Transverse and longitudinal combined control method and device of automatic driving vehicle and electronic equipment

Abstract

The invention provides a transverse and longitudinal combined control method and device for an automatic driving vehicle, electronic equipment and a medium, and relates to the technical field of automatic driving, in particular to the technical field of trajectory tracking and model prediction control. According to the implementation scheme, a transverse and longitudinal coupling state prediction model and an expected track of the automatic driving vehicle are obtained; the current state quantity and the current control quantity of the automatic driving vehicle are obtained; based on the current state quantity, the current control quantity, the transverse and longitudinal coupling state prediction model and the expected trajectory, a model prediction control algorithm is used for solving a target control quantity of trajectory tracking, model prediction control comprises quadratic programming, and a cost function of the quadratic programming at least comprises a transverse and longitudinal decoupling target error; and controlling the autonomous vehicle to perform trajectory tracking based on the target control quantity.

Description

Technical Field

[0001] The present disclosure relates to the field of autonomous driving technology, in particular to the field of trajectory tracking and model predictive control technology, and specifically to a method, device, electronic device, computer-readable storage medium, and computer program product for combined lateral and longitudinal control of an autonomous driving vehicle. Background Art

[0002] Trajectory tracking refers to describing the vehicle's motion state and behavior by constructing a vehicle system model, and determining the control amount for the vehicle based on the vehicle's current motion state and predetermined trajectory, so as to control the vehicle along the predetermined trajectory based on the control amount.

[0003] Model Predictive Control (MPC) is a strategy that uses optimization methods to solve control problems. Its main process includes:

[0004] (1) Establish a prediction model: Establish a dynamic model (state prediction model) of the controlled object (e.g., an autonomous vehicle);

[0005] (2) Rolling Optimization: For each time step, the dynamic model is used to predict the state of the controlled object in the future based on the current state of the controlled object and the future control variables, and the optimal set of control variable sequences is solved online according to a certain performance indicator (cost function). The first control variable of the set of control variable sequences is used as the output to control the controlled object; and

[0006] (3) Feedback correction: Detect the actual output of the controlled object at the next time step, and correct the model-based prediction results based on the actual output, and then continue the above-mentioned rolling optimization for the next time step.

[0007] In related technologies, model predictive control algorithms can be used to optimize and solve the control variables for autonomous vehicle trajectory tracking. A state prediction model can be established based on the autonomous vehicle's dynamic model to predict the vehicle's state. A cost function is then established based on the deviation between the predicted state and the desired state. After a rolling optimization process, an optimized control variable is output, which is then used to control the autonomous vehicle's trajectory tracking.

[0008] The approaches described in this section are not necessarily approaches that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any approach described in this section is prior art simply by virtue of its inclusion in this section. Similarly, unless otherwise indicated, the issues raised in this section should not be considered as having been recognized in any prior art. Summary of the Invention

[0009] The present disclosure provides a method, device, electronic device, computer-readable storage medium, and computer program product for combined lateral and longitudinal control of an autonomous driving vehicle.

[0010] According to one aspect of the present disclosure, a method for combined lateral and longitudinal control of an autonomous vehicle is provided, comprising: obtaining a lateral and longitudinal coupled state prediction model and a desired trajectory of the autonomous vehicle, wherein the lateral and longitudinal coupled state prediction model is used to perform trajectory tracking for the autonomous vehicle; obtaining a current state quantity and a current control quantity of the autonomous vehicle, wherein the current state quantity indicates the motion state of the autonomous vehicle at a current moment, and the current control quantity indicates the control performed on the autonomous vehicle at the current moment; solving a target control quantity for trajectory tracking using a model predictive control algorithm based on the current state quantity, the current control quantity, the lateral and longitudinal coupled state prediction model, and the desired trajectory, wherein the model predictive control algorithm includes quadratic programming, a cost function of the quadratic programming including at least a target error for lateral and longitudinal decoupling, the target error being obtained by performing lateral and longitudinal decoupling on an error between a first predicted state quantity output by the lateral and longitudinal coupled state prediction model and a corresponding first desired state quantity, the first desired state quantity being associated with the desired trajectory; and controlling the autonomous vehicle to perform trajectory tracking based on the target control quantity.

