Vehicle adaptive cruise control method and system based on stankberg game control

Abstract

The application belongs to the technical field of vehicle adaptive cruise control, and provides a vehicle adaptive cruise control method and system based on Stackelberg game control, which comprises the following steps: obtaining the relative distance and relative speed between the current vehicle and the preceding vehicle; constructing a vehicle longitudinal control model based on the obtained relative distance and relative speed; constructing an upper-layer decision control model containing a distance control target and a speed control target according to the constructed vehicle longitudinal control model; solving the constructed upper-layer decision control layer based on the Stackelberg game control theory to obtain a comprehensive expected acceleration; constructing and solving a lower-layer execution control model according to the obtained comprehensive expected acceleration to obtain the optimal throttle opening or brake pressure of the vehicle, and completing the vehicle adaptive cruise control based on the Stackelberg game control.

Description

Technical Field

[0001] This invention belongs to the field of vehicle adaptive cruise control technology, specifically relating to a vehicle adaptive cruise control method and system based on Stankberg game control. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Adaptive Cruise Control (ACC), a core function of Advanced Driver Assistance Systems (ADAS), enables vehicles to automatically follow the vehicle in front on structured roads such as highways. Its basic principle is to use onboard sensors (such as radar and cameras) to detect target vehicles ahead in real time, obtain the relative distance and speed between the two vehicles, and automatically control the vehicle's acceleration (throttle opening) and deceleration (brake pressure) to maintain a preset, safe following distance.

[0004] Traditional ACC control strategies mainly employ classical control theory (such as PID control) or modern control theory (such as model predictive control, sliding mode control, etc.). Their control objectives are usually single or a weighted sum of multiple objectives. The deviation between the expected distance calculated by the safe distance model (such as a model based on the vehicle's time distance) and the current actual distance, as well as the deviation between the expected relative speed (usually zero or the rate of change of the speed of the preceding vehicle) and the actual relative speed, are used as control inputs. The expected acceleration is calculated through a unified controller. In essence, the two control objectives of "maintaining a safe distance" and "maintaining speed matching (relative speed of zero)" are regarded as two sides of a coupled problem and are regulated by a single control law.

[0005] In real-world dynamic traffic environments, maintaining a safe following distance and maintaining speed matching often conflict. For example, if the vehicle in front suddenly decelerates, the vehicle behind needs to brake urgently to reduce the relative speed difference. However, excessively rapid deceleration may cause the actual following distance to decrease significantly in a short period, even falling below the safe threshold. Conversely, drastic braking to quickly increase the following distance can cause severe discomfort for the occupants. When the vehicle in front accelerates or the vehicle behind needs to merge into the traffic flow, rapid acceleration is required to reduce the following distance, which may lead to excessive relative speed, approaching the safe boundary. Traditional methods, when dealing with such multi-objective conflicts, often use fixed weights for trade-offs, making it difficult to achieve dynamic and optimal coordination based on real-time conditions. This can result in control responses that are either too aggressive (sacrificing comfort) or too conservative (sacrificing following efficiency and becoming vulnerable to being cut off), and their robustness and adaptability in complex scenarios need improvement.

[0006] The Standberg game is a game model describing the interactive behavior among decision-makers in a hierarchical structure. One decision-maker (leader) acts first, and another decision-maker (follower) reacts after observing the leader's actions. The equilibrium solution of the game (Standberg equilibrium) is the set of optimal responses for both parties in their respective policy spaces. Introducing the Standberg game into ACC control allows us to view "distance control" and "velocity control" as two "virtual decision-makers" with different priorities or objectives. By designing a suitable game framework, the two controllers can make decisions that conform to global optimality (such as a combination of safety, comfort, and efficiency) when there is conflict, rather than a simple weighted average; this provides a new approach to solving the multi-objective dynamic coordination problem in ACC. Summary of the Invention

[0007] To address the aforementioned issues, this invention proposes a vehicle adaptive cruise control method and system based on Stankberg game theory. By employing a hierarchical control architecture and Stankberg game theory, it resolves the conflict and difficulty in coordinating relative distance control and relative speed control during dynamic following of other vehicles in the adaptive cruise control process. This improves vehicle safety and comfort and effectively enhances road traffic efficiency.

