Combined optimization method and system for vehicle formation stability and communication reliability

Abstract

The invention discloses a combined optimization method and system for vehicle formation stability and communication reliability. The method comprises the following steps: firstly, analyzing a coupling relationship between an air unmanned aerial vehicle and a ground vehicle queue in a motion control and communication process, and constructing a formation control and communication reliability dual-target joint optimization model in an air-ground collaborative network vehicle environment; and then, designing an auxiliary function to deduce a closed upper bound expression of a communication target, and converting the double-target model into an equivalent single-target optimization problem by adopting a constraint optimization strategy based on the expression, thereby ensuring the reliability of air-ground communication while realizing stable formation of a motorcade. According to the method, the coupling characteristic between the formation control system and the communication system is systematically considered, a joint optimization scheme with universality and practical value is provided, and the overall performance of the air-ground collaborative formation system can be effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent transportation, and particularly relates to a method and system for jointly optimizing the stability of vehicle formations and the reliability of communication. Background Art

[0002] With the rapid development of the economy, the demand for road transportation has been continuously rising. The intelligent transportation system has become an important direction for improving traffic safety and transportation efficiency. In this context, the communication and cooperation technology between vehicles and low-altitude unmanned aerial vehicles (UAVs) has gradually attracted attention. By constructing an air-ground integrated communication network, low-altitude UAVs can obtain real-time environmental perception and operation status information of vehicle terminals during the process of vehicle formation driving, and forward it to the edge or cloud platform to achieve efficient perception, monitoring, and scheduling of vehicle formations. Compared with traditional ground communication methods, the communication between vehicles and UAVs has a wider field of view, stronger dynamic perception ability, and lower latency, providing more comprehensive and timely data support for vehicle formation control, and is the key technical foundation for promoting the intelligent and collaborative development of intelligent transportation systems.

[0003] However, in the high-mobility environment of formation vehicles, the existing communication technology between vehicles and low-altitude UAVs still faces many challenges. On the one hand, the existing formation control technology is difficult to effectively ensure the coupling reliability between communication and control. The design of the formation control system and the air-ground communication system is usually based on different objectives, and there may even be resource competition. In high-dynamic scenarios, frequent information sampling helps to improve control performance, but it consumes a large amount of air-ground communication link resources, leading to communication link congestion and channel contention, thus causing problems such as increased transmission latency and data packet loss, which will have an adverse impact on the stability and control accuracy of the formation system. On the other hand, the traditional communication architecture is difficult to effectively cope with the complexity brought about by the growth of formation scale. The optimization objectives of formation control usually include meeting the transient and asymptotic system dynamic requirements, such as vehicle mobility and system stability. However, the existing air-ground communication optimization schemes mostly focus on the safety and communication quality of individual vehicles, and it is difficult to take into account the collaborative performance requirements of the entire formation. Therefore, the traditional air-ground communication strategy may not be able to provide sufficient efficient and reliable communication support in the face of a high-mobility, multi-vehicle collaborative formation environment. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes a method and system for jointly optimizing the stability of vehicle formations and the reliability of communication to solve the problems existing in the above prior art.

[0005] To achieve the above object, in the first aspect, the present invention provides a method for jointly optimizing the stability of vehicle formations and the reliability of communication, including:

[0006] Based on discrete time, establish the kinematic equation of formation vehicles;

[0007] Based on the kinematic equation and combined with the communication link between the formation vehicles and the low-altitude drones, a communication reliability function is established.

[0008] Based on the kinematic equation, a model predictive control function for the entire formation vehicles is established.

[0009] Based on the communication reliability function and the model predictive control function, a two-objective joint optimization model for the communication between the formation vehicle control and the drones is established.

[0010] Based on the two-objective joint optimization model, a closed-loop expression for the upper bound of the communication reliability objective is derived.

[0011] The two-objective joint optimization model is converted into a single-objective model through a constrained optimization method.

[0012] Based on the single-objective model, an MPC rolling optimization strategy is executed to update the state information of the formation vehicles.

