Path tracking control method, device and electronic equipment of autonomous vehicle

Abstract

The application discloses a path tracking control method and device of an automatic driving vehicle and electronic equipment, and the method comprises the following steps: constructing an ideal tracking error dynamics model; constructing an improved tracking error dynamics model according to the ideal tracking error dynamics model and uncertainty information of vehicle dynamics and state measurement noise; constructing a path tracking controller of the automatic driving vehicle according to the improved tracking error dynamics model; determining lateral tracking control information of the automatic driving vehicle according to the path tracking controller of the automatic driving vehicle, and performing lateral tracking control of the automatic driving vehicle according to the lateral control information. The application comprehensively considers the influence of uncertainty of vehicle dynamics and state measurement noise on lateral tracking control of the automatic driving vehicle, designs a tracking error dynamics model considering the uncertainty information of vehicle dynamics and the state measurement noise, and improves the precision and feasibility of path tracking control of the automatic driving vehicle.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a path tracking control method, device and electronic device for autonomous vehicles. Background Technology

[0002] Control is an indispensable module for achieving autonomous driving. In existing methods, the control module is usually decoupled into longitudinal and lateral path tracking control, which ensures that the vehicle travels along a predetermined route.

[0003] Uncertainties in the lateral dynamics system affect the path tracking control performance. These uncertainties arise from parameter perturbations such as tire lateral stiffness, as well as external disturbances such as crosswinds. For model-based control, uncertainties also stem from model simplification. Existing research has employed robust model predictive control and fuzzy optimal robust control to suppress these uncertainties. Furthermore, both model-based and model-free path tracking control methods rely on some current vehicle state values, such as lateral position and yaw rate. The uncertainty of these state measurements, i.e., state measurement noise, significantly impacts lateral path tracking performance. Additionally, the front wheel steering angle input calculated by the lateral controller must conform to the actual physical constraints of the vehicle system's steering angle to avoid steering angle saturation.

[0004] Some path tracking control studies have used observers or filters to suppress state measurement noise, but these studies have failed to simultaneously consider the effects of vehicle dynamics uncertainties and state measurement noise. Summary of the Invention

[0005] This application provides a path tracking control method, device, and electronic device for autonomous vehicles to improve the accuracy and feasibility of path tracking control for autonomous vehicles.

[0006] The embodiments of this application adopt the following technical solutions:

[0007] In a first aspect, embodiments of this application provide a path tracking control method for an autonomous vehicle, wherein the method includes:

[0008] Construct an ideal tracking error dynamics model;

[0009] Based on the ideal tracking error dynamics model, the uncertainty information of vehicle dynamics, and the state measurement noise, an improved tracking error dynamics model is constructed.

[0010] A path tracking controller for autonomous vehicles is constructed based on the improved tracking error dynamics model.

[0011] The lateral tracking control information of the autonomous vehicle is determined according to the path tracking controller of the autonomous vehicle, and the lateral tracking control of the autonomous vehicle is performed according to the lateral control information.

[0012] Optionally, constructing the ideal tracking error dynamics model includes:

[0013] Acquire state measurement parameters of autonomous vehicles;

[0014] The ideal tracking error dynamic model is constructed based on the state measurement parameters and the vehicle dynamics relationship.

[0015] Optionally, constructing the improved tracking error dynamics model based on the ideal tracking error dynamics model, the uncertainty information of vehicle dynamics, and state measurement noise includes:

[0016] The uncertainty information of vehicle dynamics is determined, and a tracking error dynamic model containing uncertainty information is constructed based on the ideal tracking error dynamic model and the uncertainty information of vehicle dynamics.

[0017] The state measurement noise is determined, and the improved tracking error dynamics model is constructed based on the tracking error dynamics model containing uncertainty information and the state measurement noise.

[0018] Optionally, constructing a tracking error dynamics model incorporating uncertainty information based on the ideal tracking error dynamics model and the uncertainty information of the vehicle dynamics includes:

[0019] Determine the tire lateral stiffness, system modeling error, and external disturbance error of the autonomous vehicle;

[0020] Based on the ideal tracking error dynamics model and the tire lateral stiffness, system modeling error, and external disturbance error of the autonomous vehicle, the tracking error dynamics model containing uncertainty information is constructed.

