CN116755448A - Path tracking control method and device for automatic driving vehicle and electronic equipment - Google Patents
Path tracking control method and device for automatic driving vehicle and electronic equipment Download PDFInfo
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Abstract
The application discloses a path tracking control method and device for an automatic driving vehicle and electronic equipment, wherein 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, 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; and determining transverse tracking control information of the automatic driving vehicle according to the path tracking controller of the automatic driving vehicle, and performing transverse tracking control of the automatic driving vehicle according to the transverse control information. The application comprehensively considers the uncertainty of the vehicle dynamics and the influence of the state measurement noise on the transverse tracking control of the automatic driving vehicle, designs a tracking error dynamics model which simultaneously considers the uncertainty information of the vehicle dynamics and the state measurement noise, and improves the accuracy and feasibility of the path tracking control of the automatic driving vehicle.
Description
Technical Field
The present application relates to the field of autopilot technologies, and in particular, to a method and an apparatus for controlling path tracking of an autopilot vehicle, and an electronic device.
Background
The control is a module which is indispensable for realizing automatic driving, and the control module in the prior method is usually decoupled into longitudinal and transverse path tracking control, and the path tracking control ensures that the vehicle runs along a set route.
The uncertainty of the lateral dynamics system affects the path tracking control effect, both from parametric disturbances such as tire cornering stiffness and from external disturbances such as crosswinds, and for model-based control, from model simplification. The prior researches have been used for suppressing the interference of the uncertainty through schemes such as robust model predictive control, fuzzy optimal robust control and the like. In addition, the path tracking control method based on the model or without the model depends on partial current vehicle state values, such as lateral position, yaw rate and the like, and uncertainty of state measurement values, namely the existence of state measurement noise, has a great influence on the lateral path tracking effect. In addition, the front wheel steering angle input calculated by the transverse controller also needs to conform to the steering angle physical constraint of an actual vehicle system, and the situation of saturated steering angle should be avoided.
Some path-tracking control studies have suppressed state measurement noise by observers or filters, but related studies fail to take into account both the effects of vehicle dynamics uncertainty and state measurement noise.
Disclosure of Invention
The embodiment of the application provides a path tracking control method and device for an automatic driving vehicle and electronic equipment, so as to improve the accuracy and feasibility of the path tracking control of the automatic driving vehicle.
The embodiment of the application adopts the following technical scheme:
in a first aspect, an embodiment of the present application provides a path tracking control method for an autonomous vehicle, where the method includes:
constructing an ideal tracking error dynamics model;
constructing an improved tracking error dynamics model according to the ideal tracking error dynamics model, 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;
and determining transverse tracking control information of the automatic driving vehicle according to the path tracking controller of the automatic driving vehicle, and performing transverse tracking control of the automatic driving vehicle according to the transverse control information.
Optionally, the building the ideal tracking error dynamics model includes:
acquiring state measurement parameters of an automatic driving vehicle;
and constructing the ideal tracking error dynamics model according to the state measurement parameters and the vehicle dynamics relation.
Optionally, the constructing an improved tracking error dynamics model according to the ideal tracking error dynamics model, uncertainty information of vehicle dynamics and state measurement noise includes:
determining uncertainty information of vehicle dynamics, and constructing a tracking error dynamics model containing the uncertainty information according to the ideal tracking error dynamics model and the uncertainty information of the vehicle dynamics;
and determining state measurement noise, and constructing the improved tracking error dynamics model according to the tracking error dynamics model containing uncertainty information and the state measurement noise.
Optionally, the constructing a tracking error dynamics model including uncertainty information according to the ideal tracking error dynamics model and uncertainty information of the vehicle dynamics includes:
determining the tire cornering stiffness, system modeling errors and external disturbance errors of the autonomous vehicle;
And constructing the tracking error dynamics model containing uncertainty information according to the ideal tracking error dynamics model, the tire cornering stiffness of the automatic driving vehicle, the system modeling error and the external interference error.
Optionally, the lateral tracking control information includes a front wheel steering angle, and the constructing a path tracking controller of the autonomous vehicle according to the improved tracking error dynamics model includes:
determining a control quantity compensation term containing uncertainty information and state measurement noise according to the improved tracking error dynamics model;
and constructing the corresponding relation between the steering angle of the front wheels and the state measurement parameter according to the control quantity compensation item containing the uncertainty information and the state measurement noise, and using the corresponding relation as a path tracking controller of the automatic driving vehicle.