[0008] According to some embodiments, the first aspect of the present invention provides a vehicle adaptive cruise control method based on Stankberg game control, employing the following technical solution: A vehicle adaptive cruise control method based on Stankberg game control includes: Get the relative distance and relative speed between the current vehicle and the vehicle in front; Based on the obtained relative distance and relative speed, a longitudinal control model for the vehicle is constructed; Based on the constructed vehicle longitudinal control model, a higher-level decision control model containing distance control objectives and speed control objectives is constructed. The upper-level decision control layer constructed based on the control theory of Stankberg game is used to obtain the comprehensive expected acceleration; Based on the obtained comprehensive expected acceleration, a lower-level execution control model is constructed and solved to obtain the optimal throttle opening or braking pressure of the vehicle, thus completing the vehicle adaptive cruise control based on Stankberg game control.

[0009] As a further technical limitation, after obtaining the relative distance and relative speed between the current vehicle and the vehicle in front, a time-varying target distance model is constructed based on the time-varying characteristics of the speed of the vehicle in front. The constructed time-varying target distance model is as follows: ;in, For the target vehicle distance, Vehicle speed, For the delay duration of the vehicle braking system, This refers to the change in distance due to relative vehicle speed. Relative vehicle speed For safe parking distance.

[0010] Furthermore, the constructed vehicle longitudinal control model is as follows: ;in, This represents the vehicle's optimal acceleration.

[0011] As a further technical limitation, the process of constructing the upper-level decision control model containing distance control targets and velocity control targets is as follows: Based on the vehicle longitudinal control model, a first control input related to distance error and a second control input related to speed error are defined, and a discretized vehicle system state equation containing the first control input and the second control input is constructed. The discretized vehicle system is augmented based on road preview information to obtain an augmented system state equation that includes the current state and preview state of the vehicle. Based on the augmented system state equation, quadratic performance index functions are constructed for the first control input and the second control input, respectively. The first performance index function focuses on minimizing the relative distance tracking error, and the second performance index function focuses on minimizing the relative velocity tracking error. By treating the first and second control inputs as the leader and follower in the Standberg game, respectively, and solving the coupled optimization problem, the linear state feedback gain matrix characterizing the Standberg equilibrium solution is obtained. The comprehensive expected acceleration is calculated based on the augmented system state at the current moment and the feedback gain matrix.

[0012] As a further technical limitation, the lower-level execution control model adopts a vehicle inverse dynamics model that includes the inverse model of the drive module and the inverse model of the braking module; when the comprehensive expected acceleration is greater than or equal to the drive switching threshold, the drive mode is entered, that is, according to the vehicle longitudinal dynamics equation, the required drive force is calculated based on the comprehensive expected acceleration, and combined with the transmission system parameters, the required engine torque is deduced from the required drive force; the target throttle opening is calculated based on the current engine speed and the required engine torque.

[0013] Furthermore, when the comprehensive expected acceleration is less than or equal to the braking switching threshold, the braking mode is entered. That is, the required braking force is calculated based on the vehicle's longitudinal dynamics equation and the comprehensive expected acceleration; the required braking force is distributed to each wheel according to the braking system parameters; and the target braking pressure of each wheel is obtained by querying the pre-calibrated braking force-pressure relationship table based on the distributed braking force of each wheel.

[0014] According to some embodiments, a second aspect of the present invention provides a vehicle adaptive cruise control system based on Stankberg game control, employing the following technical solution: A vehicle adaptive cruise control system based on Stankberg game control includes: The acquisition module is configured to acquire the relative distance and relative speed between the current vehicle and the vehicle in front; The module is configured to construct a vehicle longitudinal control model based on the acquired relative distance and relative speed; based on the constructed vehicle longitudinal control model, a higher-level decision control model containing distance control objectives and speed control objectives is constructed; the constructed higher-level decision control model is solved based on Stankberg game control theory to obtain the comprehensive expected acceleration; The control module is configured to construct and solve the lower-level execution control model based on the obtained comprehensive expected acceleration, obtain the optimal throttle opening or braking pressure of the vehicle, and complete the vehicle adaptive cruise control based on Stankberg game control.

[0015] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium, employing the following technical solution: A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the vehicle adaptive cruise control method based on Stankberg game control as described in the first aspect of the present invention.

[0016] According to some embodiments, the fourth aspect of the present invention provides an electronic device, which adopts the following technical solution: An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps in the vehicle adaptive cruise control method based on Steinkeberg game control as described in the first aspect of the present invention.