[0021] Optionally, the lateral tracking control information includes the front wheel steering angle, and the construction of the path tracking controller for the autonomous vehicle based on the improved tracking error dynamics model includes:

[0022] Based on the improved tracking error dynamics model, a control quantity compensation term containing uncertainty information and state measurement noise is determined;

[0023] Based on the control quantity compensation term containing uncertainty information and state measurement noise, the correspondence between the front wheel steering angle and the state measurement parameters is constructed, serving as the path tracking controller for the autonomous vehicle.

[0024] Optionally, the path tracking controller includes a control quantity compensation term containing uncertainty information and state measurement noise, and determining the lateral tracking control information of the autonomous vehicle based on the path tracking controller of the autonomous vehicle includes:

[0025] The adaptive estimate of the uncertainty information is determined based on the adaptive estimation algorithm of the uncertainty information of vehicle dynamics;

[0026] The compensation control quantity for the uncertainty information and the state measurement noise is determined based on the adaptive estimate of the uncertainty information and the compensation term for the control quantity containing the uncertainty information and the state measurement noise.

[0027] The lateral tracking control information of the autonomous vehicle is determined based on the uncertainty information and the compensation control amount for state measurement noise.

[0028] Optionally, the improved tracking error dynamics model includes state measurement parameters containing state measurement noise, the lateral tracking control information includes the front wheel steering angle, and determining the lateral tracking control information of the autonomous vehicle based on the path tracking controller of the autonomous vehicle includes:

[0029] Determine the front wheel steering angle constraints for autonomous vehicles;

[0030] The compensation correction term is determined based on the front wheel steering angle constraint of the autonomous vehicle.

[0031] The state measurement parameters containing state measurement noise are corrected using the compensation correction term to obtain the corrected state measurement parameters;

[0032] The path tracking controller is updated based on the corrected state measurement parameters, and the lateral tracking control information of the autonomous vehicle is determined based on the updated path tracking controller.

[0033] Secondly, embodiments of this application also provide a path tracking control device for an autonomous vehicle, wherein the device includes:

[0034] The first building unit is used to construct the ideal tracking error dynamics model;

[0035] The second building unit is used to build an improved tracking error dynamic model based on the ideal tracking error dynamic model, the uncertainty information of vehicle dynamics, and state measurement noise.

[0036] The third building unit is used to build a path tracking controller for autonomous vehicles based on the improved tracking error dynamics model.

[0037] The determining unit is configured to determine the lateral tracking control information of the autonomous vehicle based on the path tracking controller of the autonomous vehicle, and to perform lateral tracking control of the autonomous vehicle based on the lateral control information.

[0038] Thirdly, embodiments of this application also provide an electronic device, including:

[0039] Processor; and

[0040] A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform any of the methods described above.

[0041] Fourthly, embodiments of this application also provide a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform any of the methods described above.

[0042] The at least one technical solution adopted in the embodiments of this application can achieve the following beneficial effects: The path tracking control method for autonomous vehicles in the embodiments of this application first constructs an ideal tracking error dynamic model; then, based on the ideal tracking error dynamic model and the uncertainty information of vehicle dynamics and state measurement noise, an improved tracking error dynamic model is constructed; subsequently, a path tracking controller for autonomous vehicles is constructed based on the improved tracking error dynamic model; finally, the lateral tracking control information of autonomous vehicles is determined based on the path tracking controller, and lateral tracking control of autonomous vehicles is performed based on the lateral control information. The path tracking control method for autonomous vehicles in the embodiments of this application comprehensively considers the influence of vehicle dynamics uncertainty and state measurement noise on the lateral tracking control of autonomous vehicles, and designs a tracking error dynamic model that simultaneously considers the uncertainty information of vehicle dynamics and state measurement noise, thereby improving the accuracy and feasibility of path tracking control for autonomous vehicles. Attached Figure Description

[0043] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0044] Figure 1 This is a flowchart illustrating a path tracking control method for an autonomous vehicle according to an embodiment of this application.

[0045] Figure 2 This is a schematic diagram of path tracking for an autonomous vehicle according to an embodiment of this application;

[0046] Figure 3 This is a schematic diagram of a path tracking control process for an autonomous vehicle according to an embodiment of this application;

[0047] Figure 4 This is a schematic diagram of the structure of a path tracking device for an autonomous vehicle according to an embodiment of this application;

[0048] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0050] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0051] This application provides a path tracking control method for autonomous vehicles, such as... Figure 1 The diagram shows a flowchart of a path tracking control method for an autonomous vehicle according to an embodiment of this application. The method includes at least the following steps S110 to S140:

[0052] Step S110: Construct an ideal tracking error dynamic model.