Optionally, the path tracking controller includes a control quantity compensation term including uncertainty information and state measurement noise, and the determining the lateral tracking control information of the autonomous vehicle according to the path tracking controller of the autonomous vehicle includes:
determining an adaptive estimation amount of the uncertainty information according to an adaptive estimation algorithm of the uncertainty information of the vehicle dynamics;
Determining a compensation control quantity of the uncertainty information and the state measurement noise according to the adaptive estimator of the uncertainty information and the control quantity compensation term containing the uncertainty information and the state measurement noise;
and determining the transverse tracking control information of the automatic driving vehicle according to the uncertainty information and the compensation control quantity of the state measurement noise.
Optionally, the improved tracking error dynamics model includes a state measurement parameter including a state measurement noise, the lateral tracking control information includes a front wheel steering angle, and determining the lateral tracking control information of the autonomous vehicle according to the path tracking controller of the autonomous vehicle includes:
determining a front wheel steering angle constraint of the autonomous vehicle;
determining a compensation correction term according to the front wheel steering angle constraint of the automatic driving vehicle;
correcting the state measurement parameters containing state measurement noise by using the compensation correction term to obtain corrected state measurement parameters;
and updating the path tracking controller according to the corrected state measurement parameters, and determining transverse tracking control information of the automatic driving vehicle according to the updated path tracking controller.
In a second aspect, an embodiment of the present application further provides a path tracking control device for an autopilot vehicle, where the device includes:
the first construction unit is used for constructing an ideal tracking error dynamics model;
the second construction unit is used for constructing an improved tracking error dynamics model according to the ideal tracking error dynamics model, the uncertainty information of the dynamics of the vehicle and the state measurement noise;
a third construction unit, configured to construct a path tracking controller of the autonomous vehicle according to the improved tracking error dynamics model;
and the determining unit is used for determining transverse tracking control information of the automatic driving vehicle according to the path tracking controller of the automatic driving vehicle and carrying out transverse tracking control of the automatic driving vehicle according to the transverse control information.
In a third aspect, an embodiment of the present application further provides an electronic device, including:
a processor; and
a memory arranged to store computer executable instructions which, when executed, cause the processor to perform any of the methods described hereinbefore.
In a fourth aspect, embodiments of the present application also provide a computer-readable storage medium storing one or more programs, which when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform any of the methods described above.
The above at least one technical scheme adopted by the embodiment of the application can achieve the following beneficial effects: the path tracking control method of the automatic driving vehicle firstly builds an ideal tracking error dynamics model; then, an improved tracking error dynamics model is constructed according to the ideal tracking error dynamics model, uncertainty information of vehicle dynamics and state measurement noise; then constructing a path tracking controller of the automatic driving vehicle according to the improved tracking error dynamics model; and finally, determining transverse tracking control information of the automatic driving vehicle according to the path tracking controller of the automatic driving vehicle, and performing transverse tracking control of the automatic driving vehicle according to the transverse control information. The path tracking control method of the automatic driving vehicle comprehensively considers the uncertainty of the vehicle dynamics and the influence of the state measurement noise on the transverse tracking control of the automatic driving vehicle, designs a tracking error dynamics model which simultaneously considers the uncertainty information of the vehicle dynamics and the state measurement noise, and improves the accuracy and feasibility of the path tracking control of the automatic driving vehicle.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a schematic flow chart of a method for controlling path tracking of an autonomous vehicle according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a path trace of an autonomous vehicle according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a path tracking control flow of an autonomous vehicle according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a path tracking device for an autonomous vehicle according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
The embodiment of the application provides a path tracking control method of an automatic driving vehicle, as shown in fig. 1, and provides a flow chart of the path tracking control method of the automatic driving vehicle in the embodiment of the application, wherein the method at least comprises the following steps S110 to S140:
and step S110, constructing an ideal tracking error dynamics model.
When the path tracking control of the automatic driving vehicle is realized, the automatic driving vehicle starts the path tracking function, and the expected path is a smooth curve with known coordinates. The embodiment of the application needs to construct an ideal path tracking error dynamics model firstly to be used as a basic model for subsequent improvement, wherein the ideal tracking error dynamics model refers to a tracking error dynamics model under an ideal state without considering system uncertainty and state measurement noise, is a state space model constructed based on tracking error variables, and particularly how to construct the ideal tracking error dynamics model, and can be flexibly determined by a person skilled in the art according to actual requirements and in combination with the prior art, and is not particularly limited.