[0017] According to some embodiments, the fifth aspect of the present invention provides a computer program product, which adopts the following technical solution: A computer program product includes software code, wherein the program in the software code performs the steps of the vehicle adaptive cruise control method based on Stankberg game control as described in the first aspect of the present invention.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention introduces the Stankberg game framework to model the distance and speed control objectives as two decision-makers with a master-slave relationship, providing a dynamic and optimal coordination mechanism from a mechanistic perspective. When faced with conflicting conditions (such as emergency braking), it can automatically weigh and generate a control command that conforms to the global optimum (rather than a local weighted compromise), fundamentally improving the system's decision-making intelligence in complex dynamic scenarios.

[0019] This invention solves the problem of conflict and difficulty in coordinating and optimizing relative distance control and relative speed control during dynamic following in vehicle adaptive cruise control by using a hierarchical control architecture and Stankberg game theory, thereby improving vehicle safety and comfort and effectively enhancing road traffic efficiency. Attached Figure Description

[0020] The accompanying drawings, which form part of this embodiment, are used to provide a further understanding of this embodiment. The illustrative embodiments and their descriptions are used to explain this embodiment and do not constitute an improper limitation of this embodiment.

[0021] Figure 1 This is a flowchart of the vehicle adaptive cruise control method based on Stankberg game control in Embodiment 1 of the present invention; Figure 2 This is a MAP diagram of engines with different torques in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the reverse engine MAP in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of the Simulink throttle opening lookup table design in Embodiment 1 of the present invention; Figure 5 This is a schematic diagram of the Simulink braking system lookup table design in Embodiment 1 of the present invention; Figure 6 This is a schematic diagram of the Trucksim and MATLAB / Simulink joint simulation verification experiment in Embodiment 1 of the present invention; Figure 7 This is a schematic diagram of the relative vehicle distance curve in Embodiment 1 of the present invention; Figure 8 This is a schematic diagram of the relative vehicle speed curve in Embodiment 1 of the present invention; Figure 9 This is a schematic diagram of the throttle opening curve in Embodiment 1 of the present invention; Figure 10 This is a schematic diagram of the braking pressure curve in Embodiment 1 of the present invention; Figure 11 This is a structural block diagram of the vehicle adaptive cruise control system based on Stankberg game control in Embodiment 2 of the present invention. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0024] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0025] In this invention, terms such as "upper," "lower," "left," "right," "front," "back," "vertical," "horizontal," "side," and "bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used only to facilitate the description of the structural relationships of the various components or elements of this invention and do not specifically refer to any component or element in this invention. They should not be construed as limiting the invention.

[0026] In this invention, terms such as "fixed connection," "connected," and "linked" should be interpreted broadly, indicating a fixed connection, an integral connection, or a detachable connection; a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can determine the specific meaning of these terms in this invention based on the specific circumstances, and they should not be construed as limitations on the invention.

[0027] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0028] Example 1 Embodiment 1 of this invention introduces a vehicle adaptive cruise control method based on Stankberg game control.

[0029] like Figure 1 The vehicle adaptive cruise control method shown includes: Get the relative distance and relative speed between the current vehicle and the vehicle in front; Based on the obtained relative distance and relative speed, a longitudinal control model for the vehicle is constructed; Based on the constructed vehicle longitudinal control model, a higher-level decision control model containing distance control objectives and speed control objectives is constructed. The upper-level decision control layer constructed based on the control theory of Stankberg game is used to obtain the comprehensive expected acceleration; Based on the obtained comprehensive expected acceleration, a lower-level execution control model is constructed and solved to obtain the optimal throttle opening or braking pressure of the vehicle, thus completing the vehicle adaptive cruise control based on Stankberg game control.

[0030] This embodiment employs a layered control design in the vehicle adaptive cruise control process. The upper layer is the acceleration decision control layer, which acquires state variables such as the relative distance and speed between the current vehicle and the vehicle in front, as well as the vehicle's own speed, based on radar and camera perception. A linear quadratic regulator (LQR) is used to describe the relationship between the acquired state variables, and different performance index functions are constructed for each. The optimal control solution is then obtained by combining Stackelberg game control theory. The lower layer is the execution control layer, which determines the vehicle's driving or braking mode based on the optimal acceleration obtained from the upper layer. The optimal throttle opening or braking pressure is obtained by constructing and solving the inverse dynamics model, thus completing the vehicle adaptive cruise control based on Stackelberg game control.

[0031] The target distance determines the relative distance between the vehicle and the vehicle in front under the control of the ACC system. If the target distance is too small, it will cause dangerous situations due to the difficulty in effectively performing emergency braking. If the target distance is too large, it will be easy for other vehicles to cut in, thus reducing the following efficiency.

