such as a set of computer-executable instructions.

[0204] In some embodiments, based on the trajectory tracking accuracy, stability index, and wheel slip ratio of the autonomous vehicle under different operating conditions, the front wheel steering angle and wheel torque of the autonomous vehicle in a stable state while tracking the target vehicle speed and reference trajectory information are determined, including: constructing an objective function with trajectory tracking accuracy, stability index, and wheel slip ratio as objectives; determining the constraints of the objective function; and determining the front wheel steering angle and wheel torque of the autonomous vehicle in a stable state while tracking the target vehicle speed and reference trajectory information under different operating conditions based on the objective function and constraints.

[0205] In some embodiments, the control quantity determination module 1002 is further configured to: acquire the target speed and reference trajectory information of the unmanned vehicle; determine the front wheel steering angle of the unmanned vehicle in tracking the target speed and reference trajectory information based on the target speed and reference trajectory information; determine the total driving or braking torque required for the unmanned vehicle to track the target speed based on the target speed and actual speed of the unmanned vehicle; distribute the total driving or braking torque required for the unmanned vehicle to track the target speed to each wheel based on the vertical load distribution of the front and rear axles of the unmanned vehicle, thereby obtaining the longitudinal driving or braking torque of each wheel; and constrain the longitudinal driving or braking torque of each wheel based on the wheel slip ratio of the unmanned vehicle under different operating conditions.

[0206] In some embodiments, the control quantity determination module 1002 is further configured to: obtain the maximum driving or braking torque output by the motor on the unmanned vehicle; and limit the total driving or braking torque required for the unmanned vehicle to track the target vehicle speed based on the maximum driving or braking torque output by the motor on the unmanned vehicle.

[0207] In some embodiments, the control quantity determination module 1002 is further configured to: acquire the lateral displacement deviation and heading angle deviation of the unmanned vehicle tracking the target vehicle speed and reference trajectory information under different operating conditions; and determine the trajectory tracking accuracy of the unmanned vehicle tracking the target vehicle speed and reference trajectory information under different operating conditions based on the lateral displacement deviation and heading angle deviation of the unmanned vehicle tracking the target vehicle speed and reference trajectory information under different operating conditions.

[0208] In some embodiments, the control quantity determination module 1002 is further configured to: acquire the lateral speed, yaw rate and front and rear axle wheel sideslip angles of the unmanned vehicle under different operating conditions; and determine the stability index of the unmanned vehicle tracking the target speed and reference trajectory information under different operating conditions based on the lateral speed, yaw rate and front and rear axle wheel sideslip angles of the unmanned vehicle under different operating conditions.

[0209] In some embodiments, the control quantity determination module 1002 is further configured to: determine lateral speed, yaw rate, lateral displacement deviation, heading angle deviation, wheel slip ratio and front and rear axle wheel sideslip angles as state variables, and establish a state space expression; and adaptively adjust the weighting coefficients of lateral speed, yaw rate and tracking deviation according to the vehicle stability state of the unmanned vehicle under different operating conditions.

[0210] In some embodiments, the control quantity determination module 1002 is further configured to: if the autonomous vehicle is in a stable state, decrease the weighting coefficient of the lateral speed and increase the weighting coefficient of the yaw angle and the tracking deviation; if the autonomous vehicle is in an unstable state, increase the weighting coefficient of the lateral speed and decrease the weighting coefficient of the yaw angle and the tracking deviation.

[0211] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0212] The following reference Figure 11 To describe an electronic device 1100 according to such an embodiment of the present disclosure. Figure 11 The electronic device 1100 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0213] like Figure 11 As shown, the electronic device 1100 is manifested in the form of a general-purpose computing device. The components of the electronic device 1100 may include, but are not limited to: at least one processing unit 1110, at least one storage unit 1120, and a bus 1130 connecting different system components (including storage unit 1120 and processing unit 1110).

[0214] The storage unit stores program code that can be executed by the processing unit 1110, causing the processing unit 1110 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure. For example, the processing unit 1110 can perform the following steps of the above method embodiments: obtaining the trajectory tracking accuracy, stability index, and wheel slip ratio of the unmanned vehicle under different operating conditions; determining the front wheel angle and wheel torque of the unmanned vehicle in a stable state while tracking the target vehicle speed and reference trajectory information under different operating conditions based on the trajectory tracking accuracy, stability index, and wheel slip ratio of the unmanned vehicle under different operating conditions; and controlling the unmanned vehicle to drive based on the determined front wheel angle and wheel torque.

[0215] Storage unit 1120 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 11201 and / or cache memory 11202, and may further include a read-only memory (ROM) 11203.

[0216] Storage unit 1120 may also include a program / utility 11204 having a set (at least one) of program modules 11205, such program modules 11205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0217] Bus 1130 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0218] Electronic device 1100 can also communicate with one or more external devices 1140 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 1100, and / or with any device that enables electronic device 1100 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 1150. Furthermore, electronic device 1100 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 1160. As shown, network adapter 1160 communicates with other modules of electronic device 1100 via bus 1130. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 1100, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0219] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0220] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, which may be a readable signal medium or a readable storage medium. Figure 12 This illustration shows a schematic diagram of a computer-readable storage medium according to an embodiment of the present disclosure, such as... Figure 12As shown, the computer-readable storage medium 1200 stores a program product capable of implementing the methods described above. In some possible embodiments, various aspects of this disclosure may also be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0221] More specific examples of computer-readable storage media in this disclosure may include, but are not limited to: electrical connections having 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 fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0222] In this disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting a program for use by or in connection with an instruction execution system, apparatus, or device.