[0053] When implementing path tracking control for autonomous vehicles, the autonomous vehicle activates the path tracking function, and its desired path is a smooth curve with known coordinates. This application embodiment requires first constructing an ideal path tracking error dynamic model, which serves as the basis for subsequent improvements. The ideal tracking error dynamic model refers to a tracking error dynamic model under ideal conditions, without considering system uncertainties and state measurement noise. Essentially, it is a state-space model constructed based on tracking error variables. How to specifically construct the ideal tracking error dynamic model can be flexibly determined by those skilled in the art based on actual needs and existing technology; no specific limitations are made here.

[0054] Step S120: Based on the ideal tracking error dynamics model, the uncertainty information of vehicle dynamics, and the state measurement noise, an improved tracking error dynamics model is constructed.

[0055] After constructing the ideal tracking error dynamic model, the embodiments of this application further consider the impact of vehicle dynamics uncertainty information and state measurement noise on the path tracking control of autonomous vehicles. Vehicle dynamics uncertainty information may include, for example, tire lateral stiffness, external disturbances and system modeling errors, etc. State measurement noise is the measurement error in the state measurement value of autonomous vehicles.

[0056] Based on the ideal tracking error dynamics model, an improved tracking error dynamics model can be constructed by comprehensively considering the uncertainty information of vehicle dynamics and state measurement noise. It should be noted that, since there are linkage changes and mutual influences between the uncertainty information of vehicle dynamics and state measurement noise, those skilled in the art cannot directly combine the existing research schemes that suppress uncertainty interference through robust model predictive control, fuzzy optimal robust control, etc., and the schemes that suppress state measurement noise through observers or filters to obtain a solution that can simultaneously solve the problem of uncertainty information and state measurement noise interference. That is, the improved tracking error dynamics model constructed in the embodiments of this application is a redesigned tracking error dynamics model compared with the existing schemes.

[0057] Step S130: Construct a path tracking controller for the autonomous vehicle based on the improved tracking error dynamics model.

[0058] Based on the improved tracking error dynamics model constructed in the aforementioned steps, an adaptive and robust path tracking controller can be further constructed. The main purpose of constructing the path tracking controller here is to determine the correspondence between the lateral tracking control information and the state measurement parameters of the autonomous vehicle under the comprehensive consideration of the uncertainty information of vehicle dynamics and state measurement noise. Thus, the lateral control quantity to be taken under different state measurement values ​​can be determined based on the correspondence.

[0059] Step S140: Determine the lateral tracking control information of the autonomous vehicle according to the path tracking controller of the autonomous vehicle, and perform lateral tracking control of the autonomous vehicle according to the lateral control information.

[0060] Based on the established path tracking controller for the autonomous vehicle and the current state measurement parameters, lateral tracking control information that comprehensively considers uncertainty information and state measurement noise can be determined. This lateral tracking control information can then be sent to the control system, enabling the autonomous vehicle to achieve lateral tracking control. The lateral tracking control information primarily includes the front wheel steering angle, which can be calculated and sent to the steering mechanism to facilitate steering control.

[0061] The path tracking control method for autonomous vehicles in this application comprehensively considers the uncertainty of vehicle dynamics and the impact of state measurement noise on the lateral tracking control of autonomous vehicles. It designs a tracking error dynamic model that simultaneously considers the uncertainty information of vehicle dynamics and state measurement noise, thereby improving the accuracy and feasibility of path tracking control for autonomous vehicles.

[0062] In some embodiments of this application, constructing the ideal tracking error dynamics model includes: obtaining state measurement parameters of the autonomous vehicle; and constructing the ideal tracking error dynamics model based on the state measurement parameters and the vehicle dynamics relationship.

[0063] In this application embodiment, the ideal tracking error dynamics model can be constructed in the following way:

[0064] 1) Select parameters that characterize tracking performance as state measurement parameters, which can be expressed in the following form:

[0065]

[0066] Where x is the system state vector, e y This is the lateral position error. This represents the heading angle error.

[0067] 2) such as Figure 2 As shown, a path tracking diagram of an autonomous vehicle according to an embodiment of this application is provided, based on... Figure 2 The following ideal tracking error dynamics model is established:

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] Among them, v x Let v be the longitudinal velocity. y Let c be the lateral velocity. R Let be the radius of curvature of the path. For the desired heading angle, This refers to the vehicle's yaw angle.

