System and method for estimating heading and yaw rate of an autonomous vehicle

Abstract

A motion control system and method are provided in the vehicle. In one embodiment, the motion control system includes a controller. The controller is configured to: receive target trajectory data associated with an upcoming trajectory of the autonomous vehicle; determine a yaw rate reference and a relative heading reference associated with the upcoming target trajectory based on the numerical integration of the target trajectory data; and control the trajectory of the autonomous vehicle based on the yaw rate reference and the relative heading reference.

Description

Technical Field

[0001] The technical field generally relates to vehicle motion control systems, and more specifically to improving the performance of motion control systems based on heading and yaw rate control references for trajectory tracking. Background Technology

[0002] The standard heading and yaw rate references are set to be equal to the target trajectory heading and trajectory curvature. The heading reference may sometimes be supplemented with a steady-state sideslip angle to compensate for non-zero relative yaw angles during cornering. These methods assume the references are quasi-stable and are only effective during highway driving. In low-speed maneuvers, limited yaw acceleration and sideslip rate of change are important.

[0003] Therefore, it is desirable to provide improved heading and yaw rate references for trajectory tracking. It is also desirable to use trajectory tracking to improve motion control using trajectory tracking. Furthermore, other desirable features and characteristics of this disclosure will become apparent from the following detailed description and appended claims, in conjunction with the accompanying drawings and the foregoing description of the technical field and background art. Summary of the Invention

[0004] The vehicle is equipped with a motion control system. In one embodiment, the motion control system includes a controller. The controller is configured to: receive target trajectory data associated with an upcoming trajectory of the autonomous vehicle; determine a yaw rate reference and a relative heading reference associated with the upcoming target trajectory based on the numerical integration of the target trajectory data; and control the trajectory of the autonomous vehicle based on the yaw rate reference and the relative heading reference.

[0005] In various embodiments, the target trajectory data includes the desired vehicle speed.

[0006] In various embodiments, the target trajectory data includes trajectory curvature.

[0007] In various embodiments, the target trajectory data includes the road tilt angle.

[0008] In various embodiments, numerical integration is based on a bicycle model.

[0009] In various embodiments, the numerical integration of the yaw rate reference is based on:

[0010]

[0011] Among them, V x Indicates the desired speed. Let φ represent the desired trajectory, φ represent the road inclination angle, ψ represent the yaw rate reference, m represent the mass of the autonomous vehicle, g represent gravity, and C represent the yaw rate reference. r This indicates the lateral stiffness on the rear tires, where L represents the vehicle wheelbase. rIndicates the distance between the rear axle and the center of gravity, I zz Let l represent the vehicle's moment of inertia relative to the vertical axis, and l f This indicates the distance between the front axle and the center of gravity.

[0012] In various embodiments, the numerical integration relative to the heading reference is based on:

[0013]

[0014] Where β represents the relative heading, V x Indicates the desired speed. Let φ represent the desired trajectory, φ represent the road inclination angle, ψ represent the yaw rate reference, m represent the mass of the autonomous vehicle, g represent gravity, and C represent the yaw rate reference. r This indicates the lateral stiffness on the rear tires, where L represents the vehicle wheelbase. r Indicates the distance between the rear axle and the center of gravity, I zz Let l represent the vehicle's moment of inertia relative to the vertical axis, and l f This indicates the distance between the front axle and the center of gravity.

[0015] In another embodiment, a method includes: receiving target trajectory data associated with an upcoming trajectory of an autonomous vehicle by a processor; determining a yaw rate reference and a relative heading reference associated with the upcoming target trajectory by the processor based on numerical integration of the target trajectory data; and controlling the trajectory of the autonomous vehicle by the processor based on the yaw rate reference and the relative heading reference.

[0016] In various embodiments, the target trajectory data includes the desired vehicle speed.

[0017] In various embodiments, the target trajectory data includes trajectory curvature.

[0018] In various embodiments, the target trajectory data includes the road tilt angle.

[0019] In various embodiments, numerical integration is based on a bicycle model.

