Control method, device, equipment and medium of autonomous vehicle

Abstract

Embodiments of the present disclosure provide a control method, device, equipment and medium of an autonomous vehicle. To solve the problem that the precision and tracking error of the control system are easily affected by external disturbances in the related art, and thus the safety and accuracy of autonomous driving are insufficient, and the user experience is affected. The control method of the autonomous vehicle comprises: obtaining vehicle information and driving information of a vehicle; inputting the vehicle information and the driving information into a deterministic control model to obtain a control signal for autonomous driving, the deterministic control model being obtained based on system noise training; and controlling the vehicle based on the control signal to enable the vehicle to drive autonomously.

Description

Technical Field

[0001] This disclosure relates to the field of autonomous driving, and in particular to a control method, device, equipment and medium for an autonomous vehicle. Background Technology

[0002] In related technologies, during autonomous driving, a feedback controller is typically used to calculate the optimal control result in order to execute the reference path or trajectory obtained from the motion planning module, thereby executing the desired motion trajectory and eliminating tracking errors. However, in practical engineering applications, autonomous vehicles in open road environments are easily affected by a variety of unpredictable factors, making the accuracy of the control system and tracking errors susceptible to external disturbances. This results in insufficient safety and accuracy of autonomous driving, impacting the user experience. Summary of the Invention

[0003] This disclosure provides a control method, apparatus, device, and medium for autonomous vehicles to address the technical problem in related technologies where the accuracy and tracking error of control systems are easily affected by external disturbances, leading to insufficient safety and accuracy of autonomous driving and impacting user experience. The technical solution of this disclosure is as follows:

[0004] In a first aspect, embodiments of this disclosure provide a control method for an autonomous vehicle, including:

[0005] Obtain vehicle information and driving information;

[0006] Vehicle information and driving information are input into a deterministic control model to obtain control signals for autonomous driving. The deterministic control model is trained based on system noise.

[0007] The vehicle is controlled based on control signals to enable autonomous driving.

[0008] In one possible implementation, the method provided in this embodiment of the invention, which obtains vehicle information and driving information, includes:

[0009] Obtain vehicle information;

[0010] The vehicle's driving information is acquired through sensors, including the vehicle's speed, steering angle, position coordinates, and direction angle.

[0011] In one possible implementation, the method provided in this embodiment of the invention inputs vehicle information and driving information into a deterministic control model to obtain control signals for autonomous driving, including:

[0012] Vehicle information and driving information are input into the deterministic control model;

[0013] The target system noise is obtained based on vehicle information;

[0014] The control signal is determined based on the target system noise and driving information.

[0015] In one possible implementation, the deterministic control model is created in the method provided by the embodiments of the present invention through the following method:

[0016] Establish an initial deterministic control model;

[0017] Based on the initial deterministic control model and system noise, the state equation of the deterministic control model is determined;

[0018] Deterministic control models are determined based on state equations.

[0019] In one possible implementation, the method provided in this embodiment of the invention, after determining the state equation of the deterministic control model based on the initial deterministic control model and system noise, further includes:

[0020] The observation equations of the deterministic control model are determined based on observation noise and state equations;

[0021] Deterministic control models are determined based on state equations, including:

[0022] A deterministic control model is determined based on the state equation and the observation equation.

[0023] Secondly, embodiments of this disclosure also provide a control device for an autonomous vehicle, comprising:

[0024] The acquisition unit is used to acquire vehicle information and driving information.

[0025] The processing unit is used to input vehicle information and driving information into the deterministic control model to obtain control signals for autonomous driving. The deterministic control model is trained based on system noise.

[0026] The control unit is used to control the vehicle based on control signals to enable the vehicle to drive automatically.

[0027] In one possible implementation, the acquisition unit in the apparatus provided by the embodiments of the present invention is specifically used for:

[0028] Obtain vehicle information;

[0029] The vehicle's driving information is acquired through sensors, including the vehicle's speed, steering angle, position coordinates, and direction angle.