[0074] 3) Based on step 2), and further considering vehicle dynamics, the following ideal tracking dynamics model is established:

[0075]

[0076] Where, σ1=2(C f +C r ), σ2=-2(l f C f -l r C r ), m is the mass of the car, C f For the front wheel lateral stiffness, C r For the rear wheel lateral stiffness, l f l is the distance from the car's center of gravity to the front axle. r I is the distance from the car's center of gravity to the rear axle. z Let δ be the moment of inertia of the yaw rotation. f This is the front wheel steering angle.

[0077] If we denote the three coefficient matrices on the right side of the equal sign in equation (7) above as A, B, and D from left to right, then the above ideal tracking error dynamic model can be simplified as follows:

[0078]

[0079] Equation (8) above represents the correspondence between the front wheel steering angle and the state measurement parameters without considering the uncertainties of vehicle dynamics and the noise of state measurement.

[0080] In some embodiments of this application, constructing an improved tracking error dynamic model based on the ideal tracking error dynamic model, the uncertainty information of vehicle dynamics, and state measurement noise includes: determining the uncertainty information of vehicle dynamics, and constructing a tracking error dynamic model containing the uncertainty information based on the ideal tracking error dynamic model and the uncertainty information of vehicle dynamics; determining the state measurement noise, and constructing the improved tracking error dynamic model based on the tracking error dynamic model containing the uncertainty information and the state measurement noise.

[0081] In this embodiment of the application, when constructing an improved tracking error dynamic model based on an ideal tracking error dynamic model, vehicle dynamics uncertainties, and state measurement noise, the uncertainties in vehicle dynamics can be determined first. These uncertainties mainly include tire lateral stiffness, external disturbances, and system modeling errors. Introducing these uncertainties into the ideal tracking error dynamic model yields a tracking error dynamic model that includes these uncertainties. Based on this, state measurement noise is determined. State measurement noise can be considered a bounded variable, representing the measurement noise present in the state measurement parameters. Therefore, state measurement noise can be further introduced into the tracking error dynamic model that already includes uncertainties, resulting in a tracking error dynamic model that simultaneously includes both uncertainties and state measurement noise.

[0082] In some embodiments of this application, constructing a tracking error dynamic model containing uncertainty information based on the ideal tracking error dynamic model and the uncertainty information of the vehicle dynamics includes: determining the tire side stiffness, system modeling error, and external disturbance error of the autonomous vehicle; and constructing the tracking error dynamic model containing uncertainty information based on the ideal tracking error dynamic model and the tire side stiffness, system modeling error, and external disturbance error of the autonomous vehicle.

[0083] The uncertainty information of vehicle dynamics in this application embodiment mainly includes the tire lateral stiffness of the autonomous vehicle, system modeling error, and external disturbance error. When constructing a tracking error dynamic model that includes uncertainty information, it can be implemented in the following way:

[0084] The tire lateral stiffness in the ideal tracking error dynamics model can be expressed in the following form in practice:

[0085]

[0086] in, and These are the nominal values ​​(i.e., factory parameters) of the lateral stiffness of the front and rear wheels, ΔC. f and ΔC r For the uncertain part, the influence of system modeling errors and external disturbances on the system state measurement parameter x can be modeled as an equivalent uncertainty term W. Then, the tracking error dynamic model containing uncertainty information can be expressed as:

[0087]

[0088] Based on the aforementioned formula (7), where, ΔA and ΔB can be expressed as:

[0089]

[0090]

[0091] in,

[0092]

[0093] Δσ1=2(ΔC f +ΔC r ), Δσ2=-2(l f ΔC f -l r ΔC r ),

[0094] Further considering system state measurement noise, the following tracking error dynamic system model, which includes uncertainty information and state measurement noise, is finally obtained:

[0095]

[0096] Where y is the state measurement value, and w is an unknown bounded variable representing the state measurement noise.

[0097] In some embodiments of this application, the lateral tracking control information includes the front wheel steering angle, and the step of constructing the path tracking controller of the autonomous vehicle based on the improved tracking error dynamics model includes: determining a control quantity compensation term containing uncertainty information and state measurement noise based on the improved tracking error dynamics model; and constructing a correspondence between the front wheel steering angle and state measurement parameters based on the control quantity compensation term containing uncertainty information and state measurement noise, as the path tracking controller of the autonomous vehicle.