[0011] According to another aspect of the present disclosure, a lateral and longitudinal combined control apparatus for an autonomous vehicle is provided, comprising: a first module configured to obtain a lateral and longitudinal coupled state prediction model and a desired trajectory of the autonomous vehicle, wherein the lateral and longitudinal coupled state prediction model is used to track the trajectory of the autonomous vehicle; a second module configured to obtain a current state variable and a current control variable of the autonomous vehicle, wherein the current state variable indicates the motion state of the autonomous vehicle at a current moment, and the current control variable indicates the control being performed on the autonomous vehicle at the current moment; a third module configured to solve a target control variable for trajectory tracking using a model predictive control algorithm based on the current state variable, the current control variable, the lateral and longitudinal coupled state prediction model, and the desired trajectory, wherein the model predictive control algorithm includes quadratic programming, wherein a cost function of the quadratic programming includes at least a target error for lateral and longitudinal decoupling, the target error being obtained by performing lateral and longitudinal decoupling on an error between a first predicted state variable output by the lateral and longitudinal coupled state prediction model and a corresponding first desired state variable, the first desired state variable being associated with the desired trajectory; and a fourth module configured to control the autonomous vehicle to track the trajectory based on the target control variable.

[0012] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the above method.

[0013] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the above method.

[0014] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the above method when executed by a processor.

[0015] According to another aspect of the present disclosure, an autonomous driving vehicle is provided, comprising the electronic device as described above.

[0016] According to one or more embodiments of the present disclosure, a method for combined lateral and longitudinal control of an autonomous driving vehicle is provided. When optimizing and solving the target control quantity for trajectory tracking of the autonomous driving vehicle based on a model predictive control algorithm, the motion state of the autonomous driving vehicle is predicted by establishing a lateral and longitudinal coupling state prediction model of the autonomous driving vehicle, and in the rolling optimization process, the optimized control quantity sequence is solved based on the error amount of lateral and longitudinal decoupling as a cost function. In this way, based on this "lateral and longitudinal coupling state prediction" combined with the "lateral and longitudinal decoupling optimization solution", the interaction of the control quantities in the lateral and longitudinal directions can be fully considered, the overall balance and accuracy of the output control quantity in the lateral and longitudinal directions can be effectively coordinated, and the vehicle driving smoothness when trajectory tracking is performed based on the target control quantity can be improved.

[0017] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings illustrate exemplary embodiments and constitute a part of the specification. Together with the description of the specification, they serve to explain exemplary implementation of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals designate similar, but not necessarily identical, elements.

[0019] Figure 1 is a schematic diagram illustrating an example system in which the various methods described herein may be implemented according to an exemplary embodiment;

[0020] Figure 2A flowchart of a method for combined lateral and longitudinal control of an autonomous driving vehicle according to an embodiment of the present disclosure is shown;

[0021] Figure 3 A partial flow chart of another method for combined lateral and longitudinal control of an autonomous driving vehicle according to an embodiment of the present disclosure is shown;

[0022] Figure 4 A schematic diagram of force analysis of a dynamics model of an autonomous driving vehicle according to an embodiment of the present disclosure is shown;

[0023] Figure 5 A partial flow chart of another method for combined lateral and longitudinal control of an autonomous driving vehicle according to an embodiment of the present disclosure is shown;

[0024] Figure 6 A partial flow chart of another method for combined lateral and longitudinal control of an autonomous driving vehicle according to an embodiment of the present disclosure is shown;

[0025] Figure 7 A partial flow chart of another method for combined lateral and longitudinal control of an autonomous driving vehicle according to an embodiment of the present disclosure is shown;

[0026] Figure 8 A structural block diagram of a lateral and longitudinal combined control device for an autonomous driving vehicle according to an embodiment of the present disclosure is shown; and

[0027] Figure 9 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0028] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be appreciated by those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0029] In this disclosure, unless otherwise specified, the use of terms such as "first" and "second" to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, while in some cases, based on the context of the description, they may also refer to different instances.

[0030] The terms used in the descriptions of the various examples described in this disclosure are for the purpose of describing specific examples only and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or more. In addition, the term "and / or" used in this disclosure encompasses any one and all possible combinations of the listed items.

[0031] In related technologies, model predictive control algorithms can be used to optimize and solve the control variables for autonomous vehicle trajectory tracking. A state prediction model can be established based on the autonomous vehicle's dynamic model to predict the vehicle's state. A cost function is then established based on the deviation between the predicted state and the desired state. After a rolling optimization process, an optimized control variable is output, which is then used to control the autonomous vehicle's trajectory tracking.

[0032] However, because the various state variables in the vehicle dynamics model are not decoupled laterally and longitudinally, the resulting state prediction model based on the vehicle dynamics model is a laterally and longitudinally coupled state prediction model. This model predicts the predicted state variables in a laterally and longitudinally coupled manner, and the error between the predicted state variables and the desired state variables is also laterally and longitudinally coupled. However, the vehicle trajectory tracking problem is sensitive to errors in the lateral and longitudinal directions to varying degrees. Therefore, when constructing a cost function based on the lateral and longitudinal coupled errors for quadratic programming, it is difficult to achieve an effective balance between the output control variables in the lateral and longitudinal directions, resulting in suboptimal control results.