[0032] Due to the time-varying characteristics of the vehicle speed in front, this embodiment establishes a time-varying target distance model as shown in formula (1), that is... (1) in, For the target vehicle distance, Vehicle speed, For the delay duration of the vehicle braking system, This refers to the change in distance due to relative vehicle speed. Relative vehicle speed For safe parking distance.

[0033] A longitudinal control model for intelligent connected vehicles is constructed based on the kinematic relationship between acceleration and relative distance. (2) The established longitudinal model of the vehicle is described in the form of state equations, as shown in formula (3): (3) in, u 1 represents the acceleration generated by distance control. u 2 represents the acceleration generated by speed control. To represent the state variables of a continuous system, the coefficient matrices of the relative vehicle distance, relative vehicle speed, and lateral state equations are as follows:

[0034] Discretize the vehicle longitudinal model state equation (3) to obtain the discrete vehicle system as shown in equation (4), i.e.: (4) in, , and These represent the discrete states at the current and next time steps, respectively. , , The corresponding continuous time matrices Ac and Ac are respectively used. , It is obtained by the discrete bilinear transformation.

[0035] A road model is constructed based on road information, and an ACC (Adaptive Cruise Control) preview augmentation model is further constructed by combining the vehicle model. Preview information can be incorporated into the discrete vehicle dynamics equations, and the vehicle's ( N p +1) Speed ​​or distance pre-aiming points r s or r v express ， Generated through a shift register, i.e.: (5) in,

[0036]

[0037] in, Including distance aiming and speed aiming, , For the first The road information matrix of the steps For shift register matrix, This is the target information matrix that will be updated at the current moment; By augmenting the ACC system of shared vehicles through a preview dynamic process, a multi-target path tracking augmentation system for intelligent connected vehicles can be obtained: (6) in: (7) in, This is the farthest pre-aiming value in the pre-aiming area of ​​the vehicle's ACC control system. To simplify calculations, the information of the farthest pre-aiming point can be omitted. Therefore, formula (6) can be further simplified to: (8) in, For vehicle-aiming state variables, ; Let be the system state variable at time k+1, i.e., the ______ k The state variables of vehicle, road, and stability target prediction information at time +1. ; , Control inputs and The matrix coefficients.

[0038] This embodiment is based on the linear quadratic optimality theory, and the cost functions are constructed respectively, specifically: Selecting weighting terms, the prediction and control time domain is designed as follows: N u The cost function for the multi-objective ACC control problem with step size is: (9) in: (10) (11) in, , These are the weighted matrices for the relative distance and relative speed errors of the ACC control system, respectively. , The first The weighted matrix of the relative distance and relative speed control system performance index functions of the ACC control system at any given time, and , , , These represent the state weighting matrices for the relative distance control system and the relative speed control system, respectively. and The relative distance and relative speed control system of the ACC control system are respectively the self-input weighting coefficients.

[0039] The optimal control solution must be in a form related to the vehicle-preview state. Assume that the optimal Stankberg control solution is in the form of Equation (12).

[0040] (12) The following derivation is based on game theory: Make the two ACC control systems in k The optimal performance index at time +1 is in the form of: (13) Substituting formulas (8) and (12) into formula (13), we get: (14) Using Stankberg game control theory, derive the optimal control strategy: (15) in:

[0041] Substituting formula (12) into formula (8), we get... k Time and k+ The recursive relationship between time points 1 yields formula (16).

[0042] (16) in: (17) Further derivation of formula (15) yields: (18) By combining formulas (8), (12), and (13) and performing algebraic transformations, we can further obtain... P i Stackelberg With K i Stackelberg The recurrence relation is: (19) in: (20) Substituting formula (19) into formula (18) yields the optimal control solution.

[0043] In this embodiment, the lower-level controller constructs an inverse dynamics model of the drive module and the braking module. This model takes into account the effects of factors such as the backward drag of the engine and transmission system, road resistance, and air resistance, and constructs a dynamics model that conforms to the actual environment.

[0044] When the vehicle is driven, the optimal acceleration is calculated by the upper-level controller. a des Perform a table lookup, Figure 2 Engine model converted into Figure 3 The inverse engine model obtains the corresponding throttle opening through the map data of the inverse engine model.