[0020] In various embodiments, the numerical integration of the yaw rate reference is based on:

[0021]

[0022] Among them, V x Indicates the desired speed. Let φ represent the desired trajectory, φ represent the road inclination angle, ψ represent the yaw rate reference, m represent the mass of the autonomous vehicle, g represent gravity, and C represent the yaw rate reference. r This indicates the lateral stiffness on the rear tires, where L represents the vehicle wheelbase. r Indicates the distance between the rear axle and the center of gravity, I zzLet l represent the vehicle's moment of inertia relative to the vertical axis, and l f This indicates the distance between the front axle and the center of gravity.

[0023] In various embodiments, the numerical integration relative to the heading reference is based on:

[0024]

[0025] Where β represents the relative heading, V x Indicates the desired speed. Let φ represent the desired trajectory, φ represent the road inclination angle, ψ represent the yaw rate reference, m represent the mass of the autonomous vehicle, g represent gravity, and C represent the yaw rate reference. r This indicates the lateral stiffness on the rear tires, where L represents the vehicle wheelbase. r Indicates the distance between the rear axle and the center of gravity, I zz Let l represent the vehicle's moment of inertia relative to the vertical axis, and l f This indicates the distance between the front axle and the center of gravity.

[0026] In another embodiment, a non-transitory computer-readable medium encoded with programming instructions configurable to cause a controller in a vehicle to perform a motion control method. The method includes: receiving target trajectory data associated with an upcoming trajectory of an autonomous vehicle; determining a yaw rate reference and a relative heading reference associated with the upcoming target trajectory based on numerical integration of the target trajectory data; and controlling the trajectory of the autonomous vehicle based on the yaw rate reference and the relative heading reference.

[0027] In various embodiments, the target trajectory data includes the desired vehicle speed, trajectory curvature, and road inclination angle.

[0028] In various embodiments, numerical integration is based on a bicycle model.

[0029] In various embodiments, the numerical integration of the yaw rate reference is based on:

[0030]

[0031] Furthermore, the numerical integration relative to the heading reference is based on:

[0032]

[0033] Among them, V x Indicates the desired speed. Let φ represent the desired trajectory, φ represent the road inclination angle, ψ represent the yaw rate reference, m represent the mass of the autonomous vehicle, g represent gravity, and C represent the yaw rate reference. r This indicates the lateral stiffness on the rear tires, where L represents the vehicle wheelbase. r Indicates the distance between the rear axle and the center of gravity, Izz Let l represent the vehicle's moment of inertia relative to the vertical axis, and l f This indicates the distance between the front axle and the center of gravity.

[0034] In various embodiments, numerical integration is performed based on the detected rolling time domain, and the initial conditions for numerical integration are reinitialized at the start of the time domain and at each cycle time of motion control.

[0035] In various embodiments, numerical integration is performed independently of each other along the rolling time domain, and the initial conditions of the numerical integration are reinitialized at the start of motion control. Attached Figure Description

[0036] In the following description, exemplary embodiments will be illustrated with reference to the accompanying drawings, wherein the same numerals denote the same elements, and wherein:

[0037] Figure 1 This is a block diagram illustrating an autonomous vehicle with a trajectory tracking system according to various embodiments;

[0038] Figure 2 This is a functional block diagram illustrating the features of an autonomous driving system for an autonomous vehicle according to various embodiments;

[0039] Figure 3 This is a data flow diagram illustrating the features of a trajectory tracking system for an autonomous driving system according to various embodiments;

[0040] Figure 4 These are illustrations of vehicles and vehicle dynamics according to various embodiments; and

[0041] Figure 5 and Figure 6 This is a process flowchart depicting an example process of trajectory tracking according to various embodiments. Detailed Implementation

[0042] The following detailed description is exemplary in nature only and is not intended to limit application and use. Furthermore, it is not intended to be bound by any express or implied theory set forth in the foregoing technical fields, background art, summary of the invention, or the following detailed description. As used herein, the term "module" means any hardware, software, firmware, electronic control components, processing logic, and / or processor device, individually or in any combination, including but not limited to: application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), electronic circuits, processors (shared, dedicated, or grouped) and memories executing one or more software or firmware programs, combinational logic circuits, and / or other suitable components providing the described functionality.