And step S120, constructing an improved tracking error dynamics model according to the ideal tracking error dynamics model, the uncertainty information of the dynamics of the vehicle and the state measurement noise.
After an ideal tracking error dynamics model is constructed, the influence of uncertainty information of vehicle dynamics and state measurement noise on path tracking control of an automatic driving vehicle are further considered, wherein the uncertainty information of the vehicle dynamics can comprise tire cornering stiffness, external interference, system modeling errors and the like, and the state measurement noise is the measurement error of a state measurement value of the automatic driving vehicle.
On the basis of an ideal tracking error dynamics model, the uncertainty information and the state measurement noise of the vehicle dynamics are comprehensively considered, and an improved tracking error dynamics model can be constructed, and it is required to be noted that, because linkage change and interaction exist between the uncertainty information and the state measurement noise of the vehicle dynamics, for the scheme of suppressing uncertainty interference through robust model predictive control, fuzzy optimal robust control and the like in the existing research and the scheme of suppressing state measurement noise through an observer or a filter, a person skilled in the art cannot directly combine the schemes with each other to obtain the scheme capable of simultaneously solving the problem of uncertainty information and state measurement noise interference, namely the improved tracking error dynamics model constructed by the embodiment of the application is a tracking error dynamics model redesigned compared with the existing scheme.
And step S130, constructing a path tracking controller of the automatic driving vehicle according to the improved tracking error dynamics model.
Based on the improved tracking error dynamics model constructed in the previous step, a self-adaptive robust path tracking controller can be further constructed, wherein the path tracking controller is mainly constructed for determining the corresponding relation between the transverse tracking control information and the state measurement parameters of the automatic driving vehicle under the condition of comprehensively considering the uncertainty information and the state measurement noise of the vehicle dynamics, so that the transverse control quantity required to be adopted under different state measurement values can be determined based on the corresponding relation.
Step S140, determining lateral tracking control information of the autonomous vehicle according to the path tracking controller of the autonomous vehicle, and performing lateral tracking control of the autonomous vehicle according to the lateral control information.
According to the constructed path tracking controller of the automatic driving vehicle, the transverse tracking control information comprehensively considering the uncertainty information and the state measurement noise can be determined by combining the current state measurement parameters, and then the transverse tracking control information can be issued to the control system, so that the transverse tracking control of the automatic driving vehicle is realized through the control system. The lateral tracking control information mainly comprises a front wheel steering angle, and the calculated front wheel steering angle can be sent to the steering mechanism, so that the steering mechanism is convenient to steer.
The path tracking control method of the automatic driving vehicle comprehensively considers the uncertainty of the vehicle dynamics and the influence of the state measurement noise on the transverse tracking control of the automatic driving vehicle, designs a tracking error dynamics model which simultaneously considers the uncertainty information of the vehicle dynamics and the state measurement noise, and improves the accuracy and feasibility of the path tracking control of the automatic driving vehicle.
In some embodiments of the application, the constructing the ideal tracking error dynamics model includes: acquiring state measurement parameters of an automatic driving vehicle; and constructing the ideal tracking error dynamics model according to the state measurement parameters and the vehicle dynamics relation.
The embodiment of the application can be realized by the following modes when an ideal tracking error dynamics model is constructed:
1) The parameters characterizing the tracking performance are chosen as state measurement parameters, which can be expressed, for example, in the following form:
where x is a system state vector, e y As a result of the lateral position error,is the heading angle error.
2) As shown in fig. 2, a schematic diagram of path tracking of an autonomous vehicle according to an embodiment of the present application is provided, and the following ideal tracking error dynamics model is established based on fig. 2:
Wherein v is x For longitudinal speed, v y For transverse velocity, c R As a radius of curvature of the path,for the desired heading angle, < >>Is the yaw angle of the vehicle.
3) Based on step 2), further combining with the vehicle dynamics relation, building the following ideal tracking dynamics model:
wherein sigma 1 ＝2(C f +C r )，σ 2 ＝-2(l f C f -l r C r )，m is the mass of the automobile, C f C is the cornering stiffness of the front wheel r For the cornering stiffness of the rear wheels, l f Distance from center of mass to front axle of automobile, l r I is the distance from the center of mass of the automobile to the rear axle z For yaw moment of inertia, delta f Is in front ofWheel steering angle.
