Vehicle obstacle avoidance control method and device and vehicle

Abstract

The application provides a vehicle obstacle avoidance control method, device and vehicle. The method comprises: when obstacle avoidance is triggered, generating an obstacle avoidance reference trajectory of the vehicle according to vehicle state information and obstacle avoidance target information; determining a longitudinal control instruction and a lateral control instruction according to the obstacle avoidance reference trajectory and the current vehicle state information, wherein the longitudinal control instruction comprises an accelerator pedal opening degree and a longitudinal deceleration requirement, and the lateral control instruction comprises a front wheel steering angle, a rear wheel steering angle and a target additional yaw moment; generating an execution instruction for executing obstacle avoidance according to the longitudinal control instruction and the lateral control instruction, and outputting the execution instruction to a vehicle execution mechanism to control the vehicle to complete obstacle avoidance. In addition, the controller calculation complexity is reduced by decoupling the longitudinal control and the lateral control. The vehicle yaw control capability and stability margin are improved in the extreme working condition by cooperatively controlling the front wheel steering angle, the rear wheel steering angle and the target additional yaw moment in the obstacle avoidance process.

Description

Technical Field

[0001] This application relates to the field of active safety control for vehicles, and more particularly to a vehicle obstacle avoidance control method, device, and vehicle. Background Technology

[0002] With the rapid development of intelligent driving technology, Autonomous Emergency Steering (AES) has become an active safety technology as important as Autonomous Emergency Braking (AEB) in typical scenarios such as highways and urban expressways. When a vehicle encounters an obstacle ahead that suddenly decelerates or comes to a stop, and braking alone cannot avoid a collision within a safe distance, AES needs to perform rapid lateral maneuvers to achieve emergency obstacle avoidance, thereby reducing or preventing the occurrence of a collision.

[0003] However, when performing emergency steering maneuvers at high speeds or on low-traction surfaces (such as wet or icy roads), vehicles are prone to entering a high-dynamic limit state. This manifests as a significant increase in lateral acceleration, rapid changes in yaw rate, and a gradual approach to or even entry into the saturation range of the tire slip angle. Under these conditions, the vehicle's handling and stability significantly decrease, potentially leading to insufficient yaw response, reduced trajectory tracking accuracy, or even fishtailing and instability. This places higher demands on the dynamic performance and stability of the vehicle control system.

[0004] In existing technologies, AES control methods mainly rely on the front wheel steering actuator to complete obstacle avoidance control. When the vehicle is under extreme conditions, the control capability of a single steering actuator is limited, making it difficult to simultaneously ensure trajectory tracking accuracy and vehicle motion stability. Summary of the Invention

[0005] This application provides a vehicle obstacle avoidance control method, device, and vehicle, which improves the vehicle's trajectory tracking ability and motion stability during emergency obstacle avoidance by coordinating the control of the vehicle's steering actuator and braking actuator.

[0006] In a first aspect, this application provides a vehicle obstacle avoidance control method. The method includes: upon obstacle avoidance triggering, generating an obstacle avoidance reference trajectory for the vehicle based on vehicle status information and obstacle avoidance target information; determining longitudinal control commands and lateral control commands based on the obstacle avoidance reference trajectory and the current vehicle status information, wherein the longitudinal control commands include accelerator pedal opening and longitudinal deceleration requirements, and the lateral control commands include front wheel steering angle, rear wheel steering angle, and target additional yaw moment; and generating execution commands for performing obstacle avoidance based on the longitudinal control commands and the lateral control commands.

[0007] Optionally, generating the execution command for obstacle avoidance based on the longitudinal control command and the lateral control command includes: generating the braking torque of each wheel based on the longitudinal deceleration requirement and the target additional yaw moment, wherein the execution command includes the accelerator pedal opening, the braking torque of each wheel, the front wheel steering angle, and the rear wheel steering angle.

[0008] Optionally, generating the obstacle avoidance reference trajectory of the vehicle based on the vehicle status information and obstacle avoidance target information includes: generating a longitudinal reference trajectory of the vehicle based on the vehicle status information, wherein the information of the longitudinal reference trajectory includes longitudinal reference velocity and longitudinal reference acceleration; generating a lateral reference trajectory of the vehicle based on the vehicle status information and obstacle avoidance target information, wherein the information of the lateral reference trajectory includes the vehicle's position deviation and heading angle deviation; wherein, determining the longitudinal control command and the lateral control command based on the obstacle avoidance reference trajectory and the current vehicle status information includes: determining the longitudinal control command based on the longitudinal reference trajectory and the current vehicle status information; determining the lateral control command based on the lateral reference trajectory and the current vehicle status information.

[0009] Optionally, determining the longitudinal control command based on the longitudinal reference trajectory and the current vehicle state information includes: determining the longitudinal control command based on the longitudinal reference speed and the vehicle's current actual speed, including: when the longitudinal reference speed is greater than the current actual speed, determining the accelerator pedal opening based on the difference between the longitudinal reference speed and the current actual speed using a proportional-integral-derivative control method; when the longitudinal reference speed is less than the current actual speed, determining the longitudinal deceleration requirement based on the difference between the longitudinal reference speed and the current actual speed using a proportional-integral-derivative control method.

[0010] Optionally, the vehicle state information includes the initial speed of the vehicle during obstacle avoidance, wherein generating the longitudinal reference trajectory of the vehicle based on the vehicle state information includes: obtaining the maximum longitudinal acceleration of the vehicle and the road surface friction coefficient constraint; generating the longitudinal reference trajectory of the vehicle based on the initial speed and the target speed expected at the end of obstacle avoidance, and in combination with the maximum longitudinal acceleration and the road surface friction coefficient constraint.