[0043] In this document, embodiments of the present disclosure may be described in terms of functional and / or logical block components and various processing steps. It should be understood that such block components can be implemented by any number of hardware, software, and / or firmware components configured to perform specified functions. For example, embodiments of the present disclosure may employ various integrated circuit components, such as memory elements, digital signal processing elements, logic elements, or lookup tables, capable of performing various functions under the control of one or more microprocessors or other control devices. Furthermore, those skilled in the art will understand that embodiments of the present disclosure can be practiced in conjunction with any number of systems, and the systems described herein are merely exemplary embodiments of the present disclosure.

[0044] For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, machine learning models, radar, lidar, image analysis, and other functional aspects of the system (and its various operating components) may not be described in detail herein. Furthermore, the connecting lines shown in the various figures included herein are intended to illustrate exemplary functional relationships and / or physical couplings between various elements. It should be noted that many alternative or additional functional relationships or physical connections may exist in the embodiments of this disclosure.

[0045] refer to Figure 1 According to various embodiments, the trajectory tracking system 100, generally shown as 100, is associated with vehicle 10. Typically, trajectory tracking system 100 receives environmentally sensed data from vehicle 10, processes the received data to calculate a reference heading and reference yaw rate coupled to the main vehicle dynamics, taking into account finite yaw acceleration and varying sideslip angles during cornering. In various embodiments, tracking system 100 uses references to single-point error feedback of trajectory tracking (such as error feedback and feedforward) and optimal control based on the receding horizon (such as model predictive control).

[0046] like Figure 1 As shown, vehicle 10 typically includes a chassis 12, a body 14, front wheels 16, and rear wheels 18. The body 14 is mounted on the chassis 12 and substantially surrounds the components of vehicle 10. The body 14 and chassis 12 may together form a frame. The wheels 16-18 are each rotatably coupled to the chassis 12 near a corresponding corner of the body 14.

[0047] In various embodiments, vehicle 10 is an autonomous vehicle, and trajectory tracking system 100 is incorporated into autonomous vehicle 10 (hereinafter referred to as autonomous vehicle 10). Autonomous vehicle 10 is, for example, a vehicle automatically controlled to transport passengers from one location to another. Vehicle 10 is depicted as a passenger car in the illustrated embodiment, but it should be understood that any other means of transportation, including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), ships, aircraft, etc., may also be used. In exemplary embodiments, autonomous vehicle 10 is a so-called Level 4 or Level 5 automation system. Level 4 system means “high automation”, referring to the performance of the automated driving system in specific driving modes in all aspects of a dynamic driving task, even if the human driver does not respond appropriately to intervention requests. Level 5 system means “full automation”, referring to the full-time performance of the automated driving system in all aspects of a dynamic driving task under all road and environmental conditions that can be managed by a human driver.

[0048] As shown in the figure, an autonomous vehicle 10 typically includes a propulsion system 20, a transmission system 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one controller 34, and a communication system 36. In various embodiments, the propulsion system 20 may include an internal combustion engine, an electric motor such as a traction motor, and / or a fuel cell propulsion system. The transmission system 22 is configured to transmit power from the propulsion system 20 to the wheels 16-18 according to a selectable speed ratio. According to various embodiments, the transmission system 22 may include a graded automatic transmission, a continuously variable transmission (CVT), or other suitable transmission. The braking system 26 is configured to provide braking torque to the wheels 16-18. In various embodiments, the braking system 26 may include a friction brake, a brake-by-wire brake, a regenerative braking system such as an electric motor, and / or other suitable braking systems. The steering system 24 affects the position of the wheels 16-18. Although the steering system 24 is depicted as including a steering wheel for illustrative purposes, in some embodiments contemplated within the scope of this disclosure, the steering system 24 may not include a steering wheel.