[0098] Based on the improved tracking error dynamics model obtained from the foregoing embodiments, this application embodiment designs a robust path tracking controller, which can be specifically represented as follows:

[0099]

[0100] According to the stability theory of linear systems, For theoretical parameter terms that do not consider uncertainty information and state measurement noise, P is the equation The solution is R = Q = I. 4×4 , This is a compensation term for control variables that include uncertainty information and state measurement noise. This is an adaptive vector used to estimate the uncertainty boundary. and It is an adaptive variable.

[0101] In some embodiments of this application, the path tracking controller includes a control quantity compensation term containing uncertainty information and state measurement noise. Determining the lateral tracking control information of the autonomous vehicle based on the path tracking controller includes: determining an adaptive estimate of the uncertainty information based on an adaptive estimation algorithm for uncertainty information in vehicle dynamics; determining a compensation control quantity for the uncertainty information and state measurement noise based on the adaptive estimate of the uncertainty information and the control quantity compensation term containing uncertainty information and state measurement noise; and determining the lateral tracking control information of the autonomous vehicle based on the compensation control quantity for the uncertainty information and state measurement noise.

[0102] In this embodiment of the application, when determining the current front wheel steering angle based on the path tracking controller of the autonomous vehicle, an adaptive estimation method for the uncertainty boundary can be designed first, namely, an adaptive vector. It can be represented as:

[0103]

[0104]

[0105] Where L1, L2, and L3 are all 2-by-2 positive definite matrices with constant coefficients, and g(y) = [1 ||y||]. T , Given an initial quantity, t0 is a positive constant coefficient, and t0 is the initial time.

[0106] Based on the above equations (15)-(16), a saturated control quantity for compensating for system uncertainties can be further designed, which can be expressed in the following form:

[0107]

[0108] in, and Adaptive vectors The two components.

[0109] Combining the adaptive law in equation (15) above, the above control quantity can be... When the value is increased, it is estimated that there is a large uncertainty in reality, so the compensation control quantity is adjusted accordingly to improve the control accuracy. In addition, it should be noted that since the state measurement parameter y is substituted into the above equation (15), that is, the state measurement value considering the state measurement noise, the compensation control quantity finally calculated based on the above equation (17) is the control quantity that needs to be compensated after comprehensively considering the uncertainty information and the state measurement noise.

[0110] In some embodiments of this application, the improved tracking error dynamics model includes state measurement parameters containing state measurement noise, the lateral tracking control information includes the front wheel steering angle, and the step of determining the lateral tracking control information of the autonomous vehicle based on the path tracking controller of the autonomous vehicle includes: determining the front wheel steering angle constraint of the autonomous vehicle; determining a compensation correction term based on the front wheel steering angle constraint of the autonomous vehicle; correcting the state measurement parameters containing state measurement noise using the compensation correction term to obtain corrected state measurement parameters; updating the path tracking controller based on the corrected state measurement parameters, and determining the lateral tracking control information of the autonomous vehicle based on the updated path tracking controller.

[0111] Considering that there may be a large initial lateral error in the path tracking control process, the front wheel steering angle calculated by formulas (14)-(17) in the aforementioned embodiment may be greater than the maximum front wheel steering angle of the autonomous vehicle, i.e., the steering angle saturation. This may lead to the state parameters failing to converge for a long time, resulting in overestimation of system uncertainty and deterioration of the tracking effect.

[0112] To avoid this situation, embodiments of this application further introduce a compensation correction term φ for the state measurement parameters. (t) Specifically, it is expressed in the following form:

[0113]

[0114] φ(t)=[φ1(t) φ2(t) φ3(t) φ4(t)] T (19)

[0115]

[0116] φ i (t0)=φ i，0 , i∈{1,2,3,4},(21)

[0117] in, The corrected state measurement parameters are φ0, which is a given initial value, and ω. i，1 and ω i，2 (i∈{1, 2, 3, 4}) are positive constant coefficients. Among them, sat(δ) f Let ) be a saturation function, defined as:

[0118]

[0119] Where, δ M，f >0 and δ m，f<0 represents the maximum and minimum front wheel steering angle constants, respectively. When the front wheel steering angle saturation phenomenon occurs, φ(t) can ensure that the state measurement value will not increase or decrease drastically.

[0120] Finally, with Replace y in formulas (14)-(17) in the aforementioned embodiments to calculate the final front wheel steering angle.

[0121] It should be noted that the correction of the state measurement parameters in this application embodiment can be performed in real time, that is, it is not necessary to separately determine whether the initial lateral error of the autonomous vehicle is greater than a certain error threshold. Of course, the initial lateral error of the autonomous vehicle can also be determined. When the initial lateral error of the autonomous vehicle does not exceed the error threshold, the uncorrected state measurement value y is still used in combination with formulas (14)-(17) to calculate the front wheel steering angle for lateral tracking control. When the initial lateral error of the autonomous vehicle exceeds the error threshold, the corrected state measurement value is used. Calculate the front wheel steering angle for lateral tracking control.