[0011] Optionally, the obstacle avoidance target information includes the target obstacle avoidance lateral displacement and the longitudinal relative distance between the vehicle and the obstacle avoidance target. The vehicle status information includes the vehicle's current position and the vehicle's current heading angle. The step of generating the vehicle's lateral reference trajectory based on the vehicle status information and the obstacle avoidance target information includes: determining the vehicle's maximum lateral acceleration during obstacle avoidance based on the longitudinal relative distance, wherein the maximum lateral acceleration is negatively correlated with the longitudinal relative distance; determining multiple reference trajectory points for the vehicle at control sampling intervals based on the target obstacle avoidance lateral displacement, the maximum lateral acceleration, and preset lateral constraint conditions. The information for each reference trajectory point includes the vehicle's reference position and reference heading angle. The lateral constraint conditions include the initial and final moments of obstacle avoidance, and the vehicle's lateral displacement, lateral velocity, and lateral acceleration satisfy preset boundary conditions; within each control sampling period, projecting each reference trajectory point from the global coordinate system to the current vehicle coordinate system based on the vehicle's current position and the vehicle's current heading angle to obtain the lateral reference trajectory in the vehicle coordinate system.

[0012] Optionally, the position deviation includes lateral displacement deviation, and determining the lateral control command based on the lateral reference trajectory and the current vehicle state information includes: defining a state vector based on the vehicle's lateral velocity, yaw rate, lateral displacement deviation, and heading angle deviation; defining a control vector based on the front wheel steering angle, rear wheel steering angle, and target additional yaw moment; constructing a linear time-varying model of vehicle lateral dynamics based on the state vector, the control vector, and vehicle physical parameters; and within each control sampling period, performing online optimization calculations to solve for the vehicle's optimized control sequence using model predictive control methods based on the linear time-varying model, and selecting the first set of control vectors from the optimized control sequence as the lateral control command output for the current control sampling period.

[0013] Optionally, the vehicle physical parameters include: vehicle mass, vehicle moment of inertia about a vertical axis, longitudinal distance from the vehicle's center of gravity to the front axle, longitudinal distance from the vehicle's center of gravity to the rear axle, equivalent lateral stiffness of the front axle, and equivalent lateral stiffness of the rear axle.

[0014] Optionally, the online optimization calculation of the vehicle's optimal control sequence using the model predictive control method includes: constructing a model predictive control problem with trajectory tracking error and control input cost as optimization objectives in the prediction time domain; solving the model predictive control problem to obtain the optimal control sequence.

[0015] Optionally, the model predictive control problem further includes stability constraints and actuator constraints; the stability constraints include rear wheel sideslip angle constraints; the actuator constraints include front wheel steering angle constraints, rear wheel steering angle constraints, target additional yaw moment constraints, and corresponding rate of change constraints.

[0016] Optionally, generating the braking torque of each wheel based on the longitudinal deceleration requirement and the target additional yaw moment includes: determining the total longitudinal braking force required by the vehicle based on the longitudinal deceleration requirement; distributing the total longitudinal braking force between the front and rear axles based on the vehicle's tire friction constraints and the vertical load distribution of the front and rear axles to obtain the basic braking forces of the front and rear axles; determining the braking force difference between the left and right wheels based on the target additional yaw moment; and determining the braking torque of each wheel based on the basic braking forces of the front and rear axles and the braking force difference.

[0017] Optionally, determining the braking torque of each wheel based on the base braking force of the front axle and the rear axle and the braking force difference includes: determining the target braking force of each wheel based on the base braking force of the front axle and the rear axle and the braking force difference; applying tire friction limit constraints to the target braking force of each wheel; and converting the target braking force of each wheel into the braking torque of each wheel based on the wheel radius.

[0018] In a second aspect, this application provides a vehicle obstacle avoidance control device configured to perform the vehicle obstacle avoidance control method as described in the first aspect.

[0019] Thirdly, this application provides a vehicle that includes the vehicle obstacle avoidance control device as described in the second aspect.

[0020] This application can determine longitudinal control commands and lateral control commands based on the obstacle avoidance reference trajectory and the current vehicle status information. By decoupling longitudinal control from lateral control, the longitudinal actuator can be directly incorporated into the model predictive control optimization problem, thereby reducing the computational complexity of the controller and helping to meet automotive-grade real-time requirements.

[0021] This application improves the vehicle's yaw control capability and stability margin under extreme conditions by coordinating the front wheel steering angle, rear wheel steering angle, and target additional yaw moment during obstacle avoidance. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0023] Figure 1The diagram shown is a flowchart of a vehicle obstacle avoidance control method according to an embodiment of this application.

[0024] Figure 2 As shown Figure 1 The diagram shows a specific flowchart of a vehicle obstacle avoidance control method.

[0025] Figure 3 The diagram shown is a detailed flowchart of a method for generating a vehicle's longitudinal reference trajectory based on vehicle status information, according to an embodiment of this application.

[0026] Figure 4 The diagram shown is a detailed flowchart of an embodiment of this application for generating a lateral reference trajectory of a vehicle based on vehicle status information and obstacle avoidance target information.

[0027] Figure 5 The diagram shown is a detailed flowchart of an embodiment of this application for determining longitudinal control commands based on a longitudinal reference trajectory and current vehicle status information.

[0028] Figure 6 The diagram shown is a detailed flowchart of an embodiment of this application for determining lateral control commands based on a lateral reference trajectory and current vehicle status information.

[0029] Figure 7 The diagram shown is a detailed flowchart of an embodiment of this application, which generates the braking torque of each wheel based on the target additional yaw moment and the vehicle's longitudinal deceleration requirements.

[0030] Figure 8 The diagram shown is a schematic block diagram of a vehicle obstacle avoidance control device according to an embodiment of this application. Detailed Implementation

[0031] The technical solutions in the embodiments (or "implementations") of this application will be clearly and completely described herein with reference to the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements.