[0049] Sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the external and / or internal environment of the autonomous vehicle 10. Sensing devices 40a-40n may include, but are not limited to, radar, lidar, GPS, optical cameras, thermal cameras, ultrasonic sensors, and / or other sensors. Actuator system 30 includes one or more actuator devices 42a-42n that control one or more vehicle features, such as, but not limited to, propulsion system 20, drivetrain 22, steering system 24, and braking system 26. In various embodiments, vehicle features may also include internal and / or external vehicle features, such as, but not limited to, doors, trunk, and cabin features such as air, music, lighting, etc. (unnumbered).

[0050] Communication system 36 is configured to conduct wireless information communication (targeting) with other entities 48 such as, but not limited to, other vehicles (“V2V” communication), infrastructure (“V2I” communication), remote systems and / or personal devices. Figure 2 (Described in more detail). In an exemplary embodiment, communication system 36 is a wireless communication system configured to communicate via a wireless local area network (WLAN) using the IEEE 802.11 standard or by using cellular data communication. However, additional or alternative communication methods, such as Dedicated Short Range Communication (DSRC) channels, are also considered within the scope of this disclosure. DSRC channels refer to one-way or two-way short-to-medium range wireless communication channels specifically designed for automotive use, along with a corresponding set of protocols and standards.

[0051] Data storage device 32 stores data used for automatically controlling the autonomous vehicle 10. In various embodiments, data storage device 32 stores a defined map of the navigable environment. In various embodiments, the defined map may be predefined by a remote system and may be obtained from the remote system (for...). Figure 2 (Further detailed description). For example, the defined map can be assembled by a remote system, transmitted (wirelessly and / or via wire) to the autonomous vehicle 10, and stored in a data storage device 32. It is understood that the data storage device 32 may be part of the controller 34, separate from the controller 34, or part of the controller 34 and a separate system.

[0052] The controller 34 includes at least one processor 44 and a computer-readable storage device or medium 46. The processor 44 may be any custom or commercially available processor, central processing unit (CPU), graphics processing unit (GPU), auxiliary processor among several processors associated with the controller 34, semiconductor-based microprocessor (in the form of a microchip or chipset), macroprocessor, any combination thereof, or any device generally used for executing instructions. For example, the computer-readable storage device or medium 46 may include volatile and non-volatile memory in read-only memory (ROM), random access memory (RAM), and non-deletable memory (KAM). KAM is a persistent or non-volatile memory that can be used to store various operational variables when the processor 44 is powered off. The computer-readable storage device or medium 46 may be implemented using any of many known memory devices, such as PROM (programmable read-only memory), EPROM (electrical PROM), EEPROM (electrically erasable PROM), flash memory, or any other electrical, magnetic, optical, or combined memory device capable of storing data (some of which represents executable instructions used by the controller 34 in controlling the autonomous vehicle 10).

[0053] The instructions may include one or more separate programs, each of which includes an ordered list of executable instructions for implementing logical functions. When the processor 44 executes the instructions, the instructions receive and process signals from the sensor system 28, execute logic, calculations, methods, and / or algorithms for automatically controlling the components of the autonomous vehicle 10, and generate control signals to the actuator system 30 to automatically control the components of the autonomous vehicle 10 based on logic, calculations, methods, and / or algorithms. Although Figure 1 Only one controller 34 is shown, but embodiments of the autonomous vehicle 10 may include any number of controllers 34 that communicate via any suitable communication medium or combination of communication media and cooperate to process sensor signals, perform logic, calculations, methods and / or algorithms, and generate control signals to automatically control the features of the autonomous vehicle 10.

[0054] In various embodiments, as discussed in detail below, one or more instructions of controller 34 are embodied in trajectory tracking system 100, and when executed by processor 44, they process sensor data using a bicycle model to determine reference values ​​for heading and yaw rate, and use these reference values ​​to track the trajectory of the vehicle.