The three coefficient matrices on the right side of the medium number in the above formula (7) are sequentially marked as A, B and D from left to right, and the ideal tracking error dynamics model can be simply marked as follows:
the above formula (8) is the correspondence between the steering angle of the front wheel and the state measurement parameter constructed without considering the uncertainty of the vehicle dynamics and the state measurement noise.
In some embodiments of the present application, the constructing an improved tracking error dynamics model according to the ideal tracking error dynamics model, uncertainty information of vehicle dynamics and state measurement noise includes: determining uncertainty information of vehicle dynamics, and constructing a tracking error dynamics model containing the uncertainty information according to the ideal tracking error dynamics model and the uncertainty information of the vehicle dynamics; and determining state measurement noise, and constructing the improved tracking error dynamics model according to the tracking error dynamics model containing uncertainty information and the state measurement noise.
When the improved tracking error dynamics model is constructed according to the ideal tracking error dynamics model, the uncertainty information of the vehicle dynamics and the state measurement noise, the uncertainty information of the vehicle dynamics can be firstly determined, the uncertainty information mainly comprises the tire cornering stiffness, the external interference, the system modeling error and the like, and the uncertainty information is introduced into the ideal tracking error dynamics model to obtain the tracking error dynamics model containing the uncertainty information. On the basis of this, the state measurement noise is determined, which can be regarded as a bounded variable representing the measurement noise present in the state measurement parameter, so that the state measurement noise can be further introduced into the tracking error dynamics model in the tracking error dynamics model containing uncertainty information, resulting in a tracking error dynamics model containing both uncertainty information and state measurement noise.
In some embodiments of the application, said constructing a tracking error dynamics model comprising uncertainty information from said ideal tracking error dynamics model and said uncertainty information of vehicle dynamics comprises: determining the tire cornering stiffness, system modeling errors and external disturbance errors of the autonomous vehicle; and constructing the tracking error dynamics model containing uncertainty information according to the ideal tracking error dynamics model, the tire cornering stiffness of the automatic driving vehicle, the system modeling error and the external interference error.
The uncertainty information of the vehicle dynamics of the embodiment of the application mainly comprises the tire cornering stiffness, the system modeling error and the external interference error of the automatic driving vehicle, and can be realized in the following manner when a tracking error dynamics model comprising the uncertainty information is constructed:
the tire cornering stiffness in an ideal tracking error dynamics model can be expressed in practice as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,and->Nominal values of cornering stiffness (i.e. factory parameters) of the front wheel and the rear wheel respectively, delta C f And DeltaC r For the uncertainty part, the influence of the system modeling error and external disturbance on the system state measurement parameter x can be modeled as an equivalent uncertainty term W, and then the tracking error dynamics model containing uncertainty information can be expressed as:
based on the foregoing formula (7), wherein,Δa, Δb can be expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
Δσ 1 ＝2(ΔC f +ΔC r )，Δσ 2 ＝-2(l f ΔC f -l r ΔC r )，
further considering the system state measurement noise, finally obtaining the following tracking error dynamics system model containing uncertainty information and state measurement noise:
where y is the state measurement value and w is an unknown bounded variable representing the state measurement noise.
In some embodiments of the present application, the lateral tracking control information includes a front wheel steering angle, and the constructing a path tracking controller of an autonomous vehicle according to the improved tracking error dynamics model includes: determining a control quantity compensation term containing uncertainty information and state measurement noise according to the improved tracking error dynamics model; and constructing the corresponding relation between the steering angle of the front wheels and the state measurement parameter according to the control quantity compensation item containing the uncertainty information and the state measurement noise, and using the corresponding relation as a path tracking controller of the automatic driving vehicle.
The embodiment of the application can be used for constructing an improved tracking error dynamics model according to the previous embodiment, designing a form of a robust path tracking controller, and can be specifically expressed as follows:
wherein, according to the linear system stability theory,in order to consider theoretical parameter terms of uncertainty information and state measurement noise, P is the equation +.>R=q=i 4×4 ，/>Control quantity compensation term for containing uncertainty information and state measurement noise->For an adaptive vector for estimating uncertainty boundaries, +.>And->Is an adaptive variable.