[0032] If the embodiments of this application contain terms relating to directional indications or positional relationships (such as up, down, left, right, front, back, inside, outside, top, bottom, center, vertical, horizontal, longitudinal, transverse, length, width, counterclockwise, clockwise, axial, radial, circumferential, etc.), such terms are only used to explain the relative positional relationships and movement of the components in a specific posture (as shown in the attached figures); if the specific posture changes, the directional indications or positional relationships will also change accordingly. Furthermore, the terms "first" and "second" used in the embodiments of this application are only for descriptive convenience and should not be construed as indicating or implying relative importance.

[0033] The vehicle obstacle avoidance control method, device, and vehicle of this application will be described in detail below with reference to the accompanying drawings. Unless otherwise specified, the features of the following embodiments and implementations can be combined with each other.

[0034] Figure 1 A flowchart illustrating a vehicle obstacle avoidance control method according to an embodiment of this application is provided. See also... Figure 1 As shown, a vehicle obstacle avoidance control method according to an embodiment of this application includes steps S1 to S3.

[0035] In step S1, when obstacle avoidance is triggered, an obstacle avoidance reference trajectory for the vehicle is generated based on the vehicle status information and the obstacle avoidance target information.

[0036] At the moment of obstacle avoidance triggering, a continuous and smooth obstacle avoidance reference trajectory can be constructed based on vehicle status information, obstacle avoidance target information, and combined with longitudinal acceleration constraints and lateral acceleration constraints.

[0037] In step S2, longitudinal control commands and lateral control commands are determined based on the obstacle avoidance reference trajectory and the current vehicle state information. The longitudinal control commands include accelerator pedal opening. and longitudinal deceleration requirements Lateral control commands include front wheel steering angle. Rear wheel steering angle Additional yaw moment to the target .

[0038] In step S3, execution commands for obstacle avoidance are generated based on longitudinal control commands and lateral control commands, and output to the vehicle actuators to control the vehicle to complete obstacle avoidance.

[0039] The vehicle obstacle avoidance control method of this application decouples longitudinal control from lateral control, which avoids directly incorporating the longitudinal actuator into the model predictive control optimization problem, thereby reducing the computational complexity of the controller and helping to meet automotive-grade real-time requirements.

[0040] In some embodiments, step S3, generating execution instructions for obstacle avoidance based on longitudinal control instructions and lateral control instructions, may include: based on longitudinal deceleration requirements. and target additional yaw moment Generate braking torque for each wheel The execution commands include accelerator pedal opening. Braking torque of each wheel Front wheel steering angle and rear wheel steering angle .

[0041] Through the above method, this application achieves joint control of the front wheel steering angle during emergency obstacle avoidance. Rear wheel steering angle and the target additional yaw moment It achieves target additional yaw moment through braking torque distribution, thereby realizing coordinated control of the vehicle steering system and braking system, improving the vehicle's trajectory tracking ability during emergency obstacle avoidance and motion stability under extreme conditions such as high dynamics or low adhesion, and breaking through the performance bottleneck of traditional single front wheel steering control structure in terms of actuator capability limitations.

[0042] Figure 2 Revealed Figure 1 The diagram shows a specific flowchart of a vehicle obstacle avoidance control method. See also... Figure 2 As shown, in some embodiments, step S1, which generates the vehicle's obstacle avoidance reference trajectory based on the vehicle state information and obstacle avoidance target information, may include steps S11 and S12.

[0043] In step S11, a longitudinal reference trajectory of the vehicle can be generated based on the vehicle status information.

[0044] The information in the longitudinal reference trajectory may include the vehicle's longitudinal reference velocity and the vehicle's longitudinal reference acceleration.

[0045] See Figure 3 As shown, in some embodiments, the vehicle state information may include the initial speed of the vehicle when avoiding obstacles, wherein step S11, which generates the longitudinal reference trajectory of the vehicle based on the vehicle state information, may further include steps S111 and S112.

[0046] In step S111, the maximum longitudinal acceleration of the vehicle and the road friction coefficient constraints are obtained.

[0047] In step S112, the longitudinal reference trajectory of the vehicle is generated based on the initial speed of the vehicle during obstacle avoidance and the expected target speed at the end of obstacle avoidance, combined with the maximum longitudinal acceleration and road friction coefficient constraints.

[0048] Optionally, for the vehicle's longitudinal reference trajectory, based on the vehicle's initial velocity during obstacle avoidance, the expected target velocity at the end of obstacle avoidance, and in conjunction with the maximum longitudinal acceleration and road friction coefficient constraints, a cubic polynomial S-curve is used to generate the vehicle's longitudinal reference velocity. and the vehicle's longitudinal reference acceleration Make the longitudinal reference velocity Compared with longitudinal reference acceleration The vehicle maintains continuous changes throughout the obstacle avoidance process, thereby avoiding sudden commands that could affect the vehicle's longitudinal stability.

[0049] In step S12, a lateral reference trajectory for the vehicle can be generated based on the vehicle status information and obstacle avoidance target information. The information in the lateral reference trajectory may include the vehicle's position deviation and heading angle deviation. Positional deviations can include lateral displacement deviations. .

[0050] See Figure 4 As shown, in some embodiments, the obstacle avoidance target information may include the lateral displacement of the target and the longitudinal relative distance between the vehicle and the obstacle avoidance target, and the vehicle status information includes the vehicle's current position and the vehicle's current heading angle. The step S12 of generating the vehicle's lateral reference trajectory based on the vehicle status information and the obstacle avoidance target information may further include steps S121 to S123.

[0051] In step S121, the maximum lateral acceleration of the vehicle during obstacle avoidance can be determined based on the longitudinal relative distance. A smaller longitudinal relative distance results in a larger maximum lateral acceleration; conversely, a larger longitudinal relative distance results in a smaller maximum lateral acceleration. That is, the maximum lateral acceleration is negatively correlated with the longitudinal relative distance.