[0055] Now for reference Figure 3 And continue to refer to Figure 1The data flow diagram illustrates various embodiments of the Autonomous Driving System (ADS) 70, which may be embedded within the controller 34 and may include portions of the trajectory tracking system 100 according to various embodiments. That is, suitable software and / or hardware components of the controller 34 (e.g., processor 44 and computer-readable storage device 46) are used to provide the Autonomous Driving System 70 for use in conjunction with the vehicle 10.

[0056] Inputs to the autonomous driving system 70 may be received from the sensor system 28, from other control modules (not shown) associated with the autonomous vehicle 10, from the communication system 36, and / or determined / modeled by other submodules (not shown) within the controller 34. In various embodiments, the instructions for the autonomous driving system 70 may be organized by function or system. For example, such as... Figure 2 As shown, the autonomous driving system 70 may include a computer vision system 74, a positioning system 76, a guidance system 78, and a vehicle control system 80. It will be understood that, in various embodiments, because this disclosure is not limited to this example, instructions may be organized into any number of systems (e.g., combined, further divided, etc.).

[0057] In various embodiments, the computer vision system 74 synthesizes and processes sensor data and predicts the presence, location, classification, and / or path of environmental features of objects and vehicles 10. In various embodiments, the computer vision system 74 may combine information from multiple sensors (including but not limited to cameras, LiDAR, radar, and / or any number of other types of sensors).

[0058] The positioning system 76 processes sensor data and other data to determine the position of the vehicle 10 relative to its environment (e.g., local position relative to a map, precise position relative to a road lane, vehicle heading, speed, etc.). The guidance system 78 processes sensor data and other data to determine the path that the vehicle 10 should follow. The vehicle control system 80 generates control signals for controlling the vehicle 10 based on the determined path.

[0059] In various embodiments, the controller 34 implements machine learning techniques to assist the functions of the controller 34, such as obstacle mitigation, route traversal, mapping, sensor integration, ground condition determination, feature detection, and object classification as discussed herein.

[0060] As briefly described above, Figure 1 The trajectory tracking system 100 calculates reference values ​​for tracking the trajectory of the vehicle 10. All or part of the trajectory tracking system 100 may be included within, for example, a vehicle control system 80.

[0061] For example, such as for Figure 3For more details, please refer to [link / reference]. Figure 1 and Figure 2 The trajectory tracking system 100 includes a model database 102, a yaw rate determination module 104, a relative heading determination module 106, and a trajectory control module 108.

[0062] Model database 102 stores models used to calculate reference values. In various embodiments, the model is based on a bicycle model and the following relationship.

[0063] For example, such as Figure 4 As shown, the example bicycle model of vehicle 10 includes:

[0064] and

[0065] Where φ represents the road inclination angle, δ represents the road wheel angle, and V x Indicates the desired speed. Let ψ represent the desired trajectory, ψ represent the yaw rate reference, and β represent the sideslip angle β = V. y / V x m represents the mass of the autonomous vehicle, g represents gravity, and C represents the mass of the autonomous vehicle. r This indicates the lateral stiffness of the rear tire, where L represents the wheelbase. r I represents the distance between the rear axle and the vehicle's center of gravity (cg). zz Let l represent the moment of inertia about the vertical axis, and l f This represents the distance between the vehicle's front axle and its center of gravity. Considering the example bicycle model and the relationship between the sideslip angle β and the trajectory angle χ and the vehicle's heading angle ψ (χ = β + ψ), and applying motion constraints that cause the vehicle's trajectory to change relative to the target curvature κ... d Consistency, that is The resulting yaw rate model is as follows:

[0066]

[0067] Under suitable initial conditions, the yaw rate model is an independent differential equation with solutions... A yaw rate reference is provided along the target trajectory. Therefore, the yaw rate reference can be obtained by numerical integration of the yaw rate model of equation (1).

[0068] Under the conditions of the above yaw rate model, it can be seen from the relationship The relative heading model is derived from this, namely:

[0069]

[0070] Similarly, a relative heading reference can be obtained by numerical integration of the relative heading model of equation (2). In various embodiments, the instructions for executing models (1) and (2) are stored in the model database 102 as yaw rate model data 110 and relative heading model data 112.