[0030] In one possible implementation, the processing unit in the apparatus provided by the embodiments of the present invention is specifically used for:

[0031] Vehicle information and driving information are input into the deterministic control model;

[0032] The target system noise is obtained based on vehicle information;

[0033] The control signal is determined based on the target system noise and driving information.

[0034] In one possible implementation, the processing unit in the apparatus provided by the embodiments of the present invention is further configured to create a deterministic control model by means of the following method:

[0035] Establish an initial deterministic control model;

[0036] Based on the initial deterministic control model and system noise, the state equation of the deterministic control model is determined;

[0037] Deterministic control models are determined based on state equations.

[0038] In one possible implementation, the processing unit in the apparatus provided by the embodiments of the present invention is further configured to:

[0039] The observation equations of the deterministic control model are determined based on observation noise and state equations;

[0040] A deterministic control model is determined based on the state equation and the observation equation.

[0041] Thirdly, embodiments of this disclosure also provide an electronic device, including:

[0042] processor;

[0043] Memory used to store the processor's executable instructions;

[0044] The processor is configured to execute the instructions to implement the method described in the first aspect.

[0045] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the method described in the first aspect.

[0046] Fifthly, embodiments of this disclosure also provide a computer program product, including a computer program / instructions, characterized in that the computer program / instructions, when executed by a processor, implement the method described in the first aspect.

[0047] In a sixth aspect, embodiments of this disclosure also provide a vehicle, including a control device for an autonomous vehicle as provided in the second aspect, and an electronic device as provided in the third aspect.

[0048] The technical solutions provided by the embodiments of this disclosure bring at least the following beneficial effects:

[0049] In the embodiments of this disclosure, vehicle information and driving information are first acquired, then input into a deterministic control model to obtain control signals for autonomous driving. Finally, the vehicle is controlled based on the control signals to enable autonomous driving. This provides a control scheme for autonomous vehicles that, by inputting vehicle information and driving information into a deterministic control model, obtains control signals for autonomous driving. It considers system noise in autonomous driving, improves the control performance of the autonomous vehicle, and thus enhances the accuracy and stability of autonomous driving, thereby improving the user experience.

[0050] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0051] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0052] Figure 1 A flowchart illustrating a control method for an autonomous vehicle provided in an embodiment of this disclosure;

[0053] Figure 2 This is a schematic diagram illustrating a specific process for creating a deterministic control model, provided as an embodiment of the present disclosure.

[0054] Figure 3 This is a schematic diagram illustrating another specific process for creating a deterministic control model, provided as an embodiment of the present disclosure.

[0055] Figure 4 This is a schematic diagram of the structure of a control device for an autonomous vehicle provided in an embodiment of the present disclosure;

[0056] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0057] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0058] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0059] Before providing a detailed description of the control method for autonomous vehicles provided in this disclosure, let's first introduce the key terms involved, including:

[0060] Colored noise is a communications term referring to noise with a non-flat power spectral density function. Most noise has a non-white spectrum; however, white noise passing through a channel becomes colored due to the influence of the channel frequency.

[0061] PID controller: PID stands for proportional-integral-derivative, a classic linear controller. PID controllers calculate control operations based on the relationship between the deviation and the control variable. The proportional term proportionalizes the control output to the deviation, the integral term allows the controller to handle static errors, and the derivative term reduces overshoot and oscillations caused by system inertia. Common applications include DC motor control and temperature control.

[0062] LQR controller: LQR is an abbreviation for linear quadratic regulator, a type of optimization controller. The LQR controller attempts to minimize the weighted square of the system state and the resulting control output signal. It is commonly used in continuous-time linear time-invariant systems and has excellent practical applications in control strategy selection, parameter tuning, and robustness studies to various uncertainties.