[0045] The formula for automobile driving dynamics is: (twenty one) in, ; Where Ft is the driving force; Ttq This refers to engine torque; ig This refers to the gear ratio of the transmission. i0 The main reducer transmission ratio; η t For the mechanical efficiency of the transmission system; nt This refers to the turbine speed of the hydraulic torque converter; n For engine speed; τ( nt / n ) is the torque characteristic function of the hydraulic torque converter; r F is the rolling radius of the wheel; f For rolling resistance; f This is the rolling resistance coefficient; m For car quality; F i For slope resistance; i Road slope; g F is the acceleration due to gravity. w For air resistance; A The frontal area of ​​the vehicle; C D This refers to the air drag coefficient; ρ air density; u r F represents the vehicle speed (m / s). j To increase resistance, I This is the rotational mass conversion factor; a des The desired acceleration.

[0046] The relationship between speed, torque, and power is as follows: .pass Figure 4 The lookup table method can be used to obtain the throttle opening corresponding to torque and speed.

[0047] When the ACC system is in braking mode, the optimal control solution is calculated by the upper-level controller. a des Solve for the corresponding braking force Fb, and then obtain the corresponding braking pressure using the inverse dynamics model of the braking system. p The mechanical equilibrium formula for the braking system is: The relationship between a vehicle's braking force and braking pressure can be obtained through... Figure 5 It is obtained by looking up a table.

[0048] To verify the proposed control scheme, this implementation example... Figure 6 The Trucksim and MATLAB / Simulink co-simulation experiment was constructed as shown to verify the results, and the results are as follows: Figure 7 , Figure 8 , Figure 9 and Figure 10 The verification results show that the tracking distance error is small and the vehicle speed adjustment is rapid. The control scheme proposed in this invention, based on the throttle opening and braking pressure, shows that the braking / throttle control is relatively gentle, and the vehicle is more comfortable during following.

[0049] This embodiment introduces the Stankberg game framework, modeling the distance and speed control objectives as two decision-makers with a master-slave relationship, providing a dynamic and optimal coordination mechanism from a mechanistic perspective. When facing conflicting situations (such as emergency braking), it can automatically weigh and generate a control command that conforms to the global optimum (rather than a local weighted compromise), fundamentally improving the system's decision-making intelligence in complex dynamic scenarios.

[0050] Example 2 Embodiment 2 of the present invention introduces a vehicle adaptive cruise control system based on Stankberg game control.

[0051] like Figure 11 The vehicle adaptive cruise control system shown includes: The acquisition module is configured to acquire the relative distance and relative speed between the current vehicle and the vehicle in front; The module is configured to construct a vehicle longitudinal control model based on the acquired relative distance and relative speed; based on the constructed vehicle longitudinal control model, a higher-level decision control model containing distance control objectives and speed control objectives is constructed; the constructed higher-level decision control model is solved based on Stankberg game control theory to obtain the comprehensive expected acceleration; The control module is configured to construct and solve the lower-level execution control model based on the obtained comprehensive expected acceleration, obtain the optimal throttle opening or braking pressure of the vehicle, and complete the vehicle adaptive cruise control based on Stankberg game control.

[0052] The detailed steps are the same as those of the vehicle adaptive cruise control method based on Steinkeberg game control provided in Example 1, and will not be repeated here.

[0053] Example 3 Embodiment 3 of the present invention provides a computer-readable storage medium.

[0054] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the vehicle adaptive cruise control method based on Stankberg game control as described in Embodiment 1 of the present invention.

[0055] The detailed steps are the same as those of the vehicle adaptive cruise control method based on Steinkeberg game control provided in Example 1, and will not be repeated here.

[0056] Example 4 Embodiment 4 of the present invention provides an electronic device.

[0057] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps in the vehicle adaptive cruise control method based on Steinkeberg game control as described in Embodiment 1 of the present invention.

[0058] The detailed steps are the same as those of the vehicle adaptive cruise control method based on Steinkeberg game control provided in Example 1, and will not be repeated here.

[0059] Example 5 Embodiment 5 of the present invention provides a computer program product.

[0060] A computer program product includes software code, wherein the program in the software code performs the steps of the vehicle adaptive cruise control method based on Steinkeberg game control as described in Embodiment 1 of the present invention.

[0061] The detailed steps are the same as those of the vehicle adaptive cruise control method based on Steinkeberg game control provided in Example 1, and will not be repeated here.

[0062] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented 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. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0063] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0064] 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.

[0065] 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.

[0066] Although preferred embodiments of the invention 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 both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0067] 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.