[0122] For ease of understanding of the various embodiments of this application, such as Figure 3 The diagram illustrates a path tracking control process for an autonomous vehicle according to an embodiment of this application. First, the autonomous vehicle initiates its path tracking function. Then, the state measurement parameters of the tracking error dynamics model are determined, and an ideal tracking error dynamics model is constructed by combining vehicle dynamics. Next, based on the ideal tracking error dynamics model, the uncertainty information of vehicle dynamics is further considered to construct a tracking error dynamics model incorporating this uncertainty information. Finally, based on the tracking error dynamics model incorporating this uncertainty information, state measurement noise is further considered to construct a tracking error dynamics model incorporating both uncertainty information and state measurement noise.

[0123] Finally, a path tracking controller for the autonomous vehicle is designed based on a tracking error dynamic model incorporating uncertainty information and state measurement noise. Further, a compensation correction term for the state measurement parameters is introduced, and an adaptive uncertainty estimate in the path tracking controller is calculated. This estimate is then used to calculate the compensation control quantity for uncertainty information and state measurement noise. The front wheel steering angle of the autonomous vehicle is calculated based on the compensation correction term for the state measurement parameters and the compensation control quantity for uncertainty information and state measurement noise. The front wheel steering angle of the autonomous vehicle is then sent to the steering mechanism for steering control. The above steps are repeated until the path tracking function ends.

[0124] The path tracking control method for autonomous vehicles in this application achieves at least the following technical effects:

[0125] 1) This application establishes a tracking error dynamic model that comprehensively considers dynamic uncertainties and state measurement noise. Compared with existing models, this model is closer to the actual vehicle dynamic system.

[0126] 2) This application designs an adaptive estimation and correction method for the uncertainty boundary of the lateral tracking error dynamic system based on the actual state parameter measurement values. Combined with the upper and lower limits of the steering angle, it ensures the rationality of the uncertainty boundary estimation and makes the observation results more consistent with reality.

[0127] 3) Based on the uncertainty boundary estimated in 2), this application designs an analytical form of robust path tracking controller to achieve path tracking control that simultaneously suppresses dynamic uncertainty and state measurement noise. While compensating for the impact of system uncertainty on tracking performance, this controller prevents steering angle saturation by suppressing overestimation of uncertainty, which is more conducive to engineering deployment.

[0128] This application also provides a path tracking control device 400 for an autonomous vehicle, such as... Figure 4 The diagram shows a schematic representation of a path tracking device for an autonomous vehicle according to an embodiment of this application. The device 400 includes: a first building unit 410, a second building unit 420, a third building unit 430, and a determining unit 440, wherein:

[0129] The first building unit 410 is used to build an ideal tracking error dynamics model;

[0130] The second construction unit 420 is used to construct an improved tracking error dynamic model based on the ideal tracking error dynamic model, the uncertainty information of vehicle dynamics, and state measurement noise.

[0131] The third building unit 430 is used to build a path tracking controller for an autonomous vehicle based on the improved tracking error dynamics model.

[0132] The determining unit 440 is used to determine the lateral tracking control information of the autonomous vehicle based on the path tracking controller of the autonomous vehicle, and to perform lateral tracking control of the autonomous vehicle based on the lateral control information.

[0133] In some embodiments of this application, the first construction unit 410 is specifically used to: acquire state measurement parameters of the autonomous vehicle; and construct the ideal tracking error dynamic model based on the state measurement parameters and the vehicle dynamics relationship.

[0134] In some embodiments of this application, the second construction unit 420 is specifically used to: determine the uncertainty information of vehicle dynamics, and construct a tracking error dynamic model containing uncertainty information based on the ideal tracking error dynamic model and the uncertainty information of vehicle dynamics; determine the state measurement noise, and construct the improved tracking error dynamic model based on the tracking error dynamic model containing uncertainty information and the state measurement noise.

[0135] In some embodiments of this application, the second construction unit 420 is specifically used to: determine the tire side stiffness, system modeling error, and external disturbance error of the autonomous vehicle; and construct the tracking error dynamic model containing uncertainty information based on the ideal tracking error dynamic model and the tire side stiffness, system modeling error, and external disturbance error of the autonomous vehicle.