[0063] MPC controller: MPC stands for Model Predictive Control, a type of nonlinear optimization controller. In each control cycle, an MPC controller optimizes the future control signal by estimating the current state and predicting the system's behavior over a future period. It is commonly used in industrial process control, automotive active suspension systems, and other applications.

[0064] In related technologies, during autonomous driving, feedback controllers, such as MPC, LQR, or PID control methods, are typically used to calculate the optimal control result in order to execute the reference path or trajectory obtained from the motion planning module, thereby executing the desired motion trajectory and eliminating tracking errors. However, in practical engineering applications, autonomous vehicles in open road environments are easily affected by various unpredictable factors, making the accuracy of the control system and tracking errors susceptible to external disturbances. This results in insufficient safety and accuracy of autonomous driving, impacting the user experience.

[0065] The technical solutions provided by the embodiments of this disclosure are described in detail below with reference to the accompanying drawings.

[0066] Figure 1 A flowchart of a control method for an autonomous vehicle provided in the first embodiment of this disclosure is shown below. Figure 1 As shown, the control method for this autonomous vehicle may include the following steps:

[0067] S101, obtain vehicle information and driving information.

[0068] In practice, vehicle information and driving information are acquired by sensors and vehicle-mounted systems installed on the vehicle. Vehicle information includes observations from the vehicle body system that reflect the external environment of the vehicle. Driving information includes the vehicle's speed, front wheel steering angle, and the vehicle's position coordinates and azimuth.

[0069] S102, input vehicle information and driving information into the deterministic control model to obtain control signals for autonomous driving.

[0070] In practice, vehicle information and driving information are input into a deterministic control model. The system noise of the vehicle is determined through the vehicle information. Then, the driving information and system noise are both input into the deterministic control model to obtain control signals for autonomous driving of the vehicle. The system noise is used to eliminate interference, and the driving information is used to obtain control information through the model.

[0071] S103 controls the vehicle based on control signals to enable autonomous driving.

[0072] In practice, control signals include vehicle acceleration, steering angle, brake status, etc. These instructions will be sent to the actuators, brakes and other hardware components on the autonomous vehicle so that the vehicle can move according to the input target and perception information, thereby realizing the control of the autonomous vehicle.

[0073] Figure 2 This disclosure provides a schematic diagram of a specific process for creating a deterministic control model, which includes, as follows: Figure 2The following steps are shown:

[0074] S201, Establish the initial deterministic control model.

[0075] In practical implementation, an initial deterministic control model is first established, and the state equation of this model is as follows:

[0076]

[0077] X(t) = [x, y, θ] T This is a state vector used to represent the vehicle's position coordinates and azimuth angle during autonomous driving;

[0078] U(t)=[v,δ) T The control input vector is used to characterize the speed and front wheel steering angle of the autonomous vehicle.

[0079] Discretize the state equations to transform them into: X(k+1)=AX(k)+BU(k)

[0080] Where A = e AT , T is the sampling time of the discrete system.

[0081] S202, based on the initial deterministic control model and system noise, determine the state equation of the deterministic control model.

[0082] In practical implementation, autonomous driving systems are often accompanied by colored noise, meaning that the noise levels are correlated. Considering the system noise in the above system, the stochastic state equation of the autonomous vehicle control model is as follows: X(k+1)=AX(k)+BU(k)+GW(k); where W(t)=[ω1,ω2,ω3] T The system noise is colored noise, which is non-Gaussian distributed noise, and satisfies W(k+1)=ΨW(k)+ξ(k).

[0083] Where ξ(k) is zero-mean white noise with variance Q, the system state equation can be expressed as follows by expanding the state variables:

[0084]

[0085] S203, Determine the deterministic control model based on the state equation.

[0086] In practice, the deterministic model is determined based on the state equation confirmed in step S202. The model is used to achieve autonomous driving, which can avoid the impact of system noise.