[0068] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A vehicle adaptive cruise control method based on Stankberg game control, characterized in that, include: Get the relative distance and relative speed between the current vehicle and the vehicle in front; Based on the obtained relative distance and relative speed, a longitudinal control model for the vehicle is constructed; Based on the constructed vehicle longitudinal control model, a higher-level decision control model containing distance control objectives and speed control objectives is constructed. The upper-level decision control layer constructed based on the control theory of Stankberg game is used to obtain the comprehensive expected acceleration; Based on the obtained comprehensive expected acceleration, a lower-level execution control model is constructed and solved to obtain the vehicle's optimal throttle opening or braking pressure, thus completing the vehicle adaptive cruise control based on Stankberg game control. The process of constructing the upper-level decision control model containing distance control targets and velocity control targets is as follows: Based on the vehicle longitudinal control model, a first control input related to distance error and a second control input related to speed error are defined, and a discretized vehicle system state equation containing the first control input and the second control input is constructed. The discretized vehicle system is augmented based on road preview information to obtain an augmented system state equation that includes the current state and preview state of the vehicle. Based on the augmented system state equation, quadratic performance index functions are constructed for the first control input and the second control input, respectively. The first performance index function focuses on minimizing the relative distance tracking error, and the second performance index function focuses on minimizing the relative velocity tracking error. By treating the first and second control inputs as the leader and follower in the Standberg game, respectively, and solving the coupled optimization problem, the linear state feedback gain matrix characterizing the Standberg equilibrium solution is obtained. The comprehensive expected acceleration is calculated based on the augmented system state at the current moment and the feedback gain matrix.

2. The vehicle adaptive cruise control method based on Stankberg game control as described in claim 1, characterized in that, After obtaining the relative distance and relative speed between the current vehicle and the vehicle in front, a time-varying target distance model is constructed based on the time-varying characteristics of the vehicle in front's speed. The constructed time-varying target distance model is as follows: ;in, For the target vehicle distance, Vehicle speed, For the delay duration of the vehicle braking system, This refers to the change in distance due to relative vehicle speed. Relative vehicle speed For safe parking distance.

3. The vehicle adaptive cruise control method based on Stankberg game control as described in claim 2, characterized in that, The constructed vehicle longitudinal control model is ;in, This is the optimal acceleration for the vehicle.

4. The vehicle adaptive cruise control method based on Stankberg game control as described in claim 1, characterized in that, The lower-level execution control model adopts a vehicle inverse dynamics model that includes the inverse model of the drive module and the inverse model of the braking module. When the comprehensive expected acceleration is greater than or equal to the drive switching threshold, the drive mode is entered. That is, according to the vehicle longitudinal dynamics equation, the required drive force is calculated based on the comprehensive expected acceleration. Combined with the transmission system parameters, the required engine torque is deduced from the required drive force. The target throttle opening is calculated based on the current engine speed and the required engine torque.

5. The vehicle adaptive cruise control method based on Stankberg game control as described in claim 4, characterized in that, When the combined expected acceleration is less than or equal to the braking switching threshold, the braking mode is entered. That is, the required braking force is calculated based on the vehicle's longitudinal dynamics equation and the combined expected acceleration; the required braking force is distributed to each wheel according to the braking system parameters; and the target braking pressure of each wheel is obtained by querying the pre-calibrated braking force-pressure relationship table based on the distributed braking force of each wheel.

6. A vehicle adaptive cruise control system based on Stankberg game control using the method described in claim 1, characterized in that, include: The acquisition module is configured to acquire the relative distance and relative speed between the current vehicle and the vehicle in front; The module is configured to construct a vehicle longitudinal control model based on the acquired relative distance and relative speed; based on the constructed vehicle longitudinal control model, a higher-level decision control model containing distance control objectives and speed control objectives is constructed; the constructed higher-level decision control model is solved based on Stankberg game control theory to obtain the comprehensive expected acceleration; The control module is configured to construct and solve the lower-level execution control model based on the obtained comprehensive expected acceleration, obtain the optimal throttle opening or braking pressure of the vehicle, and complete the vehicle adaptive cruise control based on Stankberg game control.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the vehicle adaptive cruise control method based on Steinkeberg game control as described in any one of claims 1-5.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps of the vehicle adaptive cruise control method based on Steinkberg game control as described in any one of claims 1-5.

9. A computer program product, comprising software code, characterized in that, The program in the software code performs the steps of the vehicle adaptive cruise control method based on Steinkeberg game control as described in any one of claims 1-5.

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