In some embodiments of the application, the path tracking controller includes a control quantity compensation term including uncertainty information and state measurement noise, and the determining lateral tracking control information of the autonomous vehicle from the path tracking controller of the autonomous vehicle includes: determining an adaptive estimation amount of the uncertainty information according to an adaptive estimation algorithm of the uncertainty information of the vehicle dynamics; determining a compensation control quantity of the uncertainty information and the state measurement noise according to the adaptive estimator of the uncertainty information and the control quantity compensation term containing the uncertainty information and the state measurement noise; and determining the transverse tracking control information of the automatic driving vehicle according to the uncertainty information and the compensation control quantity of the state measurement noise.
The embodiment of the application can firstly design the self-adaptive estimation method of the uncertainty boundary, namely the self-adaptive vector when determining the current steering angle of the front wheel according to the path tracking controller of the automatic driving vehicleCan be expressed as:
wherein L is 1 、L 2 、L 3 A constant coefficient positive definite matrix of 2 times 2, g (y) = [1 y] T ，For a given initial amount, +.>Is a positive constant coefficient, t 0 Is the initial time.
Further design of the saturation type control amount compensating for the uncertainty of the system based on the above formulas (15) - (16) can be expressed specifically as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,and->Adaptive vectors +.>Is included in the (a) and (b) is included in the (b) component.
The control amount may be in the following range, in combination with the adaptive law in the above formula (15)When the control is increased, the fact that larger uncertainty exists is estimated, so that the compensation control quantity is correspondingly adjusted, and the control precision is improved. Since the state measurement parameter substituted in the above formula (15) is y, that is, the state measurement value of the state measurement noise is taken into consideration, the compensation control amount finally calculated based on the above formula (17) is the control amount to be compensated by comprehensively taking the uncertainty information and the state measurement noise into consideration.
In some embodiments of the application, the improved tracking error dynamics model includes a state measurement parameter including state measurement noise, the lateral tracking control information includes a front wheel steering angle, and the determining the lateral tracking control information of the autonomous vehicle from the path tracking controller of the autonomous vehicle includes: determining a front wheel steering angle constraint of the autonomous vehicle; determining a compensation correction term according to the front wheel steering angle constraint of the automatic driving vehicle; correcting the state measurement parameters containing state measurement noise by using the compensation correction term to obtain corrected state measurement parameters; and updating the path tracking controller according to the corrected state measurement parameters, and determining transverse tracking control information of the automatic driving vehicle according to the updated path tracking controller.
Considering that there may be a case where an initial lateral error is large during the path tracking control, it may be caused that the front wheel steering angle calculated by the formulas (14) - (17) in the foregoing embodiments is larger than the maximum front wheel steering angle of the autonomous vehicle, that is, the steering angle is saturated, and further, the state parameter cannot be converged for a long time, resulting in overestimation of the system uncertainty, and deteriorating the tracking effect.
To avoid this, embodiments of the present application further introduce a compensation correction term phi for the state measurement parameters (t) The method is specifically expressed as follows:
φ(t)＝[φ 1 (t) φ 2 (t) φ 3 (t) φ 4 (t)] T ，(19)
φ i (t 0 )＝φ i，0 ，i∈{1，2，3，4}，(21)
wherein, the liquid crystal display device comprises a liquid crystal display device,for the corrected state measurement parameters phi 0 For a given initial value, ω i，1 And omega i，2 (i.epsilon.1, 2,3, 4) is a positive constant coefficient,/->Wherein sat (delta) f ) As a saturation function, defined as:
wherein delta M，f > 0 and delta m，f < 0 indicates the maximum front wheel steering angle and the minimum front wheel steering angle constant, respectively, and phi (t) can ensure that the state measurement value does not increase or decrease drastically when the front wheel steering angle saturation phenomenon occurs.
Finally, byInstead of y in equations (14) - (17) in the previous embodiments, the final front wheel steering angle is calculated.
It should be noted that, the correction of the state measurement parameter in the embodiment of the present application may 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, and of course, it is also possible to determine the initial lateral error of the autonomous vehicle, and when the initial lateral error of the autonomous vehicle does not exceed the error threshold, calculate the front wheel steering angle by using the uncorrected state measurement value y in combination with formulas (14) - (17) to perform lateral tracking control, and when the initial lateral error of the autonomous vehicle exceeds the error threshold, use the corrected state measurement value And calculating the steering angle of the front wheels to carry out transverse tracking control.