[0087] Figure 3 This is a schematic diagram illustrating another specific process for creating a deterministic control model provided in an embodiment of this disclosure, specifically including, as follows: Figure 3 The following steps are shown:

[0088] S301, Establish the initial deterministic control model.

[0089] In practical implementation, an initial deterministic control model is first established, and the state equation of this model is as follows:

[0090]

[0091] X(t) = [x, y, θ] T This is a state vector used to represent the vehicle's position coordinates and azimuth angle during autonomous driving;

[0092] U(t)=[v,δ) T The control input vector is used to characterize the speed and front wheel steering angle of the autonomous vehicle.

[0093] Discretize the state equations to transform them into: X(k+1)=AX(k)+BU(k)

[0094] Where A = e AT , T is the sampling time of the discrete system.

[0095] S302, based on the initial deterministic control model and system noise, determine the state equation of the deterministic control model.

[0096] In practical implementation, autonomous driving systems are often accompanied by colored noise, meaning that the noise levels are correlated. Considering the system noise in the above system, the stochastic state equation of the autonomous vehicle control model is as follows: X(k+1)=AX(k)+BU(k)+GW(k); where W(t)=[ω1,ω2,ω3] T The system noise is colored noise, which is non-Gaussian distributed noise, and satisfies W(k+1)=ΨW(k)+ξ(k).

[0097] Where ξ(k) is zero-mean white noise with variance Q, the system state equation can be expressed as follows by expanding the state variables:

[0098]

[0099] S303, determining the observation equations of a deterministic control model based on observation noise and state equations.

[0100] In practice, the observations of the stochastic system can be obtained from the vehicle information of the autonomous vehicle. The vehicle information can be measured from the gyroscope and the navigation system. These observations are often accompanied by observation noise due to random factors such as the measurement accuracy of the sensors and interference in the driving environment. Therefore, considering the colored noise in the observation equation, the stochastic observation equation of the autonomous vehicle control system is as follows:

[0101] Z(k) = HX(k) + V(k);

[0102] Where, H∈ 3×3 Let ζ(k) be the observation equation matrix. The observation noise V(k) is also colored noise and is related to the system noise, satisfying the following form: V(k+1)=ΓV(k)+v(k), where ζ(k) is zero-mean white noise with variance R. Further expanding the state variables, the expanded observation equation is expressed as Z′(k)=H′X′(k)+V′(k), where H′=(H 0 Γ), V′(k)=(0 0 ζ(k)), X′(k)=(X(k) W(k) V(k)). T .

[0103] The extended state equation is expressed as: X′(k+1)=A′X′(k)+B′U(k)+G′W′(k);

[0104] in,

[0105] The system state equation and observation equation, after the above expansion, can be combined as follows:

[0106]

[0107] Where, E[W′(k)]=0, E[W′(k)(W′(j)) T ]=diag(0 QR)δ kj Define Q * =diag(0 QR), E[V′(k)]=0, E[V′(k)(V′(j)) T ]=diag(0 0 R)δ kj Define R * =diag(0 0 R),E[W′(k)(′(j)) T ]=diag(0 0 R)δ kj This shows that system noise and measurement noise are related. Further processing allows us to rewrite the state equation as follows:

[0108] X′(k+1)=A′X′(k)+B′U(k)+G′W′(k)+J(Z′(k)-H′X′(k)-V′(k))

[0109] =(A′-JH′)X′(k)+B′U(k)+JZ′(K)+(G′W′(k)-JV′(k))

[0110] =A * X′(k)+B′U(k)+JZ′(k)+W * (k);

[0111] Among them, A * =A′-JH′, W * (k)=G′W′(k)-JV′(k), and the relationship between system noise and measurement noise is as follows:

[0112] E[W * (k)(V′(j)) T ]=(G′-J)diag(0 0 R)δ kj ;

[0113] Therefore, when J = G′, E[W * (k)(V′(j)) T ] = 0, which makes the system noise in the above state equation independent of the measurement noise in the extended system state equation and the observation equation.