[0136] In some embodiments of this application, the lateral tracking control information includes the front wheel steering angle, and the third construction unit 430 is specifically used to: determine a control quantity compensation term containing uncertainty information and state measurement noise based on the improved tracking error dynamics model; and construct the correspondence between the front wheel steering angle and state measurement parameters based on the control quantity compensation term containing uncertainty information and state measurement noise, as the path tracking controller of the autonomous vehicle.

[0137] In some embodiments of this application, the path tracking controller includes a control quantity compensation term containing uncertainty information and state measurement noise. The determining unit 440 is specifically configured to: determine an adaptive estimate of uncertainty information based on an adaptive estimation algorithm for uncertainty information of vehicle dynamics; determine a compensation control quantity for the uncertainty information and state measurement noise based on the adaptive estimate of uncertainty information and the control quantity compensation term containing uncertainty information and state measurement noise; and determine lateral tracking control information of the autonomous vehicle based on the compensation control quantity for uncertainty information and state measurement noise.

[0138] In some embodiments of this application, the improved tracking error dynamics model includes state measurement parameters containing state measurement noise, and the lateral tracking control information includes the front wheel steering angle. The determining unit 440 is specifically used to: determine the front wheel steering angle constraint of the autonomous vehicle; determine a compensation correction term based on the front wheel steering angle constraint of the autonomous vehicle; correct the state measurement parameters containing state measurement noise using the compensation correction term to obtain corrected state measurement parameters; update the path tracking controller based on the corrected state measurement parameters, and determine the lateral tracking control information of the autonomous vehicle based on the updated path tracking controller.

[0139] It is understood that the path tracking control device for the aforementioned autonomous vehicle can implement each step of the path tracking control method for the autonomous vehicle provided in the foregoing embodiments. The relevant explanations regarding the path tracking control method for the autonomous vehicle are applicable to the path tracking control device for the autonomous vehicle, and will not be repeated here.

[0140] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Please refer to it. Figure 5 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0141] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0142] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0143] The processor reads the corresponding computer program from non-volatile memory into main memory and then executes it, forming the path tracking control device for the autonomous vehicle at the logical level. The processor executes the program stored in memory and specifically performs the following operations:

[0144] Construct an ideal tracking error dynamics model;

[0145] Based on the ideal tracking error dynamics model, the uncertainty information of vehicle dynamics, and the state measurement noise, an improved tracking error dynamics model is constructed.

[0146] A path tracking controller for autonomous vehicles is constructed based on the improved tracking error dynamics model.

[0147] The lateral tracking control information of the autonomous vehicle is determined according to the path tracking controller of the autonomous vehicle, and the lateral tracking control of the autonomous vehicle is performed according to the lateral control information.

[0148] The above is as stated in this application. Figure 1 The method executed by the path tracking control device for an autonomous vehicle disclosed in the illustrated embodiments can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0149] The electronic device can also perform Figure 1 The method for executing the path tracking control device of an autonomous vehicle, and the implementation of the path tracking control device of an autonomous vehicle in... Figure 1 The functions of the embodiments shown are not described again in this application.

[0150] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform... Figure 1 The method executed by the path tracking control device of the autonomous vehicle in the illustrated embodiment is specifically used to perform:

[0151] Construct an ideal tracking error dynamics model;

[0152] Based on the ideal tracking error dynamics model, the uncertainty information of vehicle dynamics, and the state measurement noise, an improved tracking error dynamics model is constructed.

[0153] A path tracking controller for autonomous vehicles is constructed based on the improved tracking error dynamics model.

[0154] The lateral tracking control information of the autonomous vehicle is determined according to the path tracking controller of the autonomous vehicle, and the lateral tracking control of the autonomous vehicle is performed according to the lateral control information.

[0155] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0156] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0157] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0158] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0159] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0160] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0161] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0162] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0163] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0164] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A path tracking control method for an autonomous vehicle, wherein, The method includes: Construct an ideal tracking error dynamics model; Based on the ideal tracking error dynamics model, the uncertainty information of vehicle dynamics, and the state measurement noise, an improved tracking error dynamics model is constructed. A path tracking controller for autonomous vehicles is constructed based on the improved tracking error dynamics model. The lateral tracking control information of the autonomous vehicle is determined according to the path tracking controller of the autonomous vehicle, and the lateral tracking control of the autonomous vehicle is performed according to the lateral tracking control information. The uncertainty information includes tire lateral stiffness, external disturbance error, and system modeling error; The improved tracking error dynamics model includes state measurement parameters containing state measurement noise, the lateral tracking control information includes the front wheel steering angle, and the determination of the lateral tracking control information of the autonomous vehicle based on the path tracking controller of the autonomous vehicle includes: Determine the front wheel steering angle constraints for autonomous vehicles; The compensation correction term is determined based on the front wheel steering angle constraint of the autonomous vehicle. The state measurement parameters containing state measurement noise are corrected using the compensation correction term to obtain the corrected state measurement parameters; The path tracking controller is updated based on the corrected state measurement parameters, and the lateral tracking control information of the autonomous vehicle is determined based on the updated path tracking controller.