[0052] Based on the target obstacle avoidance lateral displacement, maximum lateral acceleration, and preset lateral constraints, multiple reference trajectory points for the vehicle are determined. Information for each reference trajectory point includes the vehicle's reference position. and reference heading angle Among them, the lateral constraint conditions include the boundary conditions that the vehicle's lateral displacement, lateral velocity and lateral acceleration are all zero at the initial and final moments of obstacle avoidance. In step S122, multiple reference trajectory points of the vehicle are determined at intervals based on the target obstacle avoidance lateral displacement, maximum lateral acceleration, and preset lateral constraints, with the control sampling period as the interval. Information for each reference trajectory point includes the vehicle's reference position. and reference heading angle .

[0053] In the lateral direction, a fifth-order polynomial can be used to construct the lateral displacement trajectory within the time window for obstacle avoidance. Considering the special characteristics of the obstacle avoidance and lane change trajectory, it satisfies preset lateral constraints. The lateral constraints may include: at the start and end of obstacle avoidance, the vehicle's lateral displacement, lateral velocity, and lateral acceleration satisfy preset boundary conditions. In one embodiment, the preset boundary conditions can be set as: at the start and end of obstacle avoidance, the vehicle's lateral displacement, lateral velocity, and lateral acceleration are all zero. Specifically, it is expressed in the following formula (1): (1) in, , , These are the lateral displacement, lateral velocity, and lateral acceleration at the start of obstacle avoidance (i.e., time 0).

[0054] Furthermore, at the end of the obstacle avoidance process, the lateral displacement reaches the target obstacle avoidance lateral displacement, and both the lateral velocity and lateral acceleration are zero. This is specifically expressed in formula (2) below: (2) in, , , These represent the vehicle's lateral displacement, lateral velocity, and lateral acceleration at the end of the obstacle avoidance maneuver (time T), where T is the duration of the obstacle avoidance maneuver. Lateral displacement to avoid obstacles.

[0055] Based on the above boundary conditions, a simplified fifth-degree polynomial can be obtained, as shown in formula (3) below:

[0056]

[0057] (3) in, , , Let t represent the vehicle's lateral displacement, lateral velocity, and lateral acceleration at time t.

[0058] Using the simplified fifth-order polynomial described above, we can obtain the information of the reference trajectory point at each time corresponding to the control sampling period. The information of the reference trajectory point includes the reference position. and reference heading angle .

[0059] Thus, by ensuring that the vehicle's lateral displacement is continuous at the start and end times of obstacle avoidance and that the lateral velocity and lateral acceleration are zero at the start and end times of obstacle avoidance, a smooth and trackable lateral reference trajectory for obstacle avoidance in the global coordinate system can be generated. Furthermore, by selecting the obstacle avoidance duration T to satisfy the maximum lateral acceleration constraint, the lateral reference trajectory is guaranteed to be executable under given adhesion conditions. The calculation formula for the obstacle avoidance duration T is shown in formula (4) below: (4) in, Lateral displacement to avoid obstacles. This represents the maximum lateral acceleration of the vehicle during obstacle avoidance.

[0060] Based on this, the reference heading angle for each reference trajectory point is calculated by combining the longitudinal and lateral reference velocities. The reference heading angle is determined by the tangential direction of the obstacle avoidance reference trajectory, as shown in formula (5) below: (5) in, The reference heading angle for the reference trajectory point. For lateral reference speed, The longitudinal reference speed is, i.e. .

[0061] In step S123, within each control sampling period, each reference trajectory point is projected in real time based on the vehicle's current position and current heading angle, projecting each reference trajectory point from the global coordinate system to the current vehicle coordinate system, thereby obtaining the reference trajectory information in the current vehicle coordinate system. The reference trajectory information in the current vehicle coordinate system may include the vehicle's lateral displacement deviation. and heading angle deviation .

[0062] Specifically, the generation time of the obstacle avoidance reference trajectory The vehicle's coordinate system is considered the global coordinate system. Based on the vehicle's current position and heading angle, a coordinate transformation relationship from the global coordinate system to the vehicle's coordinate system is constructed. The reference trajectory points are mapped to the current vehicle coordinate system in real time, and the reference heading angle is updated synchronously. Specifically, the above transformation is written as matrix multiplication in homogeneous matrix form, as shown in formula (6): (6) in,( , ( ) represents the coordinates of the reference trajectory point in the current vehicle body coordinate system; , ( ) represents the vehicle's current position coordinates in the global coordinate system; , () represents the coordinates of the reference trajectory point in the global coordinate system (geocentric coordinate system); This is the vehicle's current heading angle.

[0063] Meanwhile, the reference heading angle is updated in the vehicle coordinate system according to the relative heading relationship, as shown in formula (7): (7) in, This represents the heading angle deviation of the reference trajectory point in the current vehicle body coordinate system. The reference heading angle for the reference trajectory point in the global coordinate system; The current heading angle of the vehicle in the global coordinate system; wrapToPi(·) is a standard angle normalization function used to map any angle value to the interval (-π, π).

[0064] Since the obstacle avoidance reference trajectory is generated at the time of its creation... Certainly, during vehicle operation, the vehicle's position and heading attitude will change with the current time. Because the trajectory is constantly changing, the above projection process is used to control the sampling period and project the vehicle's trajectory in real time. This allows for the projection of the vehicle's trajectory at the current moment. Accurately obtain key tracking error information such as the lateral position deviation and heading angle deviation of the obstacle avoidance reference trajectory in the vehicle coordinate system, thereby providing a unified, time-domain aligned reference input for the control module.

[0065] See back Figure 2 As shown, in an optional embodiment, step S2, which determines the longitudinal control command and the lateral control command based on the obstacle avoidance reference trajectory and the current vehicle status information, may further include steps S21 and S22.