[0013] Based on the updated state information of the formation vehicles, formation control signals and data transmission amounts are output.

[0014] Preferably, the process of establishing the communication reliability function includes:

[0015] Based on the kinematic equation and combined with the communication link between the formation vehicles and the drones, the relative distance of the communication link is calculated.

[0016] Based on the relative distance, the channel capacity between the formation vehicles and the drones is calculated.

[0017] Based on the channel capacity, the first success probability of data transmission between the formation vehicles and the drones at discrete time k is calculated.

[0018] Based on the success probability, the second success probability of data transmission for all formation vehicles and the drones is calculated; the second success probability is the communication reliability function between the entire formation vehicles and the drones.

[0019] Preferably, the process of establishing the model predictive control function for the entire formation vehicles includes:

[0020] Based on the kinematic equation, a state error function is defined.

[0021] The constraint conditions for the entire formation vehicles are set.

[0022] Based on the state error function and the constraint conditions, a state equation for the entire formation vehicles is established.

[0023] Based on the state equation, a model predictive control function for the entire formation vehicles is established.

[0024] Preferably, the process of deriving the closed-loop expression of the upper bound of the communication reliability target includes:

[0025] Construct an auxiliary function based on the dual-objective joint optimization model;

[0026] Based on the auxiliary function, use the multiplier method to derive the closed-loop expression of the upper bound of the reliability target.

[0027] Preferably, the dual-objective joint optimization model is:

[0028] M1: max u,a : H(u,a)

[0029] min a : J(x,a)

[0030]

[0031] where H(u,a) is the communication reliability target of the UAV - queue, J(x,a) is the vehicle formation control target, θ out,i is the total amount of data transmitted by each vehicle, a i (k) is the acceleration of vehicle i at time k, u i (k) is the data transmission amount between the UAV and vehicle i at time k.

[0032] The communication sub-model M2 of the auxiliary function is:

[0033] M2: max u : H(u,a)

[0034]

[0035] where H(u,α) is the communication reliability target of the UAV - queue, θ out,i is the total amount of data transmitted by each vehicle, u i (k) is the data transmission amount between the UAV and vehicle i at time k.

[0036] Preferably, the closed-loop expression is:

[0037]

[0038] where H max (u * ,a) is the closed-loop expression of the upper bound of the communication reliability target.

[0039] Preferably, the single-objective model is:

[0040] M3: min u,a : J(u,x,a)

[0041]

[0042] Among them, ∈ is the target reduction rate. Based on the ∈-constrained optimization method, the communication-control dual-objective model is transformed into a single-objective optimization model.

[0043] In a second aspect, the present invention also provides a joint optimization system for vehicle formation stability and communication reliability, including:

[0044] An equation establishment module, configured to construct a kinematic model of formation vehicles based on discrete time;

[0045] A first function establishment module, configured to establish a communication reliability function based on the kinematic equation in combination with the communication link between formation vehicles and unmanned aerial vehicles;

[0046] A second function establishment module, configured to establish a model predictive control function for the entire formation vehicle based on the kinematic equation;

[0047] A dual-objective model establishment module, configured to establish a dual-objective joint optimization model for the communication between formation vehicle control and unmanned aerial vehicles based on the communication reliability function and the model predictive control function;

[0048] A closed-loop expression derivation module, configured to derive a closed-loop expression for the upper bound of the communication reliability target based on the dual-objective joint optimization model;

[0049] A single-objective model transformation module, configured to transform the dual-objective joint optimization model into a single-objective model through a constraint optimization method;

[0050] A state information update module, configured to execute the MPC rolling optimization strategy based on the single-objective model and update the state information of the formation vehicle;

[0051] An output module, configured to output a formation control signal and a data transmission volume based on the updated state information of the formation vehicle.