In order to facilitate understanding of the embodiments of the present application, as shown in fig. 3, a schematic diagram of a path tracking control flow of an autonomous vehicle in an embodiment of the present application is provided. First, the autonomous vehicle starts the path tracking function. Then, state measurement parameters of the tracking error dynamics model are determined, and an ideal tracking error dynamics model is built by combining vehicle dynamics. Then, on the basis of an ideal tracking error dynamics model, uncertainty information of vehicle dynamics is further considered, a tracking error dynamics model containing the uncertainty information is constructed, and on the basis of a tracking error dynamics model containing the uncertainty information, state measurement noise is further considered, and a tracking error dynamics model containing the uncertainty information and the state measurement noise is constructed.
And finally, designing a path tracking controller of the automatic driving vehicle according to a tracking error dynamics model containing uncertainty information and state measurement noise, further introducing a compensation correction term of the state measurement parameter on the basis, calculating an uncertainty self-adaptive estimated quantity in the path tracking controller, calculating a compensation control quantity of the uncertainty information and the state measurement noise according to the uncertainty self-adaptive estimated quantity, and calculating a front wheel steering angle of the automatic driving vehicle according to the compensation correction term of the state measurement parameter and the compensation control quantity of the uncertainty information and the state measurement noise. And sending the steering angle of the front wheels of the automatic driving vehicle to a steering mechanism for steering control, and repeatedly executing the steps until the path tracking function is finished.
The path tracking control method of the automatic driving vehicle at least has the following technical effects:
1) The application establishes a tracking error dynamics model which comprehensively considers dynamics uncertainty and state measurement noise, and compared with the existing model, the model is closer to an actual vehicle dynamics system;
2) The application designs the uncertainty boundary self-adaptive estimation and correction method of the transverse tracking error dynamic system based on the actual state parameter measurement value, and combines the executable upper and lower limits of the steering angle, thereby ensuring the rationality of the uncertainty boundary estimation and leading the observation result to be more practical;
3) The application designs a robust path tracking controller in an analytic form based on the uncertainty boundary estimated in the step 2) to realize path tracking control for simultaneously inhibiting dynamic uncertainty and state measurement noise, and the controller prevents a steering angle saturation phenomenon by inhibiting overestimation of uncertainty while compensating the influence of system uncertainty on tracking performance, thereby being more beneficial to engineering deployment.
The embodiment of the application also provides a path tracking control device 400 of an automatic driving vehicle, as shown in fig. 4, and provides a schematic structural diagram of the path tracking device of the automatic driving vehicle in the embodiment of the application, where the device 400 includes: a first construction unit 410, a second construction unit 420, a third construction unit 430, and a determination unit 440, wherein:
A first construction unit 410 for constructing an ideal tracking error dynamics model;
a second construction unit 420, configured to construct an improved tracking error dynamics model according to the ideal tracking error dynamics model, uncertainty information of vehicle dynamics, and state measurement noise;
a third construction unit 430, configured to construct a path tracking controller of the autonomous vehicle according to the improved tracking error dynamics model;
and a determining unit 440 for determining lateral tracking control information of the autonomous vehicle according to the path tracking controller of the autonomous vehicle, and performing lateral tracking control of the autonomous vehicle according to the lateral control information.
In some embodiments of the present application, the first construction unit 410 is specifically configured to: acquiring state measurement parameters of an automatic driving vehicle; and constructing the ideal tracking error dynamics model according to the state measurement parameters and the vehicle dynamics relation.
In some embodiments of the present application, the second construction unit 420 is specifically configured to: determining uncertainty information of vehicle dynamics, and constructing a tracking error dynamics model containing the uncertainty information according to the ideal tracking error dynamics model and the uncertainty information of the vehicle dynamics; and determining state measurement noise, and constructing the improved tracking error dynamics model according to the tracking error dynamics model containing uncertainty information and the state measurement noise.
In some embodiments of the present application, the second construction unit 420 is specifically configured to: determining the tire cornering stiffness, system modeling errors and external disturbance errors of the autonomous vehicle; and constructing the tracking error dynamics model containing uncertainty information according to the ideal tracking error dynamics model, the tire cornering stiffness of the automatic driving vehicle, the system modeling error and the external interference error.