[0033] To solve the above problems, the present disclosure provides a method for joint horizontal and vertical control of an autonomous driving vehicle. When optimizing and solving the target control quantity of trajectory tracking of the autonomous driving vehicle based on a model predictive control algorithm, the motion state of the autonomous driving vehicle is predicted by establishing a horizontal and vertical coupling state prediction model of the autonomous driving vehicle, and in the rolling optimization process, the error amount of horizontal and vertical decoupling is used as a cost function to solve the optimized control quantity sequence. In this way, based on this "horizontal and vertical coupling state prediction" combined with "horizontal and vertical decoupling optimization solution", the interaction of the control quantity in the horizontal and vertical directions can be fully considered, the overall balance and accuracy of the output control quantity in the horizontal and vertical directions can be effectively coordinated, and the vehicle driving smoothness when trajectory tracking is performed based on the target control quantity can be improved.

[0034] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0035] Figure 1 FIG2 is a schematic diagram of an exemplary system 100 in which the various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1, the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 can be configured to execute one or more applications.

[0036] In an embodiment of the present disclosure, the server 120 may run one or more services or software applications that enable execution of a method for combined lateral and longitudinal control of an autonomous driving vehicle.

[0037] In some embodiments, server 120 may also provide other services or software applications that may include non-virtualized environments and virtualized environments. In some embodiments, these services may be provided as web-based services or cloud services, such as provided to users of client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0038] exist Figure 1 In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 may, in turn, utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is an example of a system for implementing the methods described herein and is not intended to be limiting.

[0039] The user may use client devices 101, 102, 103, 104, 105 and / or 106 to perform the method for the combined lateral and longitudinal control of the autonomous vehicle. The client device may provide an interface that enables the user of the client device to interact with the client device. The client device may also output information to the user via the interface. Figure 1 Only six client devices are depicted, but one skilled in the art will appreciate that the present disclosure can support any number of client devices.

[0040] Client devices 101, 102, 103, 104, 105, and / or 106 may include various types of computer devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptops), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, etc. These computer devices may run various types and versions of software applications and operating systems, such as Microsoft Windows, Apple iOS, UNIX-like operating systems, Linux, or Linux-like operating systems (such as Google Chrome OS); or include various mobile operating systems, such as Microsoft Windows Mobile OS, iOS, Windows Phone, and Android. Portable handheld devices may include cellular phones, smartphones, tablet computers, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Gaming systems may include various handheld gaming devices, internet-enabled gaming devices, etc. Client devices are capable of executing a variety of different applications, such as various internet-related applications, communication applications (such as email applications), and short message service (SMS) applications, and may use various communication protocols.

[0041] The network 110 may be any type of network known to those skilled in the art that can support data communications using any of a variety of available protocols, including but not limited to TCP / IP, SNA, IPX, etc. By way of example only, the one or more networks 110 may be a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., Bluetooth, WIFI), and / or any combination of these and / or other networks.

[0042] Server 120 may include one or more general-purpose computers, specialized server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0043] The computing units in the server 120 may run one or more operating systems including any of the operating systems described above as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and / or middle-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

[0044] In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105, and 106. Server 120 may also include one or more applications to display the data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and 106.

[0045] In some embodiments, server 120 may be a distributed system server or a server integrated with blockchain. Server 120 may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host equipped with artificial intelligence technology. A cloud server is a host product within the cloud computing service system that addresses the management difficulties and poor scalability of traditional physical hosts and virtual private servers (VPS) services.

[0046] The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as text files. The databases 130 may reside in a variety of locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and communicate with the server 120 via a network-based or dedicated connection. The databases 130 may be of different types. In some embodiments, the databases used by the server 120 may be, for example, relational databases. One or more of these databases may store, update, and retrieve data to and from the databases in response to commands.

[0047] In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the applications may be different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

[0048] Figure 1 The system 100 may be configured and operated in various ways to enable application of the various methods and apparatuses described in accordance with the present disclosure.

[0049] Figure 2 A flow chart of a method for combined lateral and longitudinal control of an autonomous driving vehicle according to an embodiment of the present disclosure is shown.