[0114] S304. Determine the deterministic control model based on the state equation and the observation equation.

[0115] In practice, the final stochastic control model for the autonomous vehicle is as follows:

[0116]

[0117] Among them, E[W * ()]=0,E[W′()(′()) T ]= * δ kj , E[V′()]=0, E[V′()(′()) T ] = R * δ kj , E[W * ()(′()) T ] = 0, δ is defined as

[0118] Based on the same inventive concept, this disclosure also provides a control device for an autonomous vehicle. For example... Figure 4 As shown, the control device 400 of the autonomous vehicle includes:

[0119] The acquisition unit 401 is used to acquire vehicle information and driving information of the vehicle.

[0120] The processing unit 402 is used to input vehicle information and driving information into a deterministic control model to obtain control signals for autonomous driving. The deterministic control model is obtained based on system noise training.

[0121] Control unit 403 is used to control the vehicle based on control signals to enable the vehicle to drive automatically.

[0122] In one possible implementation, the acquisition unit 401 in the apparatus provided by the embodiments of the present invention is specifically used for:

[0123] Obtain vehicle information;

[0124] The vehicle's driving information is obtained through sensors, including the vehicle's speed, steering angle, position coordinates, and direction angle.

[0125] In one possible implementation, the processing unit 402 in the apparatus provided by the embodiments of the present invention is specifically used for:

[0126] Vehicle and driving information are input into the deterministic control model;

[0127] The target system noise is obtained based on vehicle information;

[0128] The control signal is determined based on the target system noise and driving information.

[0129] In one possible implementation, the processing unit 402 in the apparatus provided by the embodiments of the present invention is further configured to create a deterministic control model by means of the following method:

[0130] Establish an initial deterministic control model;

[0131] Based on the initial deterministic control model and system noise, the state equation of the deterministic control model is determined;

[0132] Deterministic control models are determined based on state equations.

[0133] In one possible implementation, the processing unit 402 in the apparatus provided by the embodiments of the present invention is further configured to:

[0134] The observation equations of the deterministic control model are determined based on observation noise and state equations;

[0135] A deterministic control model is determined based on the state equation and the observation equation.

[0136] In addition, combined Figures 1-4 The control method and apparatus for autonomous vehicles described in this application can be implemented by electronic devices. Figure 5 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0137] like Figure 5 As shown, the electronic device 1000 may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1008 into a random access memory (RAM) 1003 to implement the control method of an autonomous vehicle as described in the embodiments of this disclosure. Various programs and data required for the operation of the electronic device 1000 are also stored in the RAM 1003. The processing device 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0138] Typically, the following devices can be connected to the I / O interface 1005: input devices 1006 including, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 1007 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1008 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows electronic device 1000 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 An electronic device 1000 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0139] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts, thereby implementing the voice control method as described above. In such embodiments, the computer program can be downloaded and installed from a network via communication device 1009, or installed from storage device 1008, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of embodiments of this disclosure.

[0140] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0141] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0142] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0143] The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to:

[0144] Obtain vehicle information and driving information;

[0145] Vehicle information and driving information are input into a deterministic control model to obtain control signals for autonomous driving. The deterministic control model is trained based on system noise.

[0146] The vehicle is controlled based on control signals to enable autonomous driving.

[0147] Optionally, when one or more of the above-described procedures are executed by the electronic device, the electronic device may also perform other steps described in the above embodiments.

[0148] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0149] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0150] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0151] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0152] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0153] The control method for autonomous vehicles mentioned in the embodiments of the present invention includes:

[0154] In the embodiments of this disclosure, vehicle information and driving information are first acquired, then input into a deterministic control model to obtain control signals for autonomous driving. Finally, the vehicle is controlled based on the control signals to enable autonomous driving. This provides a control scheme for autonomous vehicles that, by inputting vehicle information and driving information into a deterministic control model, obtains control signals for autonomous driving. It considers system noise in autonomous driving, improves the control performance of the autonomous vehicle, and thus enhances the accuracy and stability of autonomous driving, thereby improving the user experience.