[0052] In a third aspect, the present invention also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0053] Compared with the prior art, the present invention has the following advantages and technical effects:

[0054] The present invention provides a joint optimization method for vehicle formation stability and communication reliability. First, based on discrete time, a kinematic equation of formation vehicles is established. Second, based on the kinematic equation, combined with the communication link between formation vehicles and unmanned aerial vehicles (UAVs), a communication reliability function is established. Then, based on the kinematic equation, a model predictive control function of the entire formation vehicles is established. Further, based on the communication reliability function and the model predictive control function, a two-objective joint optimization model for communication between formation vehicle control and UAVs is established. Next, based on the two-objective joint optimization model, a closed-loop expression of the upper bound of the communication reliability objective is derived. Furthermore, the two-objective joint optimization model is converted into a single-objective model through a constrained optimization method. Further, based on the single-objective model, an MPC rolling optimization strategy is executed to update the state information of formation vehicles. Finally, based on the updated state information of formation vehicles, formation control signals and data transmission amounts are output.

[0055] The present invention solves the coupling problem of vehicle mobility, formation control technology, and communication reliability optimization, makes full use of the vehicle formation control system and communication system, and ensures the stability of formation vehicles and the high reliability of UAV data transmission without affecting the formation of stable vehicle formations.

[0056] The present invention designs a comprehensive decision-making scheme for two-objective joint optimization of formation control and communication based on the model predictive control framework, which can synchronously execute vehicle formation control while multiple vehicles and UAVs perform data transmission. The present invention improves air-ground networked collaborative communication and control, enhances the road passing capacity, ensures the real-time performance of intelligent vehicle formation control and UAV data transmission, and promotes the further development of autonomous driving technology. Description of the Drawings

[0057] The drawings constituting a part of this application are used to provide a further understanding of this application. The schematic embodiments and descriptions of this application are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0058] Figure 1 It is a flowchart of the method according to an embodiment of the present invention. Detailed Embodiments

[0059] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the drawings and combine the embodiments to detail this application.

[0060] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0061] In the method of vehicle platoon control, the control strategies for vehicles generally can choose two ways: centralized control and distributed control. The centralized controller is generally deployed on the leading vehicle. Through the overall planning of the centralized controller, other following vehicles can achieve stable control of the vehicle platoon under the design of state constraints, control constraints, and control objectives. Under the distributed control strategy, a controller is deployed on each vehicle, and the design of the rear vehicle controller is based on the state of the front vehicle. Since the architecture design of the centralized controller is simple, the coupling degree between generality and application is low, resource scheduling is flexible, and it is easy to deploy, centralized control is usually adopted in vehicle platoon scenarios. However, in current vehicle platoon control, issues such as resource sharing and data transmission between vehicles and unmanned aerial vehicles (UAVs) are not considered, nor is the coupling relationship between vehicle mobility and the design and communication of the controller considered, and there is no specific optimization method for implementation.

[0062] The vehicle platoon control system includes two parts: a communication system and a platoon control system;

[0063] The first part, the communication system is used for information interaction between platooning vehicles and UAVs, including:

[0064] Perception module: Perceive the information around the platooning vehicles and upload it to the low-altitude UAV;

[0065] Communication module: Establish a communication link to ensure high-reliability data transmission;

[0066] The second part, the platoon control system is used to perceive the state information of the traveling vehicles, predict control signals, and execute them, including:

[0067] Positioning module: Used to obtain the position information of the platooning vehicles;

[0068] Distance detection module: Used to detect the distance difference and speed difference between platooning vehicles, and detect the lateral and longitudinal distances between platooning vehicles and UAVs;

[0069] Control module: Used to predict the control signals of platooning vehicles and execute control instructions.

[0070] Embodiment 1

[0071] In this embodiment, a joint optimization method for vehicle formation stability and communication reliability is designed. Aiming at the control and communication optimization problems of the formation, a dual-objective joint optimization model for vehicle formation control and communication in the air-ground collaborative networking scenario is established, which can ensure the stable formation of high-speed moving vehicles while achieving high-reliability data transmission. For vehicle formation control, the model predictive control (MPC) method is used to control the vehicle formation, terminal constraint conditions are designed to ensure the stability of the formation, and the stability objective of the formation control is designed. For the networking communication between the formation vehicles and the UAV, the channel model is used to characterize the success probability of data transmission between the entire formation system and the UAV, and the reliability objective of the communication is established based on this. Under the constraints of other conditions, a dual-objective joint optimization model is established. To solve this dual-objective joint optimization model, an auxiliary function is constructed, the closed-loop expression of the upper bound of the communication reliability objective is derived, and based on the constrained optimization method, the communication reliability objective is converted into a constraint, and the dual-objective model is converted into a single-objective model. The rolling optimization strategy is executed to obtain the formation control signal u and execute it, and the data transmission volume a is output and uploaded to the UAV.