[0050] like Figure 2 As shown, the method 200 for combined lateral and longitudinal control of an autonomous driving vehicle includes:

[0051] Step 210: Obtain a lateral and longitudinal coupling state prediction model and a desired trajectory of the autonomous driving vehicle, wherein the lateral and longitudinal coupling state prediction model is used to track the trajectory of the autonomous driving vehicle;

[0052] Step 220: Acquire a current state quantity and a current control quantity of the autonomous driving vehicle, wherein the current state quantity indicates the motion state of the autonomous driving vehicle at the current moment, and the current control quantity indicates the control being performed on the autonomous driving vehicle at the current moment;

[0053] Step 230: Based on the current state variable, the current control variable, the lateral and longitudinal coupling state prediction model, and the desired trajectory, a model predictive control algorithm is used to solve the target control variable for trajectory tracking, wherein the model predictive control algorithm includes quadratic programming, and the cost function of the quadratic programming includes at least a target error of lateral and longitudinal decoupling. The target error is obtained by performing lateral and longitudinal decoupling on the error between a first predicted state variable output by the lateral and longitudinal coupling state prediction model and a corresponding first desired state variable, and the first desired state variable is associated with the desired trajectory; and

[0054] Step 240: Control the autonomous driving vehicle to perform trajectory tracking based on the target control amount.

[0055] Therefore, when optimizing and solving the target control quantity of trajectory tracking of an autonomous vehicle based on the model predictive control algorithm, the motion state of the autonomous vehicle is predicted by establishing a lateral and longitudinal coupling state prediction model of the autonomous vehicle, and in the rolling optimization process, the optimized control quantity sequence is solved based on the error amount of lateral and longitudinal decoupling as the cost function. In this way, based on this "lateral and longitudinal coupling state prediction" combined with "lateral and longitudinal decoupling optimization solution", the interaction of the control quantities in the lateral and longitudinal directions can be fully considered, the overall balance and accuracy of the output control quantities in the lateral and vertical directions can be effectively coordinated, and the vehicle driving smoothness when trajectory tracking is performed based on the target control quantity can be improved.

[0056] In particular, for extreme working conditions such as sharp turns and emergency obstacle avoidance, the vehicle's steering angle can be adjusted more naturally and smoothly during acceleration and braking, effectively improving the driving smoothness and ride comfort of the autonomous vehicle.

[0057] In step 210 , the lateral and longitudinal coupling state prediction model is used to describe the motion state and control of the autonomous driving vehicle, and the expected trajectory is used as a reference trajectory for the autonomous driving vehicle to track.

[0058] Figure 3 A partial flow chart of another method for combined lateral and longitudinal control of an autonomous driving vehicle according to an embodiment of the present disclosure is shown.

[0059] According to some embodiments, Figure 3 Said step 210 includes:

[0060] Step 310: Obtain a dynamic model of the autonomous driving vehicle; and

[0061] Step 320: Perform a second linearization process on the dynamic model to obtain a transverse and longitudinal coupling state prediction model.

[0062] The dynamics models of autonomous vehicles are typically nonlinear, and predicting state based on these models requires significant computational power. Therefore, linearizing these nonlinear dynamics models can reduce computational complexity and improve processing efficiency.

[0063] According to some embodiments, step 310 includes:

[0064] An initial vehicle model is determined based on at least the position coordinates of the autonomous driving vehicle in the world coordinate system, the lateral velocity along the transverse axis in the vehicle body coordinate system, the longitudinal velocity along the longitudinal axis in the vehicle body coordinate system, the yaw angle and the yaw angular velocity, wherein the transverse axis direction is the direction along the direction of the vehicle head, and the longitudinal axis direction is the direction parallel to the ground and perpendicular to the transverse axis direction.

[0065] By constructing a dynamic model of an autonomous driving vehicle based on the above-mentioned multiple state quantities, it is possible to accurately describe the vehicle's motion state while reasonably simplifying the model and improving the processing efficiency of subsequent model predictive control.

[0066] Figure 4 A schematic diagram of force analysis of a dynamic model of an autonomous driving vehicle according to an embodiment of the present disclosure is shown.

[0067] In step 310, illustratively, reference Figure 4 , establish the world coordinate system XOY and the vehicle coordinate system xoy. According to the kinematic constraints of the autonomous driving vehicle, its dynamic model can be constructed as the following nonlinear state equations:

[0068]

[0069] Where X represents the state quantity indicating the motion state of the autonomous vehicle at time t, and U represents the control quantity indicating the control of the autonomous vehicle at time t. The motion state of the autonomous vehicle at time t+1 is represented by X and U v x represents the speed of the autonomous vehicle along the x-axis in the vehicle coordinate system, v y Indicates the speed of the autonomous driving vehicle along the y-axis in the vehicle coordinate system, represents the yaw angle of the autonomous vehicle, ω represents the yaw angular velocity of the autonomous vehicle, a represents the distance from the center of mass of the autonomous vehicle to the center of the front axle (an inherent property of the vehicle), represents the derivative of the yaw angle of the autonomous vehicle, C f Represents the side height coefficient of the front wheel of the autonomous vehicle, C r Represents the side height coefficient of the rear wheel of the autonomous vehicle, l f Represents the length between the front wheel of the autonomous vehicle and the geometric center point of the vehicle, l r represents the length between the rear wheel of the autonomous vehicle and the geometric center point of the vehicle, δ represents the wheel deviation angle of the autonomous vehicle, m represents the mass of the autonomous vehicle, and I z Represents the moment of inertia of the autonomous vehicle around the z-axis.