[0155] 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.

[0156] This application also provides a vehicle, including, for example: Figure 4 The control device of the autonomous vehicle shown, and as Figure 5The electronic device shown.

[0157] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0158] 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.

[0159] 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.

[0160] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0161] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A control method for an autonomous vehicle, characterized in that, include: Obtain vehicle information and driving information; The vehicle information and the driving information are input into a deterministic control model to obtain control signals for autonomous driving. The deterministic control model is obtained based on system noise training. The vehicle is controlled based on the control signals to enable autonomous driving. The deterministic control model is created using the following method: An initial deterministic control model is established, wherein the state equation of the initial deterministic control model is: ； This is a state vector used to represent the vehicle's position coordinates and azimuth angle during autonomous driving; The control input vector is used to characterize the speed and front wheel steering angle of the autonomous vehicle. Discretize the state equations of the initial deterministic control model and transform them into: ; in, , , The sampling time of the discrete system; Based on the initial deterministic control model and the system noise, the state equation of the deterministic control model is determined by extending the state variables, wherein the system noise is colored noise, and the state equation of the deterministic control model is: ;in It is system noise, and satisfies It is zero-mean white noise with variance of The state equations of the extended deterministic control model are: ; in, , , , ; The observation equations for the deterministic control model are determined based on observation noise and state equations. The observation equation is: ; in, The observation equation matrix, observation noise It is colored noise, represented as: ,in It is zero-mean white noise with variance of The extended observation equation is: ,in , , ; A deterministic control model is determined based on the state equation and the observation equation.

2. The method according to claim 1, characterized in that, The acquisition of vehicle information and driving information includes: Obtain the vehicle information of the vehicle; The vehicle's driving information is acquired through sensors, including the vehicle's speed, steering angle, position coordinates, and direction angle.

3. The method according to claim 2, characterized in that, The step of inputting the vehicle information and the driving information into a deterministic control model to obtain control signals for autonomous driving includes: The vehicle information and the driving information are input into the deterministic control model; The target system noise is obtained based on the vehicle information; The control signal is determined based on the target system noise and the driving information.

4. A control device for an autonomous vehicle, characterized in that, include: The acquisition unit is used to acquire vehicle information and driving information. The processing unit is used to input the vehicle information and the driving information into a deterministic control model to obtain control signals for autonomous driving. The deterministic control model is obtained based on system noise training, and is created by the following method: An initial deterministic control model is established, wherein the state equation of the initial deterministic control model is: ； This is a state vector used to represent the vehicle's position coordinates and azimuth angle during autonomous driving; The control input vector is used to characterize the speed and front wheel steering angle of the autonomous vehicle. Discretize the state equations and transform them into: ; in, , , The sampling time of the discrete system; Based on the initial deterministic control model and the system noise, the state equation of the deterministic control model is determined by extending the state variables, wherein the system noise is colored noise, and the state equation of the deterministic control model is: ;in It is system noise, and satisfies It is zero-mean white noise with variance of The state equations of the extended deterministic control model are: ; in, , , , ; The observation equations of the deterministic control model are determined based on observation noise and state equations; The observation equation is: ; in, The observation equation matrix, observation noise It is colored noise, represented as: ,in It is zero-mean white noise with variance of The extended observation equation is: ,in , , ; Determine the deterministic control model based on the state equation and the observation equation; A control unit is used to control the vehicle based on the control signal to enable the vehicle to drive automatically.

5. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the control method for an autonomous vehicle as described in any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the control method for the autonomous vehicle as described in any one of claims 1-3.

7. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the control method for the autonomous vehicle as described in any one of claims 1-3.

8. A vehicle, characterized in that, It includes the control device for an autonomous vehicle as described in claim 4, and the electronic device as described in claim 5.

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