In some embodiments of the present application, the lateral tracking control information includes a front wheel steering angle, and the third construction unit 430 is specifically configured to: determining a control quantity compensation term containing uncertainty information and state measurement noise according to the improved tracking error dynamics model; and constructing the corresponding relation between the steering angle of the front wheels and the state measurement parameter according to the control quantity compensation item containing the uncertainty information and the state measurement noise, and using the corresponding relation as a path tracking controller of the automatic driving vehicle.
In some embodiments of the present application, the path tracking controller includes a control amount compensation term including uncertainty information and state measurement noise, and the determining unit 440 is specifically configured to: determining an adaptive estimation amount of the uncertainty information according to an adaptive estimation algorithm of the uncertainty information of the vehicle dynamics; determining a compensation control quantity of the uncertainty information and the state measurement noise according to the adaptive estimator of the uncertainty information and the control quantity compensation term containing the uncertainty information and the state measurement noise; and determining the transverse tracking control information of the automatic driving vehicle according to the uncertainty information and the compensation control quantity of the state measurement noise.
In some embodiments of the present application, the improved tracking error dynamics model includes a state measurement parameter including a state measurement noise, the lateral tracking control information includes a front wheel steering angle, and the determining unit 440 is specifically configured to: determining a front wheel steering angle constraint of the autonomous vehicle; determining a compensation correction term according to the front wheel steering angle constraint of the automatic driving vehicle; correcting the state measurement parameters containing state measurement noise by using the compensation correction term to obtain corrected state measurement parameters; and updating the path tracking controller according to the corrected state measurement parameters, and determining transverse tracking control information of the automatic driving vehicle according to the updated path tracking controller.
It can be understood that the above-mentioned path tracking control device for an autonomous vehicle can implement the steps of the path tracking control method for an autonomous vehicle provided in the foregoing embodiments, and the relevant explanation about the path tracking control method for an autonomous vehicle is applicable to the path tracking control device for an autonomous vehicle, which is not repeated herein.
Fig. 5 is a schematic structural view of an electronic device according to an embodiment of the present application. Referring to fig. 5, at the hardware level, the electronic device includes a processor, and optionally an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, network interface, and memory may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 5, but not only one bus or type of bus.
And the memory is used for storing programs. In particular, the program may include program code including computer-operating instructions. The memory may include memory and non-volatile storage and provide instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the path tracking control device of the automatic driving vehicle on a logic level. The processor is used for executing the programs stored in the memory and is specifically used for executing the following operations:
constructing an ideal tracking error dynamics model;
Constructing an improved tracking error dynamics model according to the ideal tracking error dynamics model, 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;
and determining transverse tracking control information of the automatic driving vehicle according to the path tracking controller of the automatic driving vehicle, and performing transverse tracking control of the automatic driving vehicle according to the transverse control information.
The method performed by the path-tracking control apparatus for an autonomous vehicle disclosed in the embodiment of fig. 1 of the present application may be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
The electronic device may also execute the method executed by the path tracking control device of the autonomous vehicle in fig. 1, and implement the functions of the path tracking control device of the autonomous vehicle in the embodiment shown in fig. 1, which is not described herein.
The embodiment of the present application also proposes a computer-readable storage medium storing one or more programs, the one or more programs including instructions, which when executed by an electronic device including a plurality of application programs, enable the electronic device to perform a method performed by a path-tracking control apparatus for an autonomous vehicle in the embodiment shown in fig. 1, and specifically configured to perform:
constructing an ideal tracking error dynamics model;
constructing an improved tracking error dynamics model according to the ideal tracking error dynamics model, 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;
and determining transverse tracking control information of the automatic driving vehicle according to the path tracking controller of the automatic driving vehicle, and performing transverse tracking control of the automatic driving vehicle according to the transverse control information.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may 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, and the like) having computer-usable program code embodied therein.
The present 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 flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may 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, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer 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 technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may 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, and the like) having computer-usable program code embodied therein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (10)
1. A path-following control method of an autonomous vehicle, wherein the method comprises:
constructing an ideal tracking error dynamics model;
constructing an improved tracking error dynamics model according to the ideal tracking error dynamics model, 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;
and determining transverse tracking control information of the automatic driving vehicle according to the path tracking controller of the automatic driving vehicle, and performing transverse tracking control of the automatic driving vehicle according to the transverse control information.