[0066] In step S21, the longitudinal control command can be determined based on the longitudinal reference trajectory and the current vehicle status information.

[0067] See Figure 5 As shown, step S21 may optionally include steps S211 to S213.

[0068] In step S211, the longitudinal reference velocity is compared. Determine the longitudinal reference speed based on the magnitude of the vehicle's current actual speed. Is it greater than the vehicle's current actual speed? If the result of the judgment is "yes", proceed to step S212. Otherwise, proceed to step S213.

[0069] In step S212, when the longitudinal reference velocity When the speed is greater than the vehicle's current actual speed, the longitudinal reference speed is used. The difference between the actual speed and the current speed is used to determine the accelerator pedal opening based on the proportional-integral-derivative (PID) control method. To drive the vehicle to accelerate to the desired speed.

[0070] In step S213, when the longitudinal reference velocity When the speed is less than the current actual speed, the speed is determined based on the longitudinal reference speed. The difference between the actual speed and the current speed is used to output the longitudinal deceleration demand based on the proportional-integral-derivative (PID) control method. .

[0071] In step S22, the lateral control command can be determined based on the lateral reference trajectory and the current vehicle status information.

[0072] See Figure 6 As shown, in an optional embodiment, step S22, which determines the lateral control command based on the lateral reference trajectory and the current vehicle status information, may further include steps S221 to S224.

[0073] In step S221, the vehicle's lateral dynamics state vector is defined based on the vehicle's lateral velocity, yaw rate, lateral displacement deviation, and heading angle deviation. In this embodiment, the vehicle's state vector is defined as shown in formula (8): (8) in, The lateral velocity at the vehicle's center of gravity is used to characterize the vehicle's lateral motion state. The yaw rate is the angular velocity of the vehicle, used to characterize the vehicle's rotational motion about the vertical axis. This refers to the lateral displacement deviation of the vehicle's center of gravity relative to the obstacle avoidance reference trajectory or the road centerline. This represents the deviation of the vehicle's heading angle from the reference heading angle.

[0074] The vehicle's state vector simultaneously describes the vehicle's dynamic state. With kinematic deviation state It is applicable to the problem of coordinated control of obstacle avoidance reference trajectory tracking and vehicle stability.

[0075] In step S222, based on the front wheel steering angle of the vehicle Rear wheel steering angle Additional yaw moment to the target Define the control vector.

[0076] In this embodiment, the control vector is defined as shown in formula (9): (9) in, This refers to the steering angle of the front wheels; This is the rear wheel steering angle. When the vehicle does not have rear wheel steering capability, the rear wheel steering angle can be set to zero. The target additional yaw moment applied to the vehicle can be generated by differential braking of the left and right wheels or other yaw moment actuators. Through the above-mentioned multi-actuator input, the coordinated adjustment of the vehicle's lateral dynamics and yaw stability can be achieved.

[0077] In step S223, a linear time-varying model of the vehicle's lateral dynamics is constructed based on the state vector, control vector, and vehicle physical parameters.

[0078] Specifically, the vehicle's lateral linear time-varying model is shown in formula (10):

[0079] (10) The system state matrix A, control matrix B, and output matrix C are shown in equation (11):

[0080]

[0081] (11) in, For the overall vehicle weight; Let be the moment of inertia of the vehicle about its vertical axis; This is the longitudinal distance from the vehicle's center of gravity to the front axle; This is the longitudinal distance from the vehicle's center of gravity to the rear axle; This represents the vehicle's current longitudinal speed. It is the equivalent lateral stiffness of the front axle of the vehicle, used to describe the linear response characteristics of the front tire lateral force to the lateral slip angle; This is the equivalent lateral stiffness of the rear axle of the vehicle, used to describe the linear response characteristics of the rear wheel tire lateral force to the lateral angle.

[0082] Among them, the vehicle's current longitudinal speed This refers to quantities that are measured or estimated online, used to implement time-varying updates of linear time-varying models. The equivalent lateral stiffness of the vehicle's front axle. Equivalent lateral stiffness of the vehicle's rear axle It is not a fixed constant, but an equivalent parameter that varies with road surface adhesion conditions, tire conditions, and vehicle operating states, used to characterize the vehicle's ability to generate lateral forces under current operating conditions. In this application, the equivalent lateral stiffness of the vehicle's front axle... Equivalent lateral stiffness of the vehicle's rear axle It can be obtained through a joint estimation method of vehicle lateral velocity and steering wheel lateral stiffness.

[0083] In step S224, within each control sampling period, the optimal control sequence of the vehicle is calculated online using the model predictive control method based on the linear time-varying model. The first set of control vectors in the optimal control sequence is selected as the lateral control command output for the current control sampling period, thereby enabling the vehicle to execute according to the lateral control command.

[0084] Using the above method, a three-actuator linear time-varying model predictive control (LTV-MPC) method based on vehicle dynamics model was realized. Under a unified optimization framework, the front wheel steering angle of the lateral control command was jointly solved. Rear wheel steering angle and target additional yaw moment This enables precise obstacle avoidance reference trajectory tracking and vehicle stability control in highly dynamic obstacle avoidance scenarios.

[0085] In an optional embodiment, the vehicle physical parameters may include: vehicle mass. Moment of inertia of the vehicle about its vertical axis Longitudinal distance from the vehicle's center of gravity to the front axle Longitudinal distance from the vehicle's center of gravity to the rear axle The vehicle's current longitudinal speed The equivalent lateral stiffness of the vehicle's front axle The equivalent lateral stiffness of the rear axle of the vehicle .

[0086] Then, based on the above linear time-varying model, the calculation result for the next time step can be obtained through the forward Euler method.

[0087] In the prediction time domain, a model predictive control problem is constructed with trajectory tracking error and control input cost as optimization objectives; the model predictive control problem is solved in the prediction time domain to obtain the optimal control sequence.

[0088] The established objective function comprehensively considers lateral displacement deviation and lateral control quantity in the prediction time domain, and its form is shown in formula (12): (12) in, To predict the time domain, To control the time domain, k is the discrete time index within the prediction time domain. This represents the actual lateral displacement deviation of the vehicle. This represents the lateral displacement deviation of the lateral reference trajectory.

[0089] In an optional embodiment, the model predictive control problem may further include stability constraints, which may include rear wheel sideslip angle constraints.

[0090] Among them, the rear wheel sideslip angle is used as the stability constraint variable, and the stability constraint is defined as shown in formula (13):

[0091] (13) in, This refers to the rear wheel slip angle; This is the maximum rear wheel slip angle.

[0092] In other embodiments, the model predictive control problem may also include actuator constraints. Actuator constraints may include front wheel steering angle constraints, rear wheel steering angle constraints, target additional yaw moment constraints, and corresponding rate of change constraints. These reflect the physical limitations and response capabilities of the actual actuators, thereby ensuring the executability and smoothness of the control commands. Within each control sampling period, the optimal control sequence is obtained by solving the aforementioned quadratic programming problem, and the first set of control vectors is selected as the lateral control command output for the current control sampling period, achieving coordinated control of the front wheel steering angle, rear wheel steering angle, and target additional yaw moment.

[0093] In an optional embodiment, see Figure 7 As shown, the target is subject to an additional yaw moment. and vehicle longitudinal deceleration requirements Generate braking torque for each wheel This may include steps S31 to S34.

[0094] In step S31, based on the longitudinal deceleration requirement Determine the total longitudinal braking force required for the vehicle. .

[0095] In step S32, based on the vehicle's tire friction constraints and the vertical load distribution of the front and rear axles, the total longitudinal braking force is... The braking force is distributed between the front and rear axles to obtain the basic braking force for both axles.

[0096] In step S33, an additional yaw moment is applied according to the target. Determine the difference in braking force between the left and right wheels.

[0097] In step S34, the braking torque of each wheel is determined based on the basic braking force and braking force difference between the front and rear axles. .

[0098] In an optional embodiment, see Figure 8 As shown, step S34 determines the braking torque of each wheel based on the basic braking force and braking force difference between the front and rear axles. It may further include steps S341 to S343.

[0099] In step S341, the braking force of each wheel is determined based on the basic braking force and braking force difference between the front axle and the rear axle.

[0100] In step S342, tire friction limit constraints are applied to the braking force of each wheel.

[0101] In step S343, the braking force of each wheel is converted into braking torque of each wheel according to the wheel radius. .

[0102] Total longitudinal braking force Calculate using the formula (14) below: (14) Where m is the mass of the vehicle. This is for the longitudinal deceleration requirement.

[0103] The basic braking force of the front axle is calculated using the following formula (15): (15) in, This serves as the basic braking force for the front axle. This represents the total vertical load on the front axle. This represents the total vertical load on the rear axle.

[0104] The basic braking force of the rear axle is calculated using the following formula (16): (16) in, It provides the basic braking force for the rear axle.

[0105] Based on this, the target additional yaw moment is output according to the formula (17) below. Establish the relationship between the braking force difference between the left and right wheels and the yaw moment: (17) in, The braking force for the left wheel; The braking force for the right wheel; This refers to the wheel track.

[0106] Overall longitudinal deceleration requirements Adding yaw moment to the target Then, the braking force of each wheel can be obtained, and the braking torque of each wheel can be further calculated based on the braking force of each wheel. .

[0107] In addition, to ensure the physical executability of the command, tire friction limit constraints are applied to the braking force of each wheel using the following formula (18): (18) in, This represents the braking force of the i-th wheel; Let be the vertical load (wheel load) of the i-th wheel. This is the road surface adhesion coefficient.

[0108] Subsequently, the braking force of each wheel is converted into the braking torque of each wheel according to the wheel radius using the formula (19) below: (19) in, Let be the braking torque of the i-th wheel; Let i be the braking force of the i-th wheel; The radius is the wheel radius.

[0109] By using the above-mentioned braking torque distribution method, this application can accurately achieve the target additional yaw moment while meeting the longitudinal braking force requirements, and fully consider the load transfer and tire friction limit constraints to ensure the stability and reliability of differential braking control in high-speed emergency obstacle avoidance scenarios.

[0110] This application also provides a vehicle obstacle avoidance control device 500. The vehicle obstacle avoidance control device 500 is used to execute the vehicle obstacle avoidance control method described above. Figure 8 A schematic block diagram illustrating one embodiment of a vehicle obstacle avoidance control device 500 of this application is shown. Figure 8 As shown, a vehicle obstacle avoidance control device 500 according to one embodiment of this application may include a trajectory generation module 510, a control module 520, and an execution command generation module.

[0111] The trajectory generation module 510 is used to generate an obstacle avoidance reference trajectory that meets the emergency obstacle avoidance requirements based on vehicle status information and obstacle avoidance target information when obstacle avoidance is triggered.

[0112] The control module 520 determines the vehicle's longitudinal and lateral control commands based on the obstacle avoidance reference trajectory and the current vehicle status information. The longitudinal control commands include accelerator pedal opening. and longitudinal deceleration requirements Lateral control commands include front wheel steering angle. Rear wheel steering angle and target additional yaw moment .

[0113] The execution command generation module generates execution commands for obstacle avoidance based on longitudinal and lateral control commands, and outputs them to the vehicle actuator 540 to control the vehicle to complete obstacle avoidance. Execution commands include accelerator pedal opening. Braking torque of each wheel Front wheel steering angle and rear wheel steering angle .

[0114] In some embodiments, the execution instruction generation module may include a braking torque allocation module 530. The braking torque allocation module 530 is used to apply an additional yaw torque according to the target output by the control module 520. and vehicle longitudinal deceleration requirements Generate braking torque for each wheel This allows for coordinated control of the vehicle's longitudinal deceleration and yaw motion through differential braking.

[0115] In some embodiments, the trajectory generation module 510 may include a global obstacle avoidance trajectory generation submodule 511 and a real-time trajectory projection submodule 512.

[0116] The global obstacle avoidance trajectory generation submodule 511 is used to generate a longitudinal reference trajectory and a lateral reference trajectory in global coordinates based on vehicle status information and obstacle avoidance target information, respectively. The information of the longitudinal reference trajectory, i.e., the longitudinal expected value, includes the longitudinal reference velocity. and longitudinal reference acceleration Information about the lateral reference trajectory in the global coordinate system, i.e., the lateral expected value, includes the reference position. and reference heading angle.

[0117] The real-time trajectory projection submodule 512 is used to extract, for example, a vector consisting of 10 discrete trajectory points from the lateral reference trajectory in the global coordinate system based on the current moment, and update it based on the vehicle body coordinate system. Thus, the lateral reference trajectory in the vehicle body coordinate system can be obtained. The information of the lateral reference trajectory in the vehicle body coordinate system includes lateral displacement deviation. and heading angle deviation .

[0118] In some embodiments, the control module 520 includes a longitudinal control submodule and a lateral control submodule 523.

[0119] The longitudinal control submodule includes a drive control submodule 521 and a braking control submodule 522. The drive control submodule 521 is used when the longitudinal reference speed is output by the trajectory generation module 510. When the speed is greater than the current actual speed, the speed is determined based on the longitudinal reference speed. The difference between the actual speed and the current speed is used to determine the accelerator pedal opening based on the proportional-integral-derivative control method. The braking control submodule 522 is used when the longitudinal reference speed output by the trajectory generation module 510 is... When the speed is less than the current actual speed, the speed is determined based on the longitudinal reference speed. The difference between the actual speed and the current speed is used to determine the longitudinal deceleration requirement based on the proportional-integral-derivative control method. .

[0120] Lateral control submodule 523 is used to calculate the lateral displacement deviation output by trajectory generation module 510. and heading angle deviation A three-actuator linear time-varying model predictive control method based on vehicle dynamics model is proposed, which jointly solves the front wheel steering angle of the lateral control command under a unified optimization framework. Rear wheel steering angle and target additional yaw moment This enables precise obstacle avoidance reference trajectory tracking and vehicle stability control in highly dynamic obstacle avoidance scenarios.

[0121] The braking torque distribution module 530 includes a longitudinal braking force demand generation submodule 531 and a braking torque distribution submodule 532.

[0122] The longitudinal braking force demand generation submodule 531 is used to generate longitudinal deceleration demand. Determine the total longitudinal braking force required for the vehicle. .

[0123] The braking torque distribution submodule 532 is used to distribute the total longitudinal braking force according to the vehicle's tire friction constraints and the vertical load distribution of the front and rear axles. The braking force is distributed between the front and rear axles to obtain the basic braking force for both axles; an additional yaw moment is then applied based on the target. The braking force difference between the left and right wheels is determined; based on the basic braking force and braking force difference between the front and rear axles, the braking force of each wheel is determined; and further, the braking torque of each wheel is obtained based on the braking force of each wheel. .

[0124] This application also provides a vehicle. The vehicle includes the vehicle obstacle avoidance control device 500 as described above.

[0125] The vehicle obstacle avoidance control device 500 and the vehicle of this application have beneficial technical effects that are substantially similar to the vehicle obstacle avoidance control method described above, so they will not be repeated here.

[0126] It should be noted that the technical solutions or features described in the above embodiments can be combined or supplemented with each other without conflict. The scope of protection of this application is not limited to the precise structures described in the above embodiments and shown in the accompanying drawings; all modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A vehicle obstacle avoidance control method, characterized in that, include: When obstacle avoidance is triggered, the vehicle's obstacle avoidance reference trajectory is generated based on the vehicle status information and obstacle avoidance target information; Based on the obstacle avoidance reference trajectory and the current vehicle status information, longitudinal control commands and lateral control commands are determined. The longitudinal control commands include accelerator pedal opening and longitudinal deceleration requirements. The lateral control commands include front wheel steering angle, rear wheel steering angle, and target additional yaw moment. Based on the longitudinal control command and the lateral control command, an execution command for obstacle avoidance is generated. The step of generating the vehicle's obstacle avoidance reference trajectory based on vehicle status information and obstacle avoidance target information includes: A lateral reference trajectory for the vehicle is generated based on vehicle status information and obstacle avoidance target information. The information in the lateral reference trajectory includes the vehicle's position deviation and heading angle deviation. The obstacle avoidance target information includes the target's lateral displacement and the longitudinal relative distance between the vehicle and the obstacle avoidance target. The vehicle status information includes the vehicle's current position and current heading angle. The generation of the vehicle's lateral reference trajectory based on the vehicle status information and the obstacle avoidance target information includes: The maximum lateral acceleration of the vehicle during obstacle avoidance is determined based on the longitudinal relative distance. Using the control sampling period as an interval, multiple reference trajectory points of the vehicle are determined based on the target obstacle avoidance lateral displacement, the maximum lateral acceleration, and preset lateral constraint conditions. The information of each reference trajectory point includes the vehicle's reference position and reference heading angle. Within each control sampling period, based on the vehicle's current position and current heading angle, each reference trajectory point is projected from the global coordinate system to the current vehicle coordinate system to obtain the lateral reference trajectory in the vehicle coordinate system. The step of determining the longitudinal control command and the lateral control command based on the obstacle avoidance reference trajectory and the current vehicle status information includes: The lateral control command is determined based on the lateral reference trajectory and the current vehicle status information.