[0071] In various embodiments, the yaw rate determination module 104 receives desired velocity data 114, trajectory curvature data 116, and road tilt data 118 as input. The yaw rate determination module 104 calculates a yaw rate reference and yaw acceleration based on the numerical integration of the received data 114-118 and yaw rate model data 110 from the model database 102. The yaw rate determination module 104 provides yaw reference data 120 based on the calculated values.

[0072] For example, the yaw rate reference can be calculated along the rolling time domain by numerically solving the yaw rate model (1) for each point. In such an example, the yaw rate reference is reinitialized and recalculated for each instance of the control routine.

[0073] In another example, by integrating point data in real time and solving the yaw rate model independently for each predicted point (1), a yaw rate reference can be calculated along the rolling time domain. It is understood that, because this disclosure is not limited to this example, other methods for numerically integrating the yaw rate reference model can be implemented according to various embodiments.

[0074] In various embodiments, the relative heading determination module 106 receives desired speed data 114, yaw rate reference data 120, and road tilt data 118 as input. The relative heading determination module 106 calculates a relative heading reference based on the numerical integration of the received data 114, 120, 118 and the relative heading model data 112 from the model database 102. The relative heading determination module 106 provides relative heading reference data 122 based on the calculated values.

[0075] For example, the relative heading reference can be calculated along the rolling time domain by numerically solving the relative heading model (2) for each point. In such an example, the relative heading reference is reinitialized and recalculated for each instance of the control routine.

[0076] In another example, by integrating point data in real time and solving the relative heading model for each point independently (2), a relative heading reference can be calculated along the rolling time domain. It is understood that, because this disclosure is not limited to this example, other methods for numerically integrating the yaw rate reference model can be implemented according to various embodiments.

[0077] In various embodiments, the trajectory control module 108 receives yaw reference data 120 and relative heading reference data 122 as inputs. Based on the received inputs 120 and 122, the trajectory control module 108 tracks the trajectory of the vehicle 10 along a desired curve. For example, when the yaw rate reference and relative heading reference are solved according to a first method, the trajectory control module 108 uses model predictive control along with the yaw rate reference and relative heading reference to generate a control signal 124 to control the trajectory. In another example, when the yaw rate reference and relative heading reference are solved according to a second method, the trajectory control module 108 uses error feedforward or feedforward control along with the yaw rate reference and relative heading reference to generate a control signal 124 to control the trajectory. It is understood that because this disclosure is not limited to this example, other control methods can be implemented according to various embodiments.

[0078] Now for reference Figure 5 and Figure 6 And continue to refer to Figures 1-4 The flowchart illustrates the process according to this disclosure. Figure 1 The trajectory tracking system 100 can execute processes 400 and 500. As can be understood from this disclosure, the order of operations within processes 400 and 500 is not limited to... Figure 5 and Figure 6 The processes are not executed in the order shown, but may be executed in one or more different orders as applicable and in accordance with this disclosure. In various embodiments, processes 400, 500 may be scheduled to run based on one or more predetermined events, and / or may run continuously during the operation of the autonomous vehicle 10.

[0079] In one embodiment, process 400 may begin at 405. Input data, including desired speed data, trajectory curvature data, and road inclination data, is received at 410. For each point (k0, k1, k2, ... kn) along the trajectory curvature at 420, a yaw rate reference is calculated at 430 via numerical integration of the yaw rate model, and a relative heading reference is calculated at 440 via numerical integration of the relative heading model. Once the reference values ​​have been calculated for each point at 420, they are used at 450, for example, by model predictive control, to track the vehicle's trajectory. Process 400 may then terminate at 460.

[0080] In another embodiment, process 500 may begin at 505. At 510, input data including desired speed data, trajectory curvature data, and road tilt data is received. At 520, the next point along the trajectory curvature is selected, and a yaw rate reference for that point is calculated in real time via numerical integration. At 530, a relative heading reference for that point is calculated in real time via numerical integration. At 540, the reference values ​​are used to track the vehicle's trajectory using, for example, error feedback control and / or feedforward control. Thereafter, process 500 may end at 550.