[0070] As you can see, the above dynamic model consists of six state equations that relate different state quantities. The first state equation relates to the autonomous vehicle's position along the x-axis in the world coordinate system, the second state equation relates to the autonomous vehicle's position along the y-axis in the world coordinate system, the third state equation relates to the autonomous vehicle's yaw angle, the fourth state equation relates to the autonomous vehicle's velocity along the x-axis in the vehicle body coordinate system, the fifth state equation relates to the autonomous vehicle's velocity along the y-axis in the vehicle body coordinate system, and the sixth state equation relates to the autonomous vehicle's yaw angular velocity.

[0071] Understandably, It is the state quantity at time t+1 represented by the state quantity X at time t and the control quantity U. Therefore, the above six state equations are all derivative forms of the mechanical model of the relevant state quantities, which can realize the state quantity prediction based on the time domain.

[0072] In addition, it should be noted that the mechanical model of the above six related state quantities can be established based on the relevant content of theoretical mechanics. The specific derivation process belongs to the existing technology and will not be described in detail.

[0073] At step 320 , the second linearization process may include a first-order Taylor expansion and bilinear discretization.

[0074] Referring to the example of the above dynamic model, through the first-order Taylor expansion, the following linear time-varying model can be obtained:

[0075]

[0076] Among them, X0 represents a state quantity indicating the motion state of the autonomous driving vehicle at the initial moment (for example, the current moment), and U0 represents a control quantity indicating the control of the autonomous driving vehicle at the initial moment.

[0077] Furthermore, since the above-mentioned linear time-varying model is a continuous model and cannot be directly used as a state prediction model, the expanded model can be bilinearly discretized to obtain a horizontally and vertically coupled state prediction model that can recursively deduce the predicted state quantity sequence of the autonomous driving vehicle within a certain time domain.

[0078] It is understandable that each state equation in the above dynamic model is a state equation of horizontal and vertical coupling. Accordingly, the linear time-varying model obtained after the second linearization processing is a state prediction model of horizontal and vertical coupling.

[0079] In step 220, the current moment can be regarded as the current time step, so that the state quantity of the autonomous driving vehicle in the subsequent time domain can be predicted based on the current state quantity and current control quantity based on the current time.

[0080] In step 230 , a specific state quantity associated with the target error may be determined based on the performance to be evaluated.

[0081] According to some embodiments, the target error includes a lateral position error, a longitudinal position error, a lateral velocity error, and a longitudinal velocity error.

[0082] Therefore, by using position and speed as performance evaluation indicators and decoupling their corresponding errors horizontally and vertically to construct a cost function for quadratic programming, it is possible to more accurately control the autonomous driving vehicle to travel along the desired trajectory as much as possible, thereby improving the overall control effect.

[0083] Exemplarily, the cost function may be a nonlinear system of equations including at least the following error equation:

[0084]

[0085]

[0086] Among them, e pc represents the longitudinal position error, e pl represents the lateral position error, e vc represents the longitudinal velocity error, e vl represents the lateral velocity error, represents the desired yaw angle of the autonomous vehicle, represents the predicted yaw angle of the autonomous vehicle, x ref represents the expected lateral position of the autonomous vehicle in the world coordinate system, x represents the predicted lateral position of the autonomous vehicle in the world coordinate system, y ref represents the desired longitudinal position of the autonomous vehicle in the world coordinate system, and y represents the predicted longitudinal position of the autonomous vehicle in the world coordinate system.

[0087] Figure 5 A partial flow chart of another method for combined lateral and longitudinal control of an autonomous driving vehicle according to an embodiment of the present disclosure is shown.