2. The method of claim 1, wherein said constructing an ideal tracking error dynamics model comprises:
acquiring state measurement parameters of an automatic driving vehicle;
and constructing the ideal tracking error dynamics model according to the state measurement parameters and the vehicle dynamics relation.
3. The method of claim 1, wherein said constructing an improved tracking error dynamics model from said ideal tracking error dynamics model and uncertainty information of vehicle dynamics and state measurement noise comprises:
determining uncertainty information of vehicle dynamics, and constructing a tracking error dynamics model containing the uncertainty information according to the ideal tracking error dynamics model and the uncertainty information of the vehicle dynamics;
and determining state measurement noise, and constructing the improved tracking error dynamics model according to the tracking error dynamics model containing uncertainty information and the state measurement noise.
4. The method of claim 3, wherein said constructing a tracking error dynamics model comprising uncertainty information from said ideal tracking error dynamics model and said uncertainty information of vehicle dynamics comprises:
determining the tire cornering stiffness, system modeling errors and external disturbance errors of the autonomous vehicle;
and constructing the tracking error dynamics model containing uncertainty information according to the ideal tracking error dynamics model, the tire cornering stiffness of the automatic driving vehicle, the system modeling error and the external interference error.
5. The method of claim 1, wherein the lateral tracking control information includes a front wheel steering angle, and the constructing a path tracking controller of an autonomous vehicle from the improved tracking error dynamics model includes:
determining a control quantity compensation term containing uncertainty information and state measurement noise according to the improved tracking error dynamics model;
and constructing the corresponding relation between the steering angle of the front wheels and the state measurement parameter according to the control quantity compensation item containing the uncertainty information and the state measurement noise, and using the corresponding relation as a path tracking controller of the automatic driving vehicle.
6. The method of claim 1, wherein the path tracking controller includes a control quantity compensation term including uncertainty information and state measurement noise, and the determining lateral tracking control information of the autonomous vehicle from the path tracking controller of the autonomous vehicle includes:
determining an adaptive estimation amount of the uncertainty information according to an adaptive estimation algorithm of the uncertainty information of the vehicle dynamics;
determining a compensation control quantity of the uncertainty information and the state measurement noise according to the adaptive estimator of the uncertainty information and the control quantity compensation term containing the uncertainty information and the state measurement noise;
and determining the transverse tracking control information of the automatic driving vehicle according to the uncertainty information and the compensation control quantity of the state measurement noise.
7. The method of claim 1, wherein the improved tracking error dynamics model includes a state measurement parameter including state measurement noise, the lateral tracking control information includes a front wheel steering angle, and the determining the lateral tracking control information of the autonomous vehicle from the path tracking controller of the autonomous vehicle includes:
Determining a front wheel steering angle constraint of the autonomous vehicle;
determining a compensation correction term according to the front wheel steering angle constraint of the automatic driving vehicle;
correcting the state measurement parameters containing state measurement noise by using the compensation correction term to obtain corrected state measurement parameters;
and updating the path tracking controller according to the corrected state measurement parameters, and determining transverse tracking control information of the automatic driving vehicle according to the updated path tracking controller.
8. A path-following control device for an autonomous vehicle, wherein the device comprises:
the first construction unit is used for constructing an ideal tracking error dynamics model;
the second construction unit is used for constructing an improved tracking error dynamics model according to the ideal tracking error dynamics model, the uncertainty information of the dynamics of the vehicle and the state measurement noise;
a third construction unit, configured to construct a path tracking controller of the autonomous vehicle according to the improved tracking error dynamics model;
and the determining unit is used for determining transverse tracking control information of the automatic driving vehicle according to the path tracking controller of the automatic driving vehicle and carrying out transverse tracking control of the automatic driving vehicle according to the transverse control information.
9. An electronic device, comprising:
a processor; and
a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method of any of claims 1 to 7.
10. A computer readable storage medium storing one or more programs, which when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method of any of claims 1-7.
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| CN202310869314.4A CN116755448A (en) | 2023-07-14 | 2023-07-14 | Path tracking control method and device for automatic driving vehicle and electronic equipment |
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| CN202310869314.4A CN116755448A (en) | 2023-07-14 | 2023-07-14 | Path tracking control method and device for automatic driving vehicle and electronic equipment |
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