[0081] Although at least one exemplary embodiment has been presented in the foregoing detailed description, it should be understood that numerous variations exist. It should also be understood that the exemplary embodiments or multiple exemplary embodiments are merely examples and are not intended to limit the scope, applicability, or configuration of this disclosure in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient roadmap for implementing the exemplary embodiments or multiple exemplary embodiments. It should be understood that various changes can be made to the function and arrangement of the elements without departing from the scope of this disclosure as set forth in the appended claims and their legal equivalents.

Claims

1. A method for motion control in a vehicle, the method comprising: The processor receives target trajectory data associated with the upcoming trajectory of the autonomous vehicle; The processor determines the yaw rate reference and relative heading reference associated with the upcoming target trajectory based on the numerical integration of the target trajectory data; as well as The processor controls the trajectory of the autonomous vehicle based on the yaw rate reference and the relative heading reference.

2. The method of claim 1, wherein, The target trajectory data includes the desired vehicle speed, trajectory curvature, and road inclination angle.

3. The method of claim 1, wherein, The numerical integration of the yaw rate reference is based on: Among them, V x Indicates the desired speed. The desired trajectory is represented by φ, the road inclination angle by φ, the yaw rate reference by ψ, the mass of the autonomous vehicle by m, and the g force by g. r This indicates the lateral stiffness on the rear tires, where L represents the vehicle wheelbase. r Indicates the distance between the rear axle and the center of gravity, I zz Let l represent the vehicle's moment of inertia relative to the vertical axis, and l f This indicates the distance between the front axle and the center of gravity.

4. The method of claim 1, wherein, The numerical integration relative to the heading reference is based on: Where β represents the relative heading, V x Indicates the desired speed. The desired trajectory is represented by φ, the road inclination angle by φ, the yaw rate reference by ψ, the mass of the autonomous vehicle by m, and the g force by g. r This indicates the lateral stiffness on the rear tires, where L represents the vehicle wheelbase. r Indicates the distance between the rear axle and the center of gravity, I zz Let l represent the vehicle's moment of inertia relative to the vertical axis, and l f This indicates the distance between the front axle and the center of gravity.

5. A non-transitory computer-readable medium encoded with programming instructions, the programming instructions being configurable to cause a controller in a vehicle to perform a motion control method, the method comprising: Receive target trajectory data associated with the upcoming trajectory of the autonomous vehicle; Based on the numerical integration of the target trajectory data, a yaw rate reference and a relative heading reference associated with the upcoming target trajectory are determined; as well as The trajectory of the autonomous vehicle is controlled based on the yaw rate reference and the relative heading reference.

6. The non-transitory computer-readable medium of claim 5, wherein, The target trajectory data includes the road inclination angle, the desired vehicle speed, and the trajectory curvature.

7. The non-transitory computer-readable medium of claim 5, wherein, The numerical integration is based on a bicycle model.

8. The non-transitory computer-readable medium of claim 5, wherein, The numerical integration of the yaw rate reference is based on: Furthermore, the numerical integration relative to the heading reference is based on: where V x denotes the desired speed, denotes the desired trajectory, φ denotes the road inclination angle, ψ denotes the said yaw rate reference, m denotes the mass of the autonomous vehicle, g denotes the gravity, C r denotes the cornering stiffness on the rear tires, L denotes the vehicle wheelbase, l r denotes the distance between the rear axle and the center of gravity, I zz denotes the vehicle moment of inertia with respect to the vertical axis, and l f denotes the distance between the front axle and the center of gravity.

9. The non-transitory computer-readable medium of claim 8, wherein, The numerical integration is performed based on the detected rolling time domain, wherein the initial conditions of the numerical integration are reinitialized at the beginning of the time domain and at each cycle time of the motion control.

10. The non-transitory computer-readable medium of claim 8, wherein, The numerical integration is performed independently of each other along the rolling time domain, wherein the initial conditions of the numerical integration are reinitialized at the start of the motion control.

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