[0088] According to some embodiments, Figure 5 As shown, step 230 includes:

[0089] Step 510: Based on the current state variable and the current control variable, determine a prediction variable sequence for the desired trajectory using the lateral and longitudinal coupling state prediction model, wherein each prediction variable in the prediction variable sequence includes a second predicted state variable indicating the motion state of the autonomous driving vehicle at a corresponding moment, and each prediction variable also includes a predicted control variable indicating the control performed on the autonomous driving vehicle at the corresponding moment;

[0090] Step 520: For each predicted quantity,

[0091] Step 521: Based on the expected trajectory, determine an initial error between a second predicted state quantity of the predicted quantity and a second expected state quantity corresponding to the predicted quantity;

[0092] Step 522: Decouple the initial error in the horizontal and vertical directions to obtain the target error.

[0093] Step 523: Using the target error as at least part of the cost function to perform quadratic programming; and

[0094] Step 524: Optimize the prediction control variable of the previous prediction variable in the prediction variable sequence according to the solution result of the quadratic programming; and

[0095] Step 530: Determine the optimized control quantity based on the optimized predicted quantity sequence.

[0096] The target error is obtained by decoupling the initial error in the horizontal and vertical directions, and a cost function is constructed based on the target error to perform quadratic programming solution. This can effectively balance the different sensitivities to the error in the horizontal and vertical directions in the trajectory tracking problem and improve the overall effect of control optimization.

[0097] In step 510 , in model predictive control, the predicted quantities included in the predicted quantity sequence may correspond to a finite number of future time steps for trajectory tracking of the desired trajectory, so that after optimizing for the current moment, rolling optimization and feedback correction can be re-performed at the next moment.

[0098] It is understandable that the derivation process of outputting the predicted quantity sequence for the target time domain through matrix transformation and iterative calculation belongs to the existing technology and will not be described in detail.

[0099] Figure 6 A partial flow chart of another method for combined lateral and longitudinal control of an autonomous driving vehicle according to an embodiment of the present disclosure is shown.

[0100] At step 520, according to some embodiments, Figure 5 As shown, step 521 includes:

[0101] Step 610: Determine a predicted trajectory point corresponding to the predicted state value according to the second predicted state value;

[0102] Step 620: Determine a point in the desired trajectory whose distance from the predicted trajectory point is less than a target threshold as a preview point; and

[0103] Step 630: Determine a second desired state value based on the desired motion state of the autonomous driving vehicle when it reaches the preview point.

[0104] Therefore, in the process of optimizing trajectory tracking, the preview point can be determined based on the distance between the predicted trajectory point and the expected trajectory point, and the expected motion state of the preview point can be used as the optimization benchmark. There is no need to project the predicted trajectory point into the expected trajectory, which can effectively reduce the probability of trajectory jumps, make the final output optimization control quantity more stable, and improve the consistency between the actual motion trajectory and the expected motion trajectory of the autonomous driving vehicle.

[0105] In step 620, the target threshold may be, for example, 0.1 m, 0.5 m, or 1 m. For example, the preview point may also be a point in the desired trajectory with the shortest branch distance from the predicted trajectory point.

[0106] In step 630, for the desired trajectory, multiple desired trajectory points can be pre-selected, and the corresponding desired motion state (for example, position, velocity, acceleration, etc.) of each desired trajectory point can be determined through the trajectory planning model and stored for use in model predictive control.

[0107] According to some embodiments, step 530 includes:

[0108] The predicted control variable included in the first predicted variable in the optimized predicted variable sequence is used as the optimized control variable.

[0109] Since model predictive control is based on the optimization solution of a finite time domain, by using only the predicted control quantity of the first predicted quantity as the optimized control quantity for control at the current moment, the control of the next time step will not be affected by the prediction error from the previous time step, thereby further improving the accuracy and stability of control.

[0110] Figure 7 A partial flow chart of another method for combined lateral and longitudinal control of an autonomous driving vehicle according to an embodiment of the present disclosure is shown.

[0111] According to some embodiments, the cost function is a nonlinear function, such as Figure 7 As shown, step 523 includes:

[0112] Step 710: Perform a first linearization process on the nonlinear cost function to obtain a linear cost function; and

[0113] Step 720: Perform quadratic programming based on the linear cost function.

[0114] The cost function constructed based on the target error is usually a set of nonlinear equations, and linearizing it can further reduce the computational difficulty and increase the scope of application.

[0115] In step 523 , the specific process of performing the optimization solution of the quadratic programming based on the cost function belongs to the prior art and will not be described in detail.

[0116] In this example, in addition to the cost function, further constraints can be added to the quadratic programming solution. For example, based on actual operating conditions, constraints can be added for the autonomous vehicle's tire slip angle, front wheel angle, acceleration, and center of mass slip angle to improve the effectiveness of the quadratic programming solution.

[0117] In step 524, since the predicted state quantity of the current time step is determined by the predicted state quantity and predicted control quantity of the previous time step, the predicted control quantity of the previous time step is optimized for the solution result of the current time step.