[0072] This embodiment adopts a centralized control method, which includes 1 UAV and N formation vehicles, where N is a set fixed value, that is, the number of formation vehicles N = 4 - 8, and the index set of the number of formation vehicles is represented as The total duration K of real-time control and communication in this system is a set fixed value, that is, K = 25 - 40s, and the discrete time domain index set is represented as In addition, there is a low-altitude UAV in the scenario.

[0073] As Figure 1 shown, a joint optimization method for vehicle formation stability and communication reliability specifically includes:

[0074] Step 1: Based on discrete time, establish the kinematic equation of the formation vehicle;

[0075]

[0076] v i (k + 1) = v i (k) + τa i (k) (2)

[0077] where s i (k), v i (k) and a i (k) are respectively the longitudinal displacement, speed and acceleration of formation vehicle i at discrete time k, and τ is the duration. The control signal of the formation system is the acceleration of all formation vehicles, that is, a i (k) = [a i (0), a i (1), ai (2), …, a i (K - 1)].

[0078] Step 2: Based on the kinematic equation, combined with the communication link between the formation vehicle and the UAV, establish a communication reliability function;

[0079] Step 201: Based on Step 1, the formation vehicle i establishes a communication link with the UAV and calculates the relative distance of the communication link:

[0080]

[0081] where L V2I,i is the lateral distance between the formation vehicle i and the UAV, and is the longitudinal distance.

[0082] In this embodiment, the lateral distance L between the formation vehicle i and the UAV V2I,i is a set fixed value, L V2I,i = 250 - 300 m, and the longitudinal distance L0 is a set fixed value, L0 = 50 - 100 m.

[0083] Step 202: Based on the relative distance, calculate the channel capacity of the formation vehicle i and the UAV

[0084] C i (a i (k)):

[0085]

[0086] where B is the communication bandwidth, M is the number of vehicles accessing the UAV simultaneously with the formation vehicle, w i is the normalized power, and the function g(L i (a i (k))) depends on the V2I channel gain of the relative distance L i (a i (k)).

[0087] In this embodiment, the communication bandwidth B is a set fixed value, B = 10 Mbit. The number of vehicles M accessing the UAV simultaneously is a set fixed value, M = 150 - 2000 vehicles, and the normalized power w i is determined by the signal-to-noise ratio where S = 1 w,

[0088] Step 203: Based on the channel capacity, calculate the first success probability p i (u i (k), a i (k)) of the data transmission between the formation vehicle i and the UAV at the discrete time k is:

[0089]

[0090] where u i (k) is the data volume uploaded by formation vehicle i to the UAV at discrete time k, and satisfies the constraint Through optimization, the data volume u i (k) uploaded by formation vehicle i to the UAV in the entire time domain K can be obtained, and

[0091] In this embodiment, the total data volume θ out,i that formation vehicle i needs to upload to the UAV in all time domains out,i is a set fixed value, θ 5 = 10 6 bits.

[0092] Step 204, the success probability P i (u i , a i (k)) of the data transmission between formation vehicle i and the UAV in the entire time domain K is:

[0093]

[0094] Step 205, the second success probability H(u, a) of the data transmission between all formation vehicles N and the UAV is

[0095]

[0096] In this embodiment, the success probability H(u, a) is defined as the communication reliability function between the entire formation system and the UAV.

[0097] Step 3, based on the kinematic equation, establish the model predictive control function of the entire formation vehicle;

[0098] Step 301, based on Step 1, define the state error function:

[0099]

[0100] Step 302, set the constraint conditions of the entire formation vehicle, including acceleration constraint, speed error constraint and distance error constraint

[0101] a min ≤ a i (k) ≤ a max (10)

[0102]

[0103] where a min and a maxare the minimum and maximum values of the control signal, and are the minimum and maximum values of the speed error, and are the minimum and maximum values of the distance error, d i is the expected constant distance between workshops.

[0104] In this embodiment, the minimum value a of the control signal min and the maximum value a max , the minimum value of the speed error and the maximum value the minimum value of the distance error and the maximum value the expected constant distance d between workshops i are all set fixed values, a min = -2.5 m / s 2 , a max = 2.5 m / s 2 , d i = 10 m.

[0105] Step 303: Based on the state error function and the constraint conditions, establish the state equation of the entire formation vehicle. The state equation of vehicle i and vehicle i-1 at discrete time k is defined as:

[0106]

[0107] Among them, Then the state equation of the formation system is:

[0108] x i (k + 1) = Ax i (k) + Ba i-1 (k) + Da i (k) (14)

[0109] Among them

[0110] Step 304: Based on the state equation, establish the model predictive control function J(x, a) of the entire formation vehicle as:

[0111]

[0112] Step 4: Based on the communication reliability function and the model predictive control function, establish the communication double-objective joint optimization model M1 between the formation vehicle control and the UAV

[0113] M1: max u,a : H(u, a)

[0114] min a : J(x, a)

[0115]

[0116] Among them, H(u, a) is the communication reliability objective of the UAV - queue, J(x, a) is the vehicle formation control objective, and θ out,i is the total amount of data transmission for each vehicle. x i (k + 1) = Ax i (k) + Ba i-1 (k) + Da i (k) is the state update equation, x min ≤ x i (k) ≤ x max is the system state constraint, a min ≤ a i (k) ≤ a max is the acceleration boundary constraint, defines the total amount of data transmission θ for each vehicle out,i ; x i (K) = 0 is the terminal constraint, u i (k) ≥ 0 indicates that the optimal data transmission amount needs to be greater than or equal to 0.

[0117] Step 5: Based on the dual - objective joint optimization model, derive the closed - loop expression of the upper bound of the communication reliability objective;

[0118] Step 501: By observing the dual - objective joint optimization model established in Step 4, construct an auxiliary function, that is, the communication sub - model M2;

[0119] M2: max u : H(u, a)

[0120]

[0121] Step 502: Using the multiplier method, fuse the equality constraint and inequality constraint into the objective to derive the closed - loop expression of the upper bound of the reliability objective;

[0122]

[0123] Step 6: Convert the dual - objective joint optimization model into a single - objective model through the constraint optimization method;

[0124] M3: min u,a : J(u, x, a)

[0125]

[0126] Among them, ∈ is the objective reduction rate, which is a set fixed value, ∈ = 3×10-5 。

[0127] Step 7: Based on the single-objective model, execute the MPC rolling optimization strategy to update the formation vehicle state information;

[0128] Step 8: Based on the updated formation vehicle state information, output the formation control signal u and execute it, and output the data transmission volume a and upload it to the UAV.

[0129] Embodiment 2

[0130] Based on the same inventive concept, this embodiment also provides a joint optimization system for vehicle formation stability and communication reliability, including:

[0131] An equation establishment module for constructing a kinematic model of formation vehicles based on discrete time;

[0132] A first function establishment module for establishing a communication reliability function based on the kinematic equation and in combination with the communication link between the formation vehicle and the UAV;

[0133] A second function establishment module for establishing a model predictive control function for the entire formation vehicle based on the kinematic equation;

[0134] A dual-objective model establishment module for establishing a dual-objective joint optimization model for the control of the formation vehicle and the communication with the UAV based on the communication reliability function and the model predictive control function;

[0135] A closed-loop expression derivation module for deriving a closed-loop expression of the upper bound of the communication reliability objective based on the dual-objective joint optimization model;

[0136] A single-objective model conversion module for converting the dual-objective joint optimization model into a single-objective model by a constraint optimization method;

[0137] A state information update module for executing the MPC rolling optimization strategy based on the single-objective model to update the formation vehicle state information;

[0138] An output module for outputting a formation control signal and a data transmission volume based on the updated formation vehicle state information.

[0139] The joint optimization system for vehicle formation stability and communication reliability provided in this embodiment has all the advantages of the joint optimization method for vehicle formation stability and communication reliability provided in Embodiment 1.

[0140] Embodiment 3

[0141] This embodiment also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in Embodiment 1 are implemented.

[0142] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the technical field of the present application within the technical scope disclosed by the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A joint optimization method for vehicle formation stability and communication reliability, characterized in that, It includes the following steps: Based on discrete time, establish the kinematic equation of the formation vehicles; Based on the kinematic equation, combined with the communication link between the formation vehicles and the low-altitude drones, establish the communication reliability function; Based on the kinematic equation, establish the model predictive control function of the entire formation vehicles; Based on the communication reliability function and the model predictive control function, establish a two-objective joint optimization model for the communication between the formation vehicle control and the drones; Based on the two-objective joint optimization model, derive the closed-loop expression of the upper bound of the communication reliability objective; Convert the two-objective joint optimization model into a single-objective model through the constraint optimization method; Based on the single-objective model, execute the MPC rolling optimization strategy to update the state information of the formation vehicles; Based on the updated state information of the formation vehicles, output the formation control signal and the data transmission volume.

2. The method according to claim 1, characterized in that The process of establishing the communication reliability function includes: Based on the kinematic equation, combined with the communication link between the formation vehicles and the drones, calculate the relative distance of the communication link; Based on the relative distance, calculate the channel capacity between the formation vehicles and the drones; Based on the channel capacity, calculate the first success probability of data transmission between the formation vehicles and the drones at discrete time k; Based on the success probability, calculate the second success probability of data transmission between all formation vehicles and the drones; The second success probability is the communication reliability function between the entire formation vehicles and the drones.

3. The method according to claim 1, characterized in that The process of establishing the model predictive control function of the entire formation vehicles includes: Based on the kinematic equation, define the state error function; Set the constraint conditions of the entire formation vehicles; Based on the state error function and the constraint conditions, establish the state equation of the entire formation vehicles; Based on the state equation, establish the model predictive control function of the entire formation vehicles.

4. The method according to claim 1, characterized in that The process of deriving the closed-loop expression of the upper bound of the communication reliability objective includes: Based on the two-objective joint optimization model, construct an auxiliary function; Based on the auxiliary function, use the multiplier method to derive the closed-loop expression of the upper bound of the communication reliability objective.

5. The method according to claim 1, characterized in that The two-objective joint optimization model is: M1:max u,a :H(u,a) min a : J(x,a) Among them, H(u,a) is the communication reliability objective of the UAV - queue, J(x,a) is the vehicle formation control objective, θ out,i is the total amount of data transmitted by each vehicle, a i (k) is the acceleration of vehicle i at time k, u i (k) is the data transmission volume between the UAV and vehicle i at time k.

6. The method according to claim 4, characterized in that The auxiliary function is the communication sub-model M2: M2: max u : H(u,a) Among them, H(u,α) is the communication reliability target of the UAV - queue, and θ out,i is the total amount of data transmission for each vehicle, and u i (k) is the data transmission amount between the UAV and vehicle i at time k.

7. The method according to claim 1, characterized in that The closed-loop expression is: Among them, H max (u * , a) is the closed-loop expression of the upper bound of the communication reliability target.

8. The method according to claim 1, characterized in that The single-objective model is: M3:min u,a :J(u,x,a) where ∈ is the target reduction rate, and based on the ∈ constraint optimization method, the communication-control two-objective model is converted into a single-objective optimization model.

9. A joint optimization system for vehicle formation stability and communication reliability, characterized in that, It includes: An equation establishment module for constructing the kinematic model of the formation vehicles based on discrete time; A first function establishