Vehicle trajectory tracking and control methods, equipment, media and products

Abstract

This application discloses a vehicle trajectory tracking control method, device, medium, and product, relating to the field of vehicle technology. The method, by integrating the vehicle's lateral and longitudinal motion coupling characteristics with steering control delay characteristics into a target prediction model, can accurately predict the vehicle's future trajectory in the future time domain under conditions such as time-varying vehicle speed and cornering. This overcomes the shortcomings of existing lateral and longitudinal decoupled control architectures in terms of insufficient tracking accuracy in high-dynamic scenarios. By incorporating steering control delay characteristics into the prediction model, it avoids problems such as control lag, increased trajectory deviation, and control oscillation caused by actuator response lag and data processing delay. Based on the deviation between the reference trajectory and the predicted trajectory, the front wheel steering angle control quantity and speed control quantity are jointly solved through a target optimization function, and the wheel torque control quantity is determined based on the speed deviation. This achieves coordinated optimization of lateral and longitudinal control, improving the trajectory tracking accuracy and safety of autonomous vehicles under conditions such as high speed and high curvature paths.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a vehicle trajectory tracking and control method, device, medium and product. Background Technology

[0002] Intelligent vehicle trajectory tracking control is a core technology in the field of autonomous driving motion control, which directly determines the safety of vehicle driving and the accuracy of path following.

[0003] Existing trajectory tracking solutions mostly employ a decoupled control architecture, which limits tracking accuracy in scenarios such as time-varying vehicle speeds and cornering. Furthermore, the steering control system suffers from actuator response lag and data processing delays, leading to a time delay between the actual front wheel steering angle and the expected value. This can easily cause control lag, increased trajectory deviation, and even control oscillations. Consequently, these limitations restrict the tracking performance and safety of autonomous vehicles on high-speed, high-curvature paths. Summary of the Invention

[0004] This application provides a vehicle trajectory tracking control method, device, medium, and product to solve the aforementioned existing problems.

[0005] In a first aspect, this application provides a vehicle trajectory tracking control method, comprising:

[0006] Acquire the vehicle's motion state parameters and reference trajectory;

[0007] Based on the motion state parameters, the target prediction model is invoked to predict the vehicle's trajectory in the future time domain. The target prediction model integrates the vehicle's lateral and longitudinal motion coupling characteristics with steering control delay characteristics.

[0008] Based on the deviation between the reference trajectory and the predicted vehicle trajectory, an objective optimization function is used to optimize and solve the problem, resulting in the front wheel steering angle control quantity and speed control quantity.

[0009] The wheel torque control amount is determined based on the deviation between the speed control amount and the actual vehicle speed.

[0010] The front wheel steering angle control value and the wheel torque control value are output to the vehicle actuator to control the vehicle to follow the reference trajectory.

[0011] In one possible design, the target prediction model construction steps include:

[0012] A three-degree-of-freedom vehicle kinematic model and a steering control delay model consisting of a pure time-delay element and a first-order inertial element are constructed.

[0013] The steering control delay model is integrated into the vehicle kinematics model.

[0014] In one possible design, the vehicle kinematic model uses longitudinal position, lateral position, and yaw angle as state variables, and longitudinal vehicle speed and front wheel steering angle as control inputs.

[0015] One possible design also includes:

[0016] Using the front wheel steering angle control value as input and the actual front wheel steering angle as a comparison benchmark, the time constant in the steering control delay model is calibrated to obtain model parameters that match the actual steering characteristics of the vehicle.

[0017] In one possible design, before calling the target prediction model to predict the vehicle's trajectory, the following steps are also included:

[0018] Based on historical front wheel steering angle control values, the current vehicle motion parameters are predicted in advance.

[0019] The predicted vehicle motion parameters are used as the initial input state for the target prediction model.

[0020] In one possible design, before determining the wheel torque control amount, the following steps are also included:

[0021] The deviation between the speed control quantity and the reference vehicle speed corresponding to the reference trajectory is compared with a preset threshold.

[0022] Based on the comparison results, the target speed for calculating the wheel torque control quantity is determined.

[0023] In one possible design, determining the target speed for calculating the wheel torque control quantity includes:

[0024] If the deviation between the speed control value and the reference vehicle speed is greater than a preset threshold, then the smaller value between the speed control value and the reference vehicle speed is taken as the target speed.

[0025] One possible design also includes:

[0026] If the deviation between the speed control quantity and the reference vehicle speed is not greater than the preset threshold, then the speed control quantity is taken as the target speed.

[0027] In one possible design, an objective function is used for optimization, including:

[0028] Construct an objective optimization function that includes constraints on position tracking error, heading angle tracking error, and control variable changes;

[0029] Under preset constraints, the optimal control input sequence in the prediction time domain is obtained by solving the problem.

[0030] One possible design also includes:

[0031] Obtain the first control increment in the optimal control input sequence;

[0032] Based on the first control increment, the front wheel steering angle control amount and speed control amount at the current moment are determined.

[0033] In one possible design, after obtaining the reference trajectory, the following is also included:

[0034] The reference trajectory is discretized to obtain a sequence of reference trajectory points;

[0035] Track each reference trajectory point sequentially according to the reference trajectory point order until all reference trajectory points have been tracked.

[0036] Secondly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0037] The memory stores computer-executed instructions;

[0038] The processor executes computer execution instructions stored in the memory to implement the method as described in any of the first aspects.

[0039] Thirdly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any of the first aspects.

[0040] Fourthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the method described in any of the first aspects.

[0041] The vehicle trajectory tracking control method, device, medium, and product provided in this application, through a target prediction model that integrates the vehicle's lateral and longitudinal motion coupling characteristics with steering control delay characteristics, can accurately predict the vehicle's future trajectory in the future time domain under complex conditions such as time-varying vehicle speed and cornering, overcoming the shortcomings of existing lateral and longitudinal decoupled control architectures in high-dynamic scenarios with insufficient tracking accuracy. Furthermore, by incorporating steering control delay characteristics into the prediction model, problems such as control lag, increased trajectory deviation, and control oscillation caused by actuator response lag and data processing delay are avoided. Then, based on the deviation between the reference trajectory and the predicted trajectory, the front wheel steering angle control quantity and speed control quantity are jointly solved through a target optimization function, and the wheel torque control quantity is determined based on the speed deviation, achieving coordinated optimization of lateral and longitudinal control, and improving the trajectory tracking accuracy and driving safety of autonomous vehicles under harsh conditions such as high speed and high curvature paths. Attached Figure Description

[0042] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0043] Figure 1 An application scenario diagram corresponding to the vehicle trajectory tracking and control method provided in an embodiment of this application;

[0044] Figure 2 A schematic flowchart of a vehicle trajectory tracking control method provided in an embodiment of this application;

[0045] Figure 3 A vehicle kinematic model provided in one embodiment of this application;

[0046] Figure 4 A flowchart illustrating a vehicle trajectory tracking control method provided in another embodiment of this application;

[0047] Figure 5 A schematic diagram of a path tracking and PID speed control architecture based on a model prediction framework provided in an embodiment of this application;

[0048] Figure 6 This is a schematic diagram of the structure of a vehicle trajectory tracking control device provided in an embodiment of this application;

[0049] Figure 7 This is a structural example diagram of an electronic device provided in an embodiment of this application.

[0050] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0051] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application.

[0052] To clearly understand the technical solution of this application, the solutions of the prior art will be described in detail first.

[0053] Intelligent vehicle trajectory tracking control is a core technology in the field of autonomous driving motion control, directly determining the safety and path-following accuracy of vehicle driving. Existing trajectory tracking solutions mostly employ a decoupled control architecture, which limits tracking accuracy in scenarios such as time-varying vehicle speeds and cornering. Furthermore, due to actuator response lag and data processing delays, the steering control system experiences a time delay between the actual front wheel steering angle and the expected value, easily leading to control lag, increased trajectory deviation, and even control oscillations. Thus, these limitations restrict the tracking performance and safety of autonomous vehicles on high-speed, high-curvature paths.

[0054] Figure 1 This is an application scenario diagram corresponding to the vehicle trajectory tracking and control method provided in one embodiment of this application, such as... Figure 1 As shown, the application scenario provided in this embodiment includes: a vehicle perception and positioning module 10, a trajectory tracking and control device 11, and a vehicle actuator 12. The vehicle perception and positioning module 10 and the trajectory tracking and control device 11 transmit status data and trajectory information in real time via a vehicle-mounted high-speed bus. The trajectory tracking and control device 11 and the vehicle actuator 12 establish a control command transmission link, forming a closed-loop control system. The vehicle perception and positioning module 10 is used to collect vehicle motion state parameters and output a reference trajectory generated by path planning. The vehicle actuator 12 includes a steering actuator and power and braking actuators.

[0055] The vehicle trajectory tracking control method provided in this application is applicable to intelligent vehicle autonomous driving trajectory tracking control scenarios, and is especially suitable for complex driving conditions such as high-speed driving, large curvature curves, and dynamic changes in vehicle speed.

[0056] Specifically, during trajectory tracking control, the trajectory tracking control device 11 first acquires the motion state parameters and reference trajectory sent by the vehicle perception and positioning module 10. Then, the trajectory tracking control device 11 calls its built-in target prediction model to predict the vehicle's future driving trajectory in the time domain. This model integrates the vehicle's lateral and longitudinal motion coupling characteristics with steering control delay characteristics. Next, based on the deviation between the reference trajectory and the predicted driving trajectory, the optimal front wheel steering angle control quantity and speed control quantity are obtained through a target optimization function. Then, based on the deviation between the speed control quantity and the vehicle's actual driving speed, the wheel torque control quantity is calculated. Finally, the trajectory tracking control device 11 outputs the front wheel steering angle control quantity and wheel torque control quantity to the vehicle actuator 12, driving the vehicle to accurately track the reference trajectory, effectively improving path tracking accuracy and driving stability under complex working conditions.

[0057] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0058] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0059] Figure 2 This is a schematic flowchart of a vehicle trajectory tracking control method provided in an embodiment of this application, as shown below. Figure 2 As shown, the execution subject of this embodiment is a vehicle trajectory tracking and control device. This device can be implemented through a computer program, or through a medium storing the relevant computer program, such as a USB flash drive and / or optical disc; alternatively, it can be implemented through a physical device that integrates or installs the relevant computer program, such as a chip or electronic device. The electronic device may be a computer or a server, etc. The vehicle trajectory tracking and control method provided in this embodiment includes the following steps:

[0060] S201. Obtain the vehicle's motion state parameters and reference trajectory.

[0061] The vehicle's motion state parameters can be collected and calculated in real time by an onboard multi-sensor fusion system. These parameters include the vehicle's lateral position, longitudinal position, and heading angle in the global coordinate system, as well as dynamic state variables such as longitudinal speed, lateral speed, sideslip angle, yaw rate, current actual front wheel steering angle, and wheel speed. The sensing system can consist of a navigation unit, wheel speed sensors, gyroscopes, and steering angle sensors. All parameters are sampled synchronously according to the control cycle, serving as the initial state input for trajectory prediction.

[0062] The reference trajectory, output by the upper-level path planning and decision-making module, is a discrete time-domain trajectory sequence. It includes reference quantities such as the expected lateral position, expected longitudinal position, expected heading angle, and expected driving speed corresponding to each sampling moment in the preset prediction time domain, and serves as the target benchmark for vehicle tracking.

[0063] S202. Based on motion state parameters, the target prediction model is called to predict the vehicle's trajectory in the future time domain. The target prediction model integrates the vehicle's lateral and longitudinal motion coupling characteristics and steering control delay characteristics.

[0064] Among them, the target prediction model is a pre-calibrated augmented state-space dynamics model. The target prediction model integrates the vehicle's lateral and longitudinal motion coupling characteristics with the steering control delay characteristics, avoiding the limitations of the ideal assumption of "no delay and no coupling" in the decoupled model.

[0065] It should be noted that existing trajectory tracking solutions generally adopt a lateral-longitudinal decoupling architecture, which assumes that lateral steering motion and longitudinal velocity motion are independent and designs separate controllers. However, in high-speed driving and high-curvature curve scenarios, the lateral-longitudinal dynamic coupling effect cannot be ignored: changes in longitudinal vehicle speed directly change tire lateral stiffness, significantly affecting lateral dynamic response; while the lateral force and driving resistance generated by lateral steering also react on the longitudinal velocity. Decoupling models that ignore this coupling relationship will cause the trajectory prediction error to increase sharply with increasing vehicle speed and curvature.

[0066] The target prediction model provided in this embodiment is based on the vehicle's two-degree-of-freedom dynamics model. It extends and introduces the longitudinal dynamics equation and the tire mechanics coupling model, incorporating the nonlinear relationship between tire lateral force and longitudinal force with slip ratio, sideslip angle, and vertical load into the model. It establishes a state-space equation that couples the lateral and longitudinal forces, enabling the model to accurately reflect the vehicle's real motion response under time-varying speed and cornering conditions.

[0067] It should be noted that actual steering systems generally suffer from three types of delays: controller calculation delay, on-board bus transmission delay, and actuator mechanical response delay. The total delay typically reaches tens to hundreds of milliseconds, causing the actual front wheel steering angle to lag behind the control command, resulting in control lag, amplified trajectory deviation, and even control oscillation. Existing solutions mostly treat the delay as interference and correct it through feedback, but they cannot eliminate the lag effect at its root.

[0068] Optionally, the steering system can be equivalent to a time-delay model of "pure lag element and first-order inertial element". The time-delay characteristics can be embedded into the target prediction model through the augmented state space method: the front wheel steering angle control quantity at the historical moment is included as an augmented state quantity in the model state vector, the pure time delay is transformed into a recursive relationship of state quantities, and the response lag characteristics of the actuator are characterized by the first-order inertial coefficient.

[0069] Therefore, the predicted trajectory output by the target prediction model takes into account the actual driving trajectory that the vehicle can achieve after the turning delay, rather than the theoretical trajectory under the ideal instantaneous turning angle, thus eliminating the prediction deviation caused by the delay from the source of prediction.

[0070] Optionally, the acquired vehicle motion state parameters are used as initial values ​​and input into the target prediction model. The model is then recursively calculated within a preset prediction time domain to obtain a sequence of vehicle states (including parameters such as position, heading angle, and speed) at multiple future times. Finally, the predicted vehicle trajectory in the future time domain is output.

[0071] S203. Based on the deviation between the reference trajectory and the predicted vehicle trajectory, the objective optimization function is used to optimize and solve the problem, thereby obtaining the front wheel steering angle control quantity and speed control quantity.

[0072] Optionally, the predicted vehicle trajectory is matched one by one with the acquired reference trajectory at points in the same time domain to calculate the lateral position deviation, heading angle deviation, and longitudinal velocity deviation at each moment in the prediction time domain.

[0073] Optionally, the objective optimization function can take "minimizing tracking deviation and ensuring smooth control" as multiple optimization objectives, and include a tracking error term and a control smoothing term. The tracking error term is obtained by weighting and summing the lateral position deviation, heading angle deviation, and longitudinal velocity deviation within the prediction time domain, with weighting coefficients applied respectively; its core purpose is to ensure trajectory tracking accuracy. The control smoothing term is obtained by weighting and summing the front wheel steering angle increment (the change in steering angle between adjacent control cycles) and longitudinal acceleration increment, with weighting coefficients applied respectively; it is used to limit abrupt changes in control quantities, avoid mechanical shocks to the actuators, and ensure smooth driving.

[0074] Optionally, physical constraints can also be set during the optimization process, including: maximum front wheel steering angle constraint, upper and lower limits of longitudinal acceleration / deceleration constraint, and tire adhesion limit constraint.

[0075] Optionally, the above objective optimization function and constraints are transformed into a standard quadratic programming problem, and a rolling solution is completed in each control cycle to obtain the optimal control quantity sequence in the prediction time domain. According to the principle of "rolling optimization and only taking the first step" in model predictive control, only the control quantity at the first moment in the optimal control quantity sequence is taken as the effective output of the current cycle, that is, the front wheel steering angle control quantity and speed control quantity at the current moment are obtained, and the speed control quantity is output in the form of desired longitudinal acceleration or desired vehicle speed.

[0076] S204. Determine the wheel torque control amount based on the deviation between the speed control amount and the actual vehicle speed.

[0077] This step is the conversion link from upper-level control commands to lower-level execution commands, which transforms the optimized speed control quantity into wheel torque commands that the vehicle's actuators can directly respond to.

[0078] Optionally, taking the desired longitudinal acceleration corresponding to the speed control quantity as the target, and combining the actual driving speed of the vehicle with the vehicle parameters, based on the vehicle's longitudinal dynamics equation, the basic torque required to overcome rolling resistance, air resistance, and slope resistance, as well as the additional torque required to achieve the target acceleration, are calculated to obtain the total required torque of the vehicle. Then, according to the vehicle's drive type (front-wheel drive, rear-wheel drive, four-wheel drive) and braking system configuration, the total required torque is distributed to each wheel to obtain the driving torque or braking torque corresponding to each wheel, i.e., the wheel torque control quantity.

[0079] Optionally, longitudinal speed tracking is achieved using a PID (Proportion Integration Differentiation) controller, which calculates the required driving torque or braking torque of the vehicle based on the deviation between the target vehicle speed and the actual vehicle speed.

[0080] Specifically, the tracking error is defined as:

[0081]

[0082] in, For speed tracking error, Indicates the desired target speed. This indicates the speed of the controlled vehicle model. For acceleration tracking error, This represents the desired longitudinal acceleration. This represents the longitudinal acceleration of the controlled vehicle model. This is the integral term of the speed tracking error. The system controls the sampling period to be consistent with the sampling period of the prediction model.

[0083] Specifically, in a PID controller, when the actual vehicle speed is lower than the target vehicle speed, the output driving torque is... , Braking torque When the actual vehicle speed is higher than the target vehicle speed, the braking torque is output. , , When the speed of the controlled vehicle is equal to the speed of the target vehicle, , .

[0084] in, This is the proportionality coefficient. The integral coefficient is... These are differential coefficients, which can be determined through actual vehicle calibration or simulation debugging. After obtaining the total required torque for the entire vehicle, the total required torque is then distributed to each wheel according to the vehicle's drive type (front-wheel drive, rear-wheel drive, four-wheel drive) and braking system configuration, resulting in the driving torque or braking torque corresponding to each wheel, i.e., the wheel torque control quantity.

[0085] It should be noted that the hierarchical torque conversion design allows the upper-level optimization control to focus on the execution details of the lower level, improving the algorithm's computational efficiency and making it adaptable to vehicles with different chassis configurations.

[0086] S205. Output the front wheel steering angle control quantity and wheel torque control quantity to the vehicle actuator to control the vehicle to follow the reference trajectory.

[0087] Specifically, the front wheel steering angle control is output to the steering actuator (such as an electric power steering system or a steer-by-wire system) via the vehicle bus, controlling the front wheels to rotate to the target steering angle; the wheel torque control is output to the drive controller (such as a motor controller) and the brake controller (such as a brake-by-wire system), respectively, controlling the vehicle to output the corresponding driving force or braking force to achieve longitudinal speed adjustment. Through the coordinated response of the steering, drive, and braking actuators, the vehicle ultimately achieves high-precision and high-stability tracking of the reference trajectory.

[0088] The vehicle trajectory tracking control method provided in this application, by integrating the vehicle's lateral and longitudinal motion coupling characteristics with the steering control delay characteristics into a target prediction model, can accurately predict the vehicle's future trajectory in the future time domain under complex conditions such as time-varying vehicle speed and cornering, overcoming the shortcomings of existing lateral and longitudinal decoupled control architectures in terms of insufficient tracking accuracy in high-dynamic scenarios. Furthermore, by incorporating the steering control delay characteristics into the prediction model, problems such as control lag, increased trajectory deviation, and control oscillation caused by actuator response lag and data processing delay are avoided. Then, based on the deviation between the reference trajectory and the predicted trajectory, the front wheel steering angle control quantity and speed control quantity are jointly solved through a target optimization function, and the wheel torque control quantity is determined based on the speed deviation. This achieves coordinated optimization of lateral and longitudinal control, improving the trajectory tracking accuracy and driving safety of autonomous vehicles under harsh conditions such as high speeds and high curvature paths.

[0089] As an optional implementation, based on any of the above embodiments, the target prediction model construction step includes the following steps:

[0090] Specifically, a three-degree-of-freedom vehicle kinematics model and a steering control delay model consisting of a pure time-delay element and a first-order inertial element are constructed. The vehicle kinematics model uses longitudinal position, lateral position, and yaw angle as state variables, and longitudinal vehicle speed and front wheel steering angle as control inputs.

[0091] This implementation method is based on a simplified bicycle model and establishes a three-degree-of-freedom vehicle kinematic model in an inertial coordinate system to characterize the pose change law of the vehicle's planar motion, which serves as the computational basis for the target prediction model.

[0092] Optionally, the global inertial coordinate system OXY is used as the reference, and the center of the rear axle of the vehicle is selected as the origin of the vehicle coordinate system. The model is based on the assumption of low-speed driving scenario, that is, the tire slip angle is small and negligible during vehicle driving, the wheel rolling direction is consistent with the vehicle body orientation, and the instantaneous turning radius of the vehicle matches the road curvature radius.

[0093] Figure 3 This is a vehicle kinematic model provided as an embodiment of this application. Specifically, as shown... Figure 3 As shown, the vehicle kinematic model uses the longitudinal position (x) and lateral position (y) of the rear axle center in the global coordinate system, as well as the vehicle body yaw angle. The three physical quantities (heading angle, etc.) serve as state variables, corresponding to the three independent degrees of freedom of the vehicle's planar motion: longitudinal translation, lateral translation, and yaw rotation. They can completely describe the vehicle's position and attitude at any given moment. The model uses the vehicle's longitudinal speed v and front wheel steering angle δ as two control inputs, corresponding to the longitudinal drive / braking control dimension and the lateral steering control dimension, respectively, and corresponding one-to-one with the lateral and longitudinal control outputs of the trajectory tracking control. This refers to the front wheel deflection angle; , These are the center speeds of the front and rear axles of the vehicle, respectively; R is the steering radius of the rear wheels.

[0094] Based on the geometric constraints of rigid body planar motion, the vehicle's kinematic equations are expressed as:

[0095]

[0096] Where l is the vehicle wheelbase, and is an inherent geometric parameter of the vehicle. This model can deduce the vehicle's posture change law only through the geometric relationship between vehicle speed, front wheel steering angle and wheelbase, without the need for complex dynamic parameters such as tire lateral stiffness and vehicle rotational inertia. It has the characteristics of simple structure, easy parameter acquisition and low computational load.

[0097] Specifically, in response to the execution response lag and data processing delay in actual steering systems, this implementation constructs a steering control delay model consisting of a pure time-delay element and a first-order inertial element connected in series. This model reproduces the response process from control command to actual front wheel steering angle, thus solving the idealization deviation problem of the traditional model's assumption of instantaneous steering angle response.