[0118] At step 710 , the first linearization process may be, for example, a first-order Taylor expansion.

[0119] Referring to the example of the vehicle dynamics model above, the linear cost function can be shown as follows:

[0120]

[0121] Therefore, a quadratic form can be constructed based on the linear cost function to complete the design of the quadratic programming cost function, thereby realizing the optimization solution process based on the model predictive control algorithm and obtaining the target control quantity.

[0122] According to another aspect of the present disclosure, a lateral and longitudinal combined control device for an autonomous driving vehicle is provided. Figure 8 As shown, the lateral and longitudinal joint control device 800 of the autonomous driving vehicle includes: a first module 810, configured to obtain a lateral and longitudinal coupling state prediction model and a desired trajectory of the autonomous driving vehicle, wherein the lateral and longitudinal coupling state prediction model is used to track the trajectory of the autonomous driving vehicle; a second module 820, configured to obtain a current state quantity and a current control quantity of the autonomous driving vehicle, wherein the current state quantity indicates the motion state of the autonomous driving vehicle at the current moment, and the current control quantity indicates the control being performed on the autonomous driving vehicle at the current moment; a third module 830, configured to use a model predictive control algorithm to solve a target control quantity for trajectory tracking based on the current state quantity, the current control quantity, the lateral and longitudinal coupling state prediction model, and the desired trajectory, wherein the model predictive control algorithm includes quadratic programming, the cost function of the quadratic programming at least including a target error of lateral and longitudinal decoupling, the target error being obtained by performing lateral and longitudinal decoupling on the error between a first predicted state quantity output by the lateral and longitudinal coupling state prediction model and a corresponding first desired state quantity, the first desired state quantity being associated with the desired trajectory; and a fourth module 840, configured to control the autonomous driving vehicle to track the trajectory based on the target control quantity.

[0123] According to another aspect of the present disclosure, an electronic device is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the aforementioned method.

[0124] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is further provided, wherein the computer instructions are used to enable the computer to execute the aforementioned method.

[0125] According to another aspect of the present disclosure, a computer program product is further provided, including a computer program, wherein the computer program implements the aforementioned method when executed by a processor.

[0126] According to another aspect of the present disclosure, an autonomous driving vehicle is also provided, comprising the electronic device as described above.

[0127] like Figure 9As shown, the electronic device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 908 into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the electronic device 900 can also be stored. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0128] Multiple components in the electronic device 900 are connected to the I / O interface 905, including: an input unit 906, an output unit 907, a storage unit 908, and a communication unit 909. The input unit 906 can be any type of device that can input information to the electronic device 900. The input unit 906 can receive input digital or character information, and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone and / or a remote control. The output unit 907 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator and / or a printer. The storage unit 908 can include but is not limited to a magnetic disk, an optical disk. The communication unit 909 allows the electronic device 900 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as Bluetooth TM devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0129] The computing unit 901 can be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 901 performs the various methods and processes described above, such as the GPU-based matrix calculation method. For example, in some embodiments, the GPU-based matrix calculation method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 908. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 900 via the ROM 902 and / or the communication unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the GPU-based matrix calculation method described above can be performed. Alternatively, in other embodiments, the computing unit 901 can be configured to perform the GPU-based matrix calculation method by any other appropriate means (e.g., by means of firmware).

[0130] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0132] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0133] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0134] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0135] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0136] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0137] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is only limited by the claims after authorization and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. In addition, the steps may be performed in an order different from that described in this disclosure. Further, the various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after this disclosure.

Claims

1. A method for combined lateral and longitudinal control of an autonomous vehicle, comprising: Obtaining a lateral and longitudinal coupling state prediction model and a desired trajectory of the autonomous driving vehicle, wherein the lateral and longitudinal coupling state prediction model is used to track the trajectory of the autonomous driving vehicle; Obtaining a current state quantity and a current control quantity of the autonomous driving vehicle, wherein the current state quantity indicates a motion state of the autonomous driving vehicle at a current moment, and the current control quantity indicates a control being performed on the autonomous driving vehicle at the current moment; Based on the current state quantity, the current control quantity, the lateral and longitudinal coupling state prediction model, and the desired trajectory, a model predictive control algorithm is used to solve the target control quantity for trajectory tracking, wherein the model predictive control algorithm includes quadratic programming, and the cost function of the quadratic programming includes at least a target error of lateral and longitudinal decoupling, the target error being obtained by performing lateral and longitudinal decoupling on the error between a first predicted state quantity output by the lateral and longitudinal coupling state prediction model and a corresponding first desired state quantity, the first desired state quantity being associated with the desired trajectory; and The autonomous driving vehicle is controlled to perform the trajectory tracking based on the target control amount.