2. The method as described in claim 1, wherein, The construction of the ideal tracking error dynamics model includes: Acquire state measurement parameters of autonomous vehicles; The ideal tracking error dynamic model is constructed based on the state measurement parameters and the vehicle dynamics relationship.

3. The method as described in claim 1, wherein, The step of constructing the improved tracking error dynamic model based on the ideal tracking error dynamic model, the uncertainty information of vehicle dynamics, and the state measurement noise includes: The uncertainty information of vehicle dynamics is determined, and a tracking error dynamic model containing uncertainty information is constructed based on the ideal tracking error dynamic model and the uncertainty information of vehicle dynamics. The state measurement noise is determined, and the improved tracking error dynamics model is constructed based on the tracking error dynamics model containing uncertainty information and the state measurement noise.

4. The method as described in claim 3, wherein, The step of constructing a tracking error dynamic model containing uncertainty information based on the ideal tracking error dynamic model and the uncertainty information of vehicle dynamics includes: Determine the tire lateral stiffness, system modeling error, and external disturbance error of the autonomous vehicle; Based on the ideal tracking error dynamics model and the tire lateral stiffness, system modeling error, and external disturbance error of the autonomous vehicle, the tracking error dynamics model containing uncertainty information is constructed.

5. The method as described in claim 1, wherein, The lateral tracking control information includes the front wheel steering angle, and the construction of the path tracking controller for the autonomous vehicle based on the improved tracking error dynamics model includes: Based on the improved tracking error dynamics model, a control quantity compensation term containing uncertainty information and state measurement noise is determined; Based on the control quantity compensation term containing uncertainty information and state measurement noise, the correspondence between the front wheel steering angle and the state measurement parameters is constructed, serving as the path tracking controller for the autonomous vehicle.

6. The method of claim 1, wherein, The path tracking controller includes a control quantity compensation term containing uncertainty information and state measurement noise. Determining the lateral tracking control information of the autonomous vehicle based on the path tracking controller of the autonomous vehicle includes: The adaptive estimate of the uncertainty information is determined based on the adaptive estimation algorithm of the uncertainty information of vehicle dynamics; The compensation control quantity for the uncertainty information and the state measurement noise is determined based on the adaptive estimate of the uncertainty information and the compensation term for the control quantity containing the uncertainty information and the state measurement noise. The lateral tracking control information of the autonomous vehicle is determined based on the uncertainty information and the compensation control amount for state measurement noise.

7. A path tracking control device for an autonomous vehicle, wherein, The device includes: The first building unit is used to construct the ideal tracking error dynamics model; The second building unit is used to build an improved tracking error dynamic model based on the ideal tracking error dynamic model, the uncertainty information of vehicle dynamics, and state measurement noise. The third building unit is used to build a path tracking controller for autonomous vehicles based on the improved tracking error dynamics model. The determining unit is configured to determine the lateral tracking control information of the autonomous vehicle based on the path tracking controller of the autonomous vehicle, and to perform lateral tracking control of the autonomous vehicle based on the lateral tracking control information. The uncertainty information includes tire lateral stiffness, external disturbance error, and system modeling error; The improved tracking error dynamics model includes state measurement parameters containing state measurement noise; the lateral tracking control information includes the front wheel steering angle; and the determining unit is specifically used for: Determine the front wheel steering angle constraints for autonomous vehicles; The compensation correction term is determined based on the front wheel steering angle constraint of the autonomous vehicle. The state measurement parameters containing state measurement noise are corrected using the compensation correction term to obtain the corrected state measurement parameters; The path tracking controller is updated based on the corrected state measurement parameters, and the lateral tracking control information of the autonomous vehicle is determined based on the updated path tracking controller.

8. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the method of any one of claims 1 to 6.

9. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the method of any one of claims 1 to 6.

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