[0223] Optionally, the program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0224] In practical implementation, program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0225] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0226] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0227] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0228] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A method for controlling an unmanned vehicle, characterized in that, include: The trajectory tracking accuracy, stability index, and wheel slip ratio of the autonomous vehicle are obtained under different operating conditions. The trajectory tracking accuracy includes lateral displacement deviation and heading angle deviation. The stability index includes lateral vehicle speed, yaw rate, and front and rear axle wheel slip angles. Based on the trajectory tracking accuracy, stability index, and wheel slip ratio of the unmanned vehicle under different operating conditions, and based on the pre-constructed state space expression, objective function, and constraints, the front wheel steering angle and wheel torque of the unmanned vehicle are determined to track the target vehicle speed and reference trajectory information in a stable state under different operating conditions. The driverless vehicle is controlled to move based on the determined front wheel steering angle and wheel torque. The state-space expression uses lateral vehicle speed, yaw rate, lateral displacement deviation, heading angle deviation, wheel slip ratio, and front and rear axle wheel sideslip angles as state variables, and front wheel steering angle and longitudinal driving / braking torque of each wheel as control quantities; the objective function aims at trajectory tracking accuracy, stability index, and wheel slip ratio; the constraints include: constraints on front wheel steering angle, constraints on longitudinal driving or braking torque of the four tires, constraints on front wheel steering angle increment, constraints on longitudinal driving or braking torque increment of the four tires, and constraints on the safe phase plane; The method further includes: adaptively adjusting the weighting coefficients of lateral speed, yaw rate and tracking deviation based on the vehicle stability state of the unmanned vehicle under different operating conditions.

2. The unmanned vehicle control method according to claim 1, characterized in that, The method further includes: Acquire the target speed and reference trajectory information of the autonomous vehicle; Based on the target speed and reference trajectory information of the autonomous vehicle, the front wheel steering angle of the autonomous vehicle is determined to track the target speed and reference trajectory information. Based on the target speed and actual speed of the autonomous vehicle, determine the total driving or braking torque required for the autonomous vehicle to track the target speed. Based on the vertical load distribution of the front and rear axles of the unmanned vehicle, the total driving or braking torque required for the unmanned vehicle to track the target speed is distributed to each wheel to obtain the longitudinal driving or braking torque of each wheel. Based on the wheel slip ratio of the unmanned vehicle under different operating conditions, the longitudinal driving or braking torque of each wheel is constrained.

3. The unmanned vehicle control method according to claim 2, characterized in that, After determining the total driving or braking torque required for the autonomous vehicle to track the target speed based on the target speed and the actual speed of the autonomous vehicle, the method further includes: Obtain the maximum driving or braking torque output by the motor on the unmanned vehicle; Based on the maximum driving or braking torque output by the motor on the autonomous vehicle, the total driving or braking torque required for the autonomous vehicle to track the target speed is limited.

4. A control device for an unmanned vehicle, characterized in that, include: The status information acquisition module is used to acquire the trajectory tracking accuracy, stability index, and wheel slip ratio of the unmanned vehicle under different operating conditions. The trajectory tracking accuracy includes lateral displacement deviation and heading angle deviation. The stability index includes lateral vehicle speed, yaw rate, and front and rear axle wheel slip angle. The control quantity determination module is used to determine the front wheel angle and wheel torque of the unmanned vehicle in a stable state when tracking the target vehicle speed and reference trajectory information under different operating conditions, based on the trajectory tracking accuracy, stability index and wheel slip ratio of the unmanned vehicle under different operating conditions, and on the basis of the pre-constructed state space expression, objective function and constraint conditions. The control module is used to control the driving of the unmanned vehicle based on the determined front wheel steering angle and wheel torque; The state-space expression uses lateral vehicle speed, yaw rate, lateral displacement deviation, heading angle deviation, wheel slip ratio, and front and rear axle wheel sideslip angles as state variables, and front wheel steering angle and longitudinal driving / braking torque of each wheel as control quantities; the objective function aims at trajectory tracking accuracy, stability index, and wheel slip ratio; the constraints include: constraints on front wheel steering angle, constraints on longitudinal driving or braking torque of the four tires, constraints on front wheel steering angle increment, constraints on longitudinal driving or braking torque increment of the four tires, and constraints on the safe phase plane; The control quantity determination module is further used to: adaptively adjust the weighting coefficients of lateral speed, yaw rate and tracking deviation based on the vehicle stability state of the unmanned vehicle under different operating conditions.

5. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the autonomous vehicle control method of any one of claims 1 to 3 by executing the executable instructions.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the unmanned vehicle control method according to any one of claims 1 to 3.

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