2. The method according to claim 1, wherein The target errors include a lateral position error, a longitudinal position error, a lateral velocity error, and a longitudinal velocity error.

3. The method according to claim 1 or 2, wherein: The method of solving the target control variable of the trajectory tracking using a model predictive control algorithm based on the current state variable, the current control variable, the lateral and longitudinal coupling state prediction model, and the desired trajectory includes: Based on the current state variable and the current control variable, determining a prediction variable sequence for tracking the desired trajectory using the lateral and longitudinal coupling state prediction model, wherein each prediction variable in the prediction variable sequence includes a second predicted state variable indicating a motion state of the autonomous driving vehicle at a corresponding moment, and each prediction variable also includes a predicted control variable indicating control performed on the autonomous driving vehicle at the corresponding moment; For each of the predicted quantities, determining, based on the expected trajectory, an initial error between a second predicted state quantity of the predicted quantity and a second expected state quantity corresponding to the predicted quantity; Performing the horizontal and vertical decoupling on the initial error to obtain the target error; Using the target error as at least part of the cost function to perform the quadratic programming; and Optimizing the predicted control amount of the previous predicted amount in the predicted amount sequence according to the solution result of the quadratic programming; and The target control amount is determined based on the optimized predicted amount sequence.

4. The method according to claim 3, wherein: The determining, based on the expected trajectory, an initial error between a second predicted state quantity of the predicted quantity and a second expected state quantity corresponding to the predicted quantity comprises: Determining a predicted trajectory point corresponding to the predicted quantity according to the second predicted state quantity; Determine a point in the expected trajectory whose distance from the predicted trajectory point is less than a target threshold as a preview point; and The second expected state quantity is determined according to the expected motion state of the autonomous driving vehicle when it reaches the preview point.

5. The method according to claim 3 or 4, wherein: The determining the target control amount based on the optimized predicted amount sequence includes: The predicted control variable included in the first predicted variable in the optimized predicted variable sequence is used as the target control variable.

6. The method according to any one of claims 3 to 5, wherein: The cost function is a nonlinear function, and using the target error as at least part of the cost function to perform the quadratic programming includes: Performing a first linearization process on the cost function to obtain a linear cost function; and The quadratic programming is performed based on the linear cost function.

7. The method according to any one of claims 1 to 6, wherein The obtaining of the lateral and longitudinal coupling state prediction model of the autonomous driving vehicle includes: Obtaining a dynamic model of the autonomous driving vehicle; and A second linearization process is performed on the dynamic model to obtain the transverse and longitudinal coupling state prediction model.

8. The method according to claim 7, wherein: The obtaining of the dynamic model of the autonomous driving vehicle includes: An initial vehicle model is determined based at least on the position coordinates of the autonomous driving vehicle in the world coordinate system, the lateral velocity along the lateral axis in the vehicle body coordinate system, the longitudinal velocity along the longitudinal axis in the vehicle body coordinate system, the yaw angle and the yaw angular velocity, wherein the lateral axis direction is the direction along the direction of the vehicle head, and the longitudinal axis direction is the direction parallel to the ground and perpendicular to the lateral axis direction.

9. A combined lateral and longitudinal control device for an autonomous vehicle, comprising: A first module is configured to obtain a lateral and longitudinal coupling state prediction model and a desired trajectory of the autonomous driving vehicle, wherein the lateral and longitudinal coupling state prediction model is used to track the trajectory of the autonomous driving vehicle; a second module configured to obtain a current state quantity and a current control quantity of the autonomous driving vehicle, wherein the current state quantity indicates a motion state of the autonomous driving vehicle at a current moment, and the current control quantity indicates a control being performed on the autonomous driving vehicle at a current moment; a third module configured to solve the target control variable for trajectory tracking using a model predictive control algorithm based on the current state variable, the current control variable, the lateral and longitudinal coupling state prediction model, and the desired trajectory, wherein the model predictive control algorithm includes quadratic programming, the cost function of the quadratic programming includes at least a target error of lateral and longitudinal decoupling, the target error being obtained by performing lateral and longitudinal decoupling on an error between a first predicted state variable output by the lateral and longitudinal coupling state prediction model and a corresponding first desired state variable, the first desired state variable being associated with the desired trajectory; and The fourth module is configured to control the autonomous driving vehicle to perform the trajectory tracking based on the target control amount.

10. An electronic device comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

11. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-8.

12. A computer program product comprising a computer program, wherein When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

13. An autonomous driving vehicle comprising the electronic device according to claim 10.

Images