[0098] The pure time delay is used to characterize the inherent pure delay characteristics of the steering control system, corresponding to the sum of the control algorithm calculation delay, the on-board bus signal transmission delay, and the steering actuator start-up delay. The pure time delay is denoted as... Its physical meaning is: the desired front wheel steering angle control command output at the current moment needs to wait for a fixed time. Only after a delay will the steering actuator begin to generate a turning response.

[0099] The first-order inertial element characterizes the mechanical response inertial characteristics of the steering actuator. Due to the mechanical inertia and damping constraints of the steering drive motor and steering transmission mechanism, the actual front wheel steering angle cannot instantaneously step to the target steering angle value, but rather exhibits a gradual inertial response pattern approaching the target value. The time constant of this element is denoted as . The larger the time constant, the smoother the steering response and the more significant the hysteresis effect; the smaller the time constant, the faster the steering response.

[0100] Optionally, the pure time-delay element and the first-order inertial element are connected in series, i.e., the desired front wheel steering angle is... First, a pure time-delay element is input, which then undergoes a fixed time... After the delay, a first-order inertial element is input for inertial smoothing, and the final output is the actual front wheel steering angle of the steering system. This serial structure fully replicates the real steering control process, from command issuance and delay waiting to mechanism response and angle output, and more closely matches the response characteristics of a real vehicle compared to a single-delay model. Model parameters and Calibration can be completed through a step response test of the steering system. Optionally, the expected front wheel steering angle in step form is used as input, and the response curve of the actual front wheel steering angle of the actual vehicle is collected. The corresponding pure lag time and inertial time constant can be obtained by fitting and matching. The calibration process is simple and easy to operate.

[0101] Specifically, the steering control delay model is integrated into the vehicle kinematics model.

[0102] Optionally, this step deeply integrates the steering control delay model with the vehicle kinematics model through state augmentation and model linearization discretization, so that the final target prediction model includes steering control delay characteristics, and the output prediction trajectory is the actual vehicle driving trajectory after considering the delay.

[0103] In the original three-degree-of-freedom vehicle kinematics model, the front wheel steering angle input was an ideal instantaneous value, without considering execution delay. During the fusion process, the front wheel steering angle... As a newly added state variable, the expected front wheel steering angle is included in the model's state vector. As a new control input, it achieves state unification between the delay model and the kinematic model. The augmented system state variables are: The control input is .

[0104] Specifically, by combining the vehicle's kinematic equations with the differential equations of the first-order inertial element, the continuous state-space equations of the augmented system are derived as follows:

[0105]

[0106] This equation integrates the dynamic response process of steering delay with the vehicle's kinematic response process into a unified state recursive relationship. This allows the model to simultaneously calculate the delayed response process of the front wheel steering angle when predicting changes in vehicle posture, and the prediction results directly correspond to the actual vehicle trajectory under actual steering angle drive.

[0107] Optionally, to adapt the rolling optimization solution to the model predictive control, at any reference point of the given reference trajectory... The above nonlinear model is expanded using Taylor, retaining first-order terms and ignoring higher-order terms, resulting in a linear time-varying model. Further discretization using the forward Euler method yields the discrete state-space equations, where the state transition matrix... With control matrix They are respectively:

[0108]

[0109]

[0110] In the formula, T is the control sampling period. , , These are the longitudinal speed, yaw angle, and front wheel steering angle corresponding to the reference point, respectively.

[0111] Optionally for pure time delay Compensation is achieved through the recursive calculation of historical control commands: Before each MPC (Model Predictive Control) optimization solution, the initial state of the vehicle after the pure time delay is predicted in advance based on the expected front wheel steering angle control command at a historical moment. This predicted state is used as the initial state input for the MPC solution of the current control cycle, thereby incorporating the pure time delay characteristics into the initial conditions of the prediction time domain and fully covering all the delay effects of the steering system.

[0112] The vehicle trajectory tracking control method provided in this application constructs a three-degree-of-freedom vehicle kinematic model, using longitudinal position, lateral position, and yaw angle as state variables, and longitudinal vehicle speed and front wheel steering angle as control inputs. This method can comprehensively describe the vehicle's planar motion characteristics while ensuring computational efficiency. Furthermore, it employs a structure combining a pure time-delay element and a first-order inertial element in series to characterize steering control delay. Compared to a single-element model, this more accurately portrays the combined dynamic characteristics of actuator response lag and data processing delay. Integrating this delay model into the kinematic model allows the target prediction model to compensate for the impact of steering delay on the vehicle trajectory in the time domain, thereby effectively suppressing trajectory deviations and oscillations caused by control delay in scenarios such as time-varying vehicle speeds and cornering, thus improving prediction accuracy.

[0113] As an optional implementation, based on any of the above embodiments, the following steps are also included:

[0114] Using the front wheel steering angle control value as input and the actual front wheel steering angle as a comparison benchmark, the time constant in the steering control delay model is calibrated to obtain model parameters that match the actual steering characteristics of the vehicle.

[0115] Optionally, a calibration test environment should be set up first. This can be done by either bench testing of the steering system or static testing of the actual vehicle. During the test, the vehicle steering system should be kept in normal working condition to eliminate the influence of external factors such as driving load and road interference on the calibration accuracy.

[0116] Optionally, during testing, a preset front wheel steering angle control value (i.e., the desired front wheel steering angle) is input to the vehicle steering controller as an excitation signal. The excitation signal can be a step signal or a sinusoidal sweep frequency signal to cover dynamic conditions. The time-domain response data of the actual output front wheel steering angle is synchronously collected by the vehicle steering angle sensor, and the corresponding timestamps of the control command issuance time and the actual steering angle response are accurately recorded to provide aligned input-output data pairs for subsequent parameter identification.

[0117] Furthermore, to ensure that the calibration accuracy covers the entire working range of the steering system, multiple sets of step angle commands with different amplitudes can be set, corresponding to small angle, medium angle and large angle steering conditions respectively, and the corresponding response data can be collected one by one to avoid the parameter limitations caused by single-condition calibration.

[0118] Specifically, based on the collected input and output data, the two types of time constants included in the steering control delay model, namely the pure time delay and the time constant of the first-order inertial element, are identified and calibrated respectively.

[0119] Optionally, for the calibration of the pure time delay, the moment the step angle control command is issued is taken as the time starting point, and the moment when the actual front wheel steering angle response first deviates from the initial value and exceeds the set judgment threshold is taken as the response starting point. The time difference between the two moments is the pure time delay. This parameter corresponds to the total delay time of control algorithm calculation, vehicle bus signal transmission, and steering actuator start-up, accurately reflecting the inherent pure time delay characteristics of the steering system.

[0120] Optionally, for the calibration of the first-order inertial time constant, after deducting the pure time delay, parameter fitting is performed based on the step response characteristics of the first-order inertial system. For a standard first-order inertial system, the time corresponding to when the output rises to 63.2% of the steady-state value under a step input is the system time constant, and preliminary parameters can be directly read from the response curve; alternatively, the least squares method can be used for accurate solution, with the optimization objective being to minimize the sum of squared errors between the model output rotation angle and the actual vehicle rotation angle, and the time constant is iteratively calculated to obtain the globally optimal fitting parameters.

[0121] Optionally, after completing the initial parameter identification, the model accuracy is verified using independent test data that was not involved in the calibration. The same front wheel steering angle control quantity can be input into the steering control delay model equipped with calibrated parameters. The output model predicts the steering angle response curve, which is then compared with the actual steering angle response curve of the real vehicle. If the phase deviation and amplitude deviation of both are within the preset allowable range, the calibration is deemed successful, and this set of time constant parameters is fixed as the parameters of the target prediction model. If the error exceeds the allowable range, the identification conditions are adjusted, test data is supplemented, and the parameters are solved again until the accuracy requirements are met.

[0122] As an optional implementation, based on any of the above embodiments, before calling the target prediction model to predict the vehicle's trajectory, the following steps are also included:

[0123] First, based on historical front wheel steering angle control values, the current vehicle motion parameters are predicted in advance.

[0124] Among them, the timing cache of historical front wheel steering angle control quantities is set in the vehicle controller. According to the system control sampling cycle, it stores all front wheel steering angle control quantities issued within a preset time period in the past, i.e., the expected front wheel steering angle commands. The total cache duration is equal to the pure time delay obtained from the steering system calibration. Matching.

[0125] It should be noted that, due to the pure time lag characteristic of the steering system, the time before the current moment... Control commands issued within the specified time frame have not yet been applied to the steering actuators and are considered historical control commands awaiting activation; while the vehicle's actual steering state and motion response at the current moment correspond to... The execution results of control commands issued before a certain time have a fixed time-domain misalignment.

[0126] Optionally, the actual vehicle motion parameters (including longitudinal position, lateral position, yaw angle, actual front wheel steering angle, etc.) obtained by the vehicle sensing system at the current moment are used as the initial values ​​for recursion. The calibrated augmented vehicle kinematics model that integrates the first-order inertial element is called, and the cached historical front wheel steering angle control values ​​are used as model inputs in chronological order. The calculation is recursively performed along the time axis. The total recursion time is equal to the pure lag time of the steering system. Finally, the vehicle motion parameters after the pure lag time are obtained, which is the advanced predicted vehicle state.

[0127] Optionally, the recursive process adopts an discrete step size consistent with the system control cycle. Each step of the recursion corresponds to a state update of a sampling cycle, ensuring that the time accuracy of the advance prediction matches the control cycle and avoiding the introduction of additional time deviations and state errors.

[0128] Secondly, the predicted vehicle motion parameters are used as the initial input state for the target prediction model.

[0129] Optionally, after the advanced prediction state is used as the initial input of the model to complete the advanced recursive calculation, the obtained advanced predicted vehicle motion parameters are used to replace the measured vehicle state at the current moment, and serve as the initial input state for the target prediction model to perform future time-domain trajectory prediction. The entire process of subsequent model predictive control (MPC), including trajectory prediction, rolling optimization solution, and control output, is based on this advanced compensated initial state.

[0130] The vehicle trajectory tracking control method provided in this application predicts the current vehicle motion parameters based on historical front wheel steering angle control values ​​and uses the prediction results as the initial input state of the target prediction model. This allows the model to have initial conditions that are closer to the actual motion trend at the start of the prediction, thereby shortening the convergence process of the prediction model and improving the initial accuracy of trajectory prediction in the future time domain. Especially in transient conditions with rapid changes in vehicle speed or frequent adjustments in front wheel steering angle, it can effectively avoid prediction drift caused by initial state deviation.

[0131] As an optional implementation, based on any of the above embodiments, before determining the wheel torque control amount, the following steps are also included:

[0132] First, the deviation between the speed control quantity and the reference vehicle speed corresponding to the reference trajectory is compared with a preset threshold.

[0133] Optionally, the speed control quantity obtained through the rolling solution of the objective optimization function is acquired. This speed is the output result of the lateral and longitudinal co-optimization under the model predictive control framework, used to match the dynamic requirements of lateral steering control and achieve optimal trajectory tracking accuracy. The reference vehicle speed at the corresponding time-domain position in the reference trajectory is acquired. This reference vehicle speed is output by the upper-level path planning module and is generated based on constraints such as road curvature, speed limit rules, traffic conditions, and vehicle adhesion limits. It is the expected driving speed with safety benchmark attributes.

[0134] Optionally, using the same time-domain location as a reference, the difference between the speed control quantity and the reference vehicle speed is calculated, and the absolute value of the deviation is taken as a comparison quantity. The deviation value is then compared with a preset threshold.

[0135] The preset threshold is a pre-calibrated safety threshold parameter. The value of the preset threshold can be statically calibrated based on vehicle dynamics characteristics and tire adhesion limits, or dynamically adjusted by combining real-time road curvature, road surface adhesion coefficient, and other sensing information. For example, a smaller deviation threshold is set for high-curvature curves to strictly constrain cornering speed and ensure yaw stability; a larger deviation threshold is set for straight roads to retain more speed adjustment space and improve tracking efficiency.

[0136] Secondly, based on the comparison results, the target speed for calculating the wheel torque control quantity is determined.

[0137] Optionally, based on the comparison between the deviation and the threshold, the step of selecting the target speed for calculating the wheel torque control quantity using two strategies includes: if the deviation between the speed control quantity and the reference vehicle speed is greater than a preset threshold, then the smaller value between the speed control quantity and the reference vehicle speed is taken as the target speed; if the deviation between the speed control quantity and the reference vehicle speed is not greater than the preset threshold, then the speed control quantity is taken as the target speed.

[0138] Specifically, when the deviation between the speed control quantity and the reference vehicle speed is less than or equal to a preset threshold, it is determined that the current speed control quantity output by the MPC optimization is within a safe and permissible range, and the speed adjustment will not exceed the vehicle's stable driving boundary. At this time, the speed control quantity is directly determined as the final target speed to maximize the retention of the control effect of lateral and longitudinal collaborative optimization and ensure trajectory tracking accuracy.

[0139] Specifically, when the deviation between the speed control quantity and the reference vehicle speed is greater than the preset threshold, it is determined that the deviation between the currently optimized speed control quantity and the planned safe vehicle speed is too large. If it is executed directly, it may lead to risks such as exceeding the speed limit when cornering and excessive longitudinal impact. At this time, the smaller value between the speed control quantity and the reference vehicle speed is selected as the target speed. The principle of reducing speed and maintaining stability is prioritized to ensure the yaw stability and safety of the vehicle and avoid safety hazards such as sideslip and loss of control caused by excessive speed.

[0140] The vehicle trajectory tracking control method provided in this application adaptively determines the target speed for calculating the wheel torque control quantity by comparing the deviation between the speed control quantity and the reference vehicle speed corresponding to the reference trajectory with a preset threshold. When the deviation is large, the smaller value between the speed control quantity and the reference vehicle speed is used as the target speed, which is equivalent to automatically introducing a conservative speed limit strategy under high-speed conditions to prevent the vehicle from speeding in areas such as curves, causing sideslip or trajectory deviation due to overly aggressive speed control. When the deviation is small, the speed control quantity is directly used to avoid unnecessary speed suppression affecting tracking efficiency, thereby achieving a dynamic balance between safety and tracking accuracy. Especially under conditions such as high curvature paths and sudden speed changes, it can effectively constrain the rationality of wheel torque output and improve the overall control safety and smoothness.

[0141] As an optional implementation, based on any of the above embodiments, optimization is performed using an objective function, including the following steps:

[0142] Construct an objective optimization function that includes constraints on position tracking error, heading angle tracking error, and control variable changes.

[0143] The tracking error term corresponds to the tracking deviation throughout the entire time period in the prediction time domain Np. It is used to constrain the degree of fit between the vehicle's predicted driving trajectory and the reference trajectory, and includes two sub-terms: position tracking error and heading angle tracking error.

[0144] Wherein, the position tracking error refers to the difference between the predicted longitudinal position x(k) and lateral position y(k) of the vehicle at each sampling time in the prediction time domain and the reference longitudinal position at the corresponding time of the reference trajectory. Reference horizontal position The squared deviation value is used to configure the longitudinal position weight coefficient. With lateral position weighting coefficient This item directly determines the positional accuracy of trajectory tracking and is the core objective of optimization. In practical applications, the weight ratio can be adjusted according to control requirements. For example, in conventional road driving scenarios, the weight of lateral position can be increased to enhance the accuracy of lateral path following.

[0145] Among them, the heading angle tracking error refers to the predicted yaw angle (heading angle) of the vehicle at each sampling time in the prediction time domain. (k) Reference heading angle at the time corresponding to the reference trajectory The squared deviation value of (k) is used to configure the heading angle weighting coefficient. The heading angle deviation directly affects the accumulation rate of subsequent position deviations. Especially under the driving conditions of large curvature curves, accurate heading angle tracking can effectively reduce the overshoot of lateral position deviation and improve the dynamic stability of curve tracking.

[0146] The tracking error term is mathematically expressed as the sum of the weighted error values ​​at each time point within the prediction time domain, i.e.:

[0147]

[0148] Among them, the control quantity change constraint term corresponds to the control increment constraint in the control time domain Nc, which is used to limit the change range of control quantity in adjacent control cycles, avoid the mechanical impact of sudden control command on the actuator, and suppress the control oscillation caused by steering delay, so as to ensure driving smoothness.

[0149] Specifically, the control inputs of this scheme include two types: longitudinal speed and front wheel steering angle. Therefore, the control variable change constraint corresponds to the longitudinal speed increment. v(k) and the increment of the front wheel steering angle δ(k) is configured with velocity increment weighting coefficients respectively. with the angle increment weighting coefficient A larger weighting coefficient results in a stronger constraint on abrupt changes in the control quantity, leading to a smoother control output; a smaller weighting coefficient results in a more sensitive control response, but with a corresponding decrease in smoothness. The mathematical expression of the control quantity change constraint term is the cumulative sum of the weighted values ​​of the control increments at each time point within the control time domain, i.e.:

[0150]

[0151] In summary, the complete objective function is the tracking error term. With control increment constraints The summation of these terms forms a standard quadratic form: .

[0152] Specifically, under preset constraints, the optimal control input sequence in the prediction time domain is obtained by solving.

[0153] Optionally, after constructing the objective function, the state-space equations of vehicle motion and physical limit constraints are used as boundary conditions to transform the optimization problem into a constrained standard mathematical programming problem for solution. The specific process is as follows:

[0154] Specifically, the preset constraints are used to limit the feasible domain of the optimization solution and ensure that the control commands obtained meet the physical limits of the vehicle and the requirements for driving safety. The constraints set in this embodiment can be divided into control quantity amplitude constraints, control increment amplitude constraints and system state constraints.

[0155]

[0156] In the formula, As system equality constraints, corresponding to the aforementioned augmented vehicle kinematics model incorporating steering control delay characteristics, the dynamic evolution of vehicle state variables with control input is described. In the formula, z is the system augmented state vector, corresponding to the state variables of the vehicle kinematics model incorporating steering control delay characteristics mentioned earlier. Let z be the first derivative of the state vector z with respect to time t. This constraint is the core premise for the optimization solution, ensuring that all predicted trajectories strictly follow the vehicle's kinematic characteristics and avoiding infeasible solutions that deviate from the vehicle's physical laws.

[0157] Among them, the control quantity amplitude constraint is the absolute physical limit constraint of the control input, which simultaneously limits the positive and negative upper and lower limits of the control quantity in absolute value form. Specifically, it includes the maximum longitudinal vehicle speed constraint and the maximum front wheel steering angle constraint. The maximum longitudinal vehicle speed constraint is as follows: This parameter is used to limit the maximum permissible amplitude of longitudinal speed control. Positive and negative values ​​correspond to the upper speed limits for forward and reverse driving, respectively. Its value is comprehensively calibrated based on the vehicle's maximum design speed, legal speed limits, and scenario safety requirements to ensure that speed control commands are always within the feasible range of the vehicle's power performance, eliminating the risk of speeding.

[0158] Among them, the maximum value constraint of the front wheel steering angle is: This parameter is used to limit the maximum deflection amplitude of the front wheel steering angle control. Positive and negative values ​​correspond to the limit angles for left and right steering, respectively. Its parameter value is determined by the inherent geometric characteristics of the vehicle steering system, such as mechanical limits and minimum turning radius, to ensure that the steering angle command does not exceed the physical travel of the steering mechanism and to avoid motor stalling and overload damage to the transmission mechanism.

[0159] Specifically, the control increment amplitude constraint is a constraint on the dynamic rate of change of the control input. It simultaneously limits the upper limit of the increase or decrease of the control quantity within a single control cycle in the form of absolute value, including the maximum value constraint of the longitudinal speed increment and the maximum value constraint of the front wheel steering angle increment.

[0160] Among them, the maximum value constraint of longitudinal velocity increment is: This parameter is used to limit the maximum increase or decrease in longitudinal speed within a single control cycle, corresponding to the dynamic limits of the vehicle's acceleration and deceleration capabilities. Its parameter values ​​are jointly calibrated by the maximum torque of the drive system, the maximum braking force of the braking system, and the longitudinal adhesion limit of the tires. This ensures that the change in speed command matches the response capability of the actuator, while also avoiding abrupt acceleration and deceleration, thus improving driving smoothness.

[0161] Among them, the maximum value constraint of the front wheel steering angle increment is: This parameter is used to limit the maximum deflection of the front wheel angle within a single control cycle, i.e., the upper limit of the steering rate. Its parameter values ​​are calibrated by the maximum power of the steering motor, the maximum angular velocity of the transmission mechanism, and the step response characteristics of the steering system. This not only matches the dynamic response speed of the steering system to avoid the expansion of the deviation between command and execution, but also limits the intensity of steering operations, suppresses control oscillations caused by delay, and improves steering stability at high speeds.

[0162] Optionally, the aforementioned linearized and discretized augmented state-space equations can be used as equality constraints, which together with the aforementioned inequality constraints form a complete set of constraints. Since the objective function is quadratic and the constraints are all linear, the entire optimization problem can be transformed into a standard quadratic programming (QP) problem.

[0163] Optionally, within each control cycle, the QP solver is invoked to solve the quadratic programming problem online, obtaining the optimal control increment sequence in the control time domain. Then, the optimal control input sequence in the prediction time domain is obtained by incremental accumulation, which includes the optimal longitudinal speed and the optimal front wheel angle at each time.

[0164] Optionally, after the solution is completed, only the control quantity of the first moment in the optimal control input sequence (the front wheel steering angle control quantity and speed control quantity of the current cycle) is output as a valid command to the subsequent control loop; after entering the next control cycle, the real-time motion status of the vehicle is re-acquired and the reference trajectory points are updated.

[0165] The vehicle trajectory tracking control method provided in this application constructs an objective optimization function that simultaneously includes position tracking error, heading angle tracking error, and control quantity change constraints. In the optimization solution, it takes into account the tracking accuracy requirements in both the lateral and longitudinal directions. At the same time, the control quantity change constraints effectively limit the drastic fluctuations in the front wheel steering angle and vehicle speed, preventing the actuator from experiencing response lag or control oscillations due to frequent and large-scale adjustments. Thus, the optimal control input sequence obtained under the preset constraints can achieve a smooth transition of control actions while ensuring tracking accuracy, thereby improving the trajectory tracking stability and ride comfort of the vehicle under complex conditions such as continuous curves and time-varying vehicle speeds.

[0166] As an optional implementation, based on any of the above embodiments, the following steps are also included:

[0167] First, obtain the first control increment in the optimal control input sequence.

[0168] Specifically, after the aforementioned rolling optimization solution is completed, the control time domain can be obtained. The optimal control increment sequence within the range is expressed in vector form as follows:

[0169]

[0170] in, Model predictive control (MPC) is the optimal control increment sequence in the control time domain obtained by solving at the t-th sampling time. Each element in the sequence... (k=0,1,…, Let be the optimal control increment corresponding to the (t+k)th control cycle; each control increment includes two dimensions: longitudinal speed increment and front wheel steering angle increment. In this step, only the control increment ranked first in the sequence is extracted. That is, the optimal control increment corresponding to the current control period t is used as the sole basis for calculating the current control quantity; the control increments at subsequent times in the control time domain are only used for optimization reference and are not directly issued to the actuator.

[0171] Secondly, based on the first control increment, the front wheel steering angle control quantity and speed control quantity at the current moment are determined.

[0172] Optionally, the front wheel steering angle control and speed control quantities output and executed in the previous control cycle t-1 are used as a reference, and accumulated with the extracted first control increment to obtain the final control quantity at the current time t. After calculation, these two control quantities are output to the subsequent longitudinal torque control and steering control links, respectively, to drive the vehicle actuators to complete the corresponding actions. After entering the next control cycle t+1, the real-time motion state of the vehicle is re-acquired, the reference trajectory points are updated, and the entire process of predictive model calculation, optimization solution, first-step increment extraction, and control quantity calculation is repeated to form a periodic rolling closed-loop control.

[0173] As an optional implementation, based on any of the above embodiments, after obtaining the reference trajectory, the following steps are further included:

[0174] The reference trajectory is discretized to obtain a sequence of reference trajectory points.

[0175] It should be noted that the reference trajectory output by the upper-level path planning module is typically a continuous path curve generated based on spline curves and polynomial fitting. It contains continuously changing motion state information such as longitudinal position, lateral position, heading angle, and reference vehicle speed in the global coordinate system, used to describe the vehicle's desired driving path and speed planning. Since the model predictive control in this embodiment is a discrete-time control system, its control cycle, prediction time domain, and control time domain are all based on fixed discrete time steps. The continuous trajectory cannot be directly substituted into the optimization framework to complete error calculation; therefore, the reference trajectory needs to be discretized and preprocessed first.

[0176] Optionally, the discretization process uses the vehicle control sampling period T as the time step reference and the trajectory point corresponding to the vehicle's current real-time position as the starting point. Sampling is performed sequentially at equal time intervals along the travel direction of the reference trajectory to obtain a sequence of reference trajectory points strictly aligned with the discrete control time domain. Each discrete reference trajectory point contains complete state variables such as the reference longitudinal position, reference lateral position, reference heading angle, and reference vehicle speed at the corresponding time. The length of the discretized reference trajectory point sequence matches the prediction time domain of the model predictive control, ensuring that the predicted vehicle state at each time point in the prediction time domain can find a corresponding reference benchmark for calculating the tracking error term.

[0177] Optionally, for road sections with drastic curvature changes, such as curves with large curvature, an equal arc length sampling method can be used to supplement the trajectory point density, ensuring the accuracy of trajectory description for curve sections and avoiding the loss of trajectory information caused by discretization.

[0178] Specifically, each reference trajectory point is tracked sequentially according to the order of the reference trajectory points until all reference trajectory points are tracked.

[0179] Optionally, the first reference trajectory point matched at the current position of the vehicle is used as the initial tracking target, and this point and the reference points within the subsequent predicted time domain are substituted into the target optimization function as the tracking benchmark.

[0180] Optionally, based on the model predictive control framework, the process of vehicle state acquisition, trajectory prediction, optimization solution, and control output is completed in each control cycle, driving the vehicle to approach the current target reference point and reduce the position and heading angle deviation.

[0181] Optionally, when the vehicle travels to a preset neighborhood of the current reference trajectory point, i.e., the positional deviation between the vehicle and the current reference point is less than a preset tracking threshold, it is determined that the tracking of the current reference point is complete, and the tracking target is switched to the next reference trajectory point in the sequence, and the rolling optimization tracking process is repeated. The tracking targets are advanced sequentially in the above order until the vehicle completes the tracking of the last reference trajectory point in the sequence, at which point the entire reference trajectory tracking task is determined to be complete, and the tracking control process ends.

[0182] Figure 4 A schematic flowchart of a vehicle trajectory tracking control method provided in another embodiment of this application is shown below. Figure 4 As shown, the vehicle trajectory tracking control method provided in this embodiment includes the following steps:

[0183] S301. Construct a three-degree-of-freedom vehicle kinematics model and a steering control delay model consisting of a pure time-delay element and a first-order inertial element connected in series.

[0184] S302. Integrate the steering control delay model into the vehicle kinematics model. Using the front wheel steering angle control value as input and the actual front wheel steering angle as a comparison benchmark, calibrate the time constant in the steering control delay model to obtain a target prediction model that integrates the vehicle's lateral and longitudinal motion coupling characteristics and steering control delay characteristics.

[0185] S303. Obtain the vehicle's motion state parameters and reference trajectory.

[0186] S304. Discretize the reference trajectory to obtain the reference trajectory point sequence.

[0187] S305. Based on the historical front wheel steering angle control values, predict the current vehicle motion parameters in advance, and use the predicted vehicle motion parameters as the initial input state of the target prediction model.

[0188] S306. Based on the initial input state, call the target prediction model to predict the vehicle's trajectory in the future time domain.

[0189] S307. Based on the deviation between the reference trajectory and the predicted vehicle trajectory, construct an objective optimization function that includes position tracking error, heading angle tracking error, and control variable change constraints.

[0190] S308. Under preset constraints, optimize the objective function to obtain the optimal control input sequence in the prediction time domain.

[0191] S309. Obtain the first control increment in the optimal control input sequence, and determine the front wheel steering angle control quantity and speed control quantity at the current moment based on the first control increment.

[0192] S310. Compare the deviation of the speed control quantity with the reference vehicle speed corresponding to the reference trajectory and the preset threshold, and determine the target speed for calculating the wheel torque control quantity based on the comparison result.

[0193] Specifically, if the deviation between the speed control quantity and the reference vehicle speed is greater than a preset threshold, the smaller value between the speed control quantity and the reference vehicle speed is used as the target speed; if the deviation between the speed control quantity and the reference vehicle speed is not greater than the preset threshold, the speed control quantity is used as the target speed.

[0194] S311. Determine the wheel torque control amount based on the deviation between the target speed and the actual vehicle speed.

[0195] S312. Output the front wheel steering angle control quantity and wheel torque control quantity to the vehicle actuator to control the vehicle to track the current reference trajectory point.

[0196] S313. Proceed with the tracking of the target in the order of the reference trajectory points until all reference trajectory points have been tracked.

[0197] The control steps S303 to S313 are executed repeatedly in a rolling manner.

[0198] It should be noted that the model construction and parameter calibration in S301-302 can be completed offline in advance and are not included in the real-time control loop. The implementation method and technical effect of each step in this embodiment are similar to the implementation method of the corresponding scheme in the above embodiments, and will not be repeated here.

[0199] Figure 5 This is a schematic diagram of a path tracking and PID speed control architecture based on a model prediction framework provided in an embodiment of this application. Figure 5 As shown, optionally, to more clearly illustrate the horizontal and vertical collaborative control architecture of the embodiments of this application, this embodiment provides a path tracking and PID speed control architecture based on a model prediction framework.

[0200] The control framework uses a model predictive controller as the core for lateral trajectory tracking and a PID controller as the core for longitudinal speed execution. The overall control logic is as follows:

[0201] The discretized reference trajectory is used as the tracking target of the model predictive controller. Combined with the real-time motion state of the vehicle, the future time-domain trajectory is predicted through the embedded augmented prediction model. The optimal front wheel steering angle control quantity and the optimal speed control quantity are obtained by rolling solution based on the objective optimization function. The deviation between the optimal speed control quantity and the reference vehicle speed corresponding to the reference trajectory is compared. After being filtered by a safety threshold, the target vehicle speed for longitudinal control is obtained and input into the PID speed controller. The PID controller calculates the wheel torque control quantity required by the whole vehicle. Finally, the front wheel steering angle control quantity and the wheel torque control quantity are synchronously output to the vehicle actuator to drive the vehicle to complete the lateral and longitudinal coordinated trajectory tracking control.

[0202] Figure 6 This is a schematic diagram of the structure of a vehicle trajectory tracking control device provided in an embodiment of this application, as shown below. Figure 6 As shown, the vehicle trajectory tracking control device provided in this embodiment is located in an electronic device. The vehicle trajectory tracking control device 40 provided in this embodiment includes: an acquisition module 41, a prediction module 42, a solution module 43, a determination module 44, and an output module 45.

[0203] Specifically, the acquisition module 41 is used to acquire the vehicle's motion state parameters and reference trajectory; the prediction module 42 is used to predict the vehicle's future driving trajectory in the time domain based on the motion state parameters by calling a target prediction model, which integrates the vehicle's lateral and longitudinal motion coupling characteristics and steering control delay characteristics; the solution module 43 is used to optimize the solution using a target optimization function based on the deviation between the reference trajectory and the predicted vehicle driving trajectory, to obtain the front wheel steering angle control quantity and speed control quantity; the determination module 44 is used to determine the wheel torque control quantity based on the deviation between the speed control quantity and the vehicle's actual driving speed; and the output module 45 is used to output the front wheel steering angle control quantity and wheel torque control quantity to the vehicle's actuator to control the vehicle to follow the reference trajectory.

[0204] Optionally, the vehicle trajectory tracking control device provided in this embodiment further includes a model building module. Optionally, when building the target prediction model, the model building module is specifically used to: build a three-degree-of-freedom vehicle kinematic model and a steering control delay model consisting of a pure time-lapse element and a first-order inertial element connected in series; and integrate the steering control delay model into the vehicle kinematic model.

[0205] Optionally, the vehicle kinematics model uses longitudinal position, lateral position, and yaw angle as state variables, and longitudinal vehicle speed and front wheel steering angle as control inputs.

[0206] Optionally, the vehicle trajectory tracking control device provided in this embodiment further includes a parameter calibration module. Optionally, the parameter calibration module is used to: take the front wheel steering angle control quantity as input, take the actual front wheel steering angle as a comparison benchmark, calibrate the time constant in the steering control delay model, and obtain model parameters that match the actual steering characteristics of the vehicle.

[0207] Optionally, before calling the target prediction model to predict the vehicle's driving trajectory, the prediction module 42 is also used to: predict the current vehicle motion parameters in advance based on the historical front wheel steering angle control value; and use the predicted vehicle motion parameters as the initial input state of the target prediction model.

[0208] Optionally, before determining the wheel torque control quantity, the determining module 44 is further used to: compare the deviation between the speed control quantity and the reference vehicle speed corresponding to the reference trajectory with a preset threshold; and determine the target speed for calculating the wheel torque control quantity based on the comparison result.

[0209] Optionally, the determining module 44, when determining the target speed for calculating the wheel torque control quantity, is specifically used to: if the deviation between the speed control quantity and the reference vehicle speed is greater than a preset threshold, then the smaller value between the speed control quantity and the reference vehicle speed is taken as the target speed.

[0210] Optionally, the determining module 44 is further configured to: if the deviation between the speed control quantity and the reference vehicle speed is not greater than a preset threshold, then use the speed control quantity as the target speed.

[0211] Optionally, when using the objective optimization function for optimization, the solution module 43 is specifically used to: construct an objective optimization function that includes position tracking error, heading angle tracking error and control quantity change constraints; and, under preset constraints, solve for the optimal control input sequence in the prediction time domain.

[0212] Optionally, the solver module 43 is also used to: obtain the first control increment in the optimal control input sequence; and determine the front wheel steering angle control quantity and speed control quantity at the current moment based on the first control increment.

[0213] Optionally, the vehicle trajectory tracking control device provided in this embodiment further includes a discrete module.

[0214] Optionally, after acquiring the reference trajectory, the discretization module is used to: discretize the reference trajectory to obtain a sequence of reference trajectory points; and track each reference trajectory point sequentially according to the order of the reference trajectory points until the tracking of all reference trajectory points is completed.

[0215] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, as shown below. Figure 7 As shown, the electronic device 50 provided in this embodiment includes: a processor 51 and a memory 52 communicatively connected to the processor 51.

[0216] The memory 52 stores computer-executable instructions; the processor 51 executes the computer-executable instructions stored in the memory 52 to implement the method provided in any of the above embodiments.

[0217] The program may include program code, which includes computer-executable instructions. Memory 52 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device.

[0218] In this embodiment, the memory 52 and the processor 51 are connected via a bus. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The bus is represented by a single straight line, but this does not mean that there is only one bus or one type of bus.

[0219] This application also provides a computer-readable storage medium, including computer-executable instructions stored in the computer-readable storage medium, which, when executed by a processor, are used to implement the method provided in any of the above embodiments.

[0220] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the method provided in any of the above embodiments.

[0221] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0222] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0223] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0224] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0225] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0226] The aforementioned storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0227] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0228] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0229] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A vehicle trajectory tracking control method, characterized in that, include: Acquire the vehicle's motion state parameters and reference trajectory; Based on the motion state parameters, the target prediction model is invoked to predict the vehicle's trajectory in the future time domain. The target prediction model integrates the vehicle's lateral and longitudinal motion coupling characteristics with steering control delay characteristics. Based on the deviation between the reference trajectory and the predicted vehicle trajectory, an objective optimization function is used to optimize and solve the problem, resulting in the front wheel steering angle control quantity and speed control quantity. The wheel torque control amount is determined based on the deviation between the speed control amount and the actual vehicle speed. The front wheel steering angle control value and the wheel torque control value are output to the vehicle actuator to control the vehicle to follow the reference trajectory.

2. The method according to claim 1, characterized in that, The steps for constructing the target prediction model include: A three-degree-of-freedom vehicle kinematic model and a steering control delay model consisting of a pure time-delay element and a first-order inertial element are constructed. The steering control delay model is integrated into the vehicle kinematics model.

3. The method according to claim 2, characterized in that, The vehicle kinematics model uses longitudinal position, lateral position, and yaw angle as state variables, and longitudinal speed and front wheel steering angle as control inputs.

4. The method according to claim 2, characterized in that, Also includes: Using the front wheel steering angle control value as input and the actual front wheel steering angle as a comparison benchmark, the time constant in the steering control delay model is calibrated to obtain model parameters that match the actual steering characteristics of the vehicle.

5. The method according to claim 1, characterized in that, Before calling the target prediction model to predict the vehicle's trajectory, the following steps are also included: Based on historical front wheel steering angle control values, the current vehicle motion parameters are predicted in advance. The predicted vehicle motion parameters are used as the initial input state for the target prediction model.

6. The method according to claim 1, characterized in that, Before determining the wheel torque control amount, the method further includes: The deviation between the speed control quantity and the reference vehicle speed corresponding to the reference trajectory is compared with a preset threshold. Based on the comparison results, the target speed for calculating the wheel torque control quantity is determined.

7. The method according to claim 6, characterized in that, Determining the target speed for calculating the wheel torque control quantity includes: If the deviation between the speed control value and the reference vehicle speed is greater than a preset threshold, then the smaller value between the speed control value and the reference vehicle speed is taken as the target speed.

8. The method according to claim 7, characterized in that, Also includes: If the deviation between the speed control quantity and the reference vehicle speed is not greater than the preset threshold, then the speed control quantity is taken as the target speed.

9. The method according to claim 1, characterized in that, The optimization is performed using an objective function, including: Construct an objective optimization function that includes constraints on position tracking error, heading angle tracking error, and control variable changes; Under preset constraints, the optimal control input sequence in the prediction time domain is obtained by solving the problem.

10. The method according to claim 9, characterized in that, Also includes: Obtain the first control increment in the optimal control input sequence; Based on the first control increment, the front wheel steering angle control amount and speed control amount at the current moment are determined.

11. The method according to any one of claims 1-10, characterized in that, After obtaining the reference trajectory, the method further includes: The reference trajectory is discretized to obtain a sequence of reference trajectory points; Track each reference trajectory point sequentially according to the reference trajectory point order until all reference trajectory points have been tracked.

12. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-11.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-11.

14. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-11.

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