2. The vehicle obstacle avoidance control method according to claim 1, characterized in that, The step of generating execution instructions for obstacle avoidance based on the longitudinal control instructions and the lateral control instructions includes: The braking torque of each wheel is generated according to the longitudinal deceleration requirement and the target additional yaw moment, wherein the execution command includes the accelerator pedal opening, the braking torque of each wheel, the front wheel steering angle and the rear wheel steering angle.

3. The vehicle obstacle avoidance control method according to claim 1, characterized in that, The process of generating the vehicle's obstacle avoidance reference trajectory based on vehicle status information and obstacle avoidance target information includes: A longitudinal reference trajectory for the vehicle is generated based on the vehicle status information. The information of the longitudinal reference trajectory includes longitudinal reference velocity and longitudinal reference acceleration. The step of determining the longitudinal control command and the lateral control command based on the obstacle avoidance reference trajectory and the current vehicle status information includes: The longitudinal control command is determined based on the longitudinal reference trajectory and the current vehicle status information.

4. The vehicle obstacle avoidance control method according to claim 3, characterized in that, The step of determining the longitudinal control command based on the longitudinal reference trajectory and the current vehicle status information includes: Based on the longitudinal reference speed and the vehicle's current actual speed, longitudinal control commands are determined, including: When the longitudinal reference speed is greater than the current actual speed, the accelerator pedal opening is determined based on the difference between the longitudinal reference speed and the current actual speed, using the proportional-integral-derivative control method. When the longitudinal reference speed is less than the current actual speed, the longitudinal deceleration requirement is determined based on the difference between the longitudinal reference speed and the current actual speed, using the proportional-integral-derivative control method.

5. The vehicle obstacle avoidance control method according to claim 3, characterized in that, The vehicle state information includes the vehicle's initial speed during obstacle avoidance, wherein generating the vehicle's longitudinal reference trajectory based on the vehicle state information includes: Obtain the vehicle's maximum longitudinal acceleration and road friction coefficient constraints; Based on the initial velocity and the expected target velocity at the end of obstacle avoidance, and combined with the maximum longitudinal acceleration and the road surface friction coefficient constraint, the longitudinal reference trajectory of the vehicle is generated.

6. The vehicle obstacle avoidance control method according to claim 1, characterized in that, The maximum lateral acceleration is negatively correlated with the longitudinal relative distance; the lateral constraint conditions include that the lateral displacement, lateral velocity and lateral acceleration of the vehicle satisfy preset boundary conditions at the initial and final moments of obstacle avoidance.

7. The vehicle obstacle avoidance control method according to claim 1, characterized in that, The positional deviation includes lateral displacement deviation, and determining the lateral control command based on the lateral reference trajectory and the current vehicle status information includes: A state vector is defined based on the vehicle's lateral velocity, yaw rate, lateral displacement deviation, and heading angle deviation. The control vector is defined based on the front wheel steering angle, the rear wheel steering angle, and the target additional yaw moment; A linear time-varying model of vehicle lateral dynamics is constructed based on the state vector, the control vector, and the vehicle physical parameters. Within each control sampling period, based on the linear time-varying model, the optimal control sequence of the vehicle is calculated online using the model predictive control method, and the first set of control vectors of the optimal control sequence is selected as the lateral control command output for the current control sampling period.

8. The vehicle obstacle avoidance control method according to claim 7, characterized in that, The vehicle physical parameters include: vehicle mass, vehicle moment of inertia about the vertical axis, longitudinal distance from the vehicle's center of gravity to the front axle, longitudinal distance from the vehicle's center of gravity to the rear axle, front axle equivalent lateral stiffness, and rear axle equivalent lateral stiffness.

9. The vehicle obstacle avoidance control method according to claim 7, characterized in that, The online optimization calculation and solution of the vehicle's optimal control sequence using the model predictive control method includes: Within the prediction time domain, a model predictive control problem is constructed with trajectory tracking error and control input cost as optimization objectives. Solve the model predictive control problem to obtain the optimal control sequence.

10. The vehicle obstacle avoidance control method according to claim 9, characterized in that, The model predictive control problem also includes stability constraints and actuator constraints; The stability constraints include the rear wheel sideslip angle constraint; The actuator constraints include front wheel steering angle constraints, rear wheel steering angle constraints, target additional yaw moment constraints, and corresponding rate of change constraints.

11. The vehicle obstacle avoidance control method according to claim 2, characterized in that, The process of generating the braking torque for each wheel based on the longitudinal deceleration requirement and the target additional yaw moment includes: The total longitudinal braking force required by the vehicle is determined based on the longitudinal deceleration requirement. Based on the tire friction constraints of the vehicle and the vertical load distribution of the front and rear axles, the total longitudinal braking force is distributed between the front and rear axles to obtain the basic braking forces of the front and rear axles. The braking force difference between the left and right wheels is determined based on the target additional yaw moment. The braking torque of each wheel is determined based on the basic braking forces of the front and rear axles and the difference in braking forces.

12. The vehicle obstacle avoidance control method according to claim 11, characterized in that, The step of determining the braking torque of each wheel based on the base braking forces of the front and rear axles and the braking force difference includes: The target braking force for each wheel is determined based on the base braking force of the front and rear axles and the difference in braking force. Apply tire friction limit constraints to the target braking force of each wheel; Based on the wheel radius, the target braking force of each wheel is converted into the braking torque of each wheel.

13. A vehicle obstacle avoidance control device, characterized in that, The vehicle obstacle avoidance control device is configured to perform the vehicle obstacle avoidance control method as described in any one of claims 1-12.

14. A vehicle, characterized in that, The vehicle includes the vehicle obstacle avoidance control device as described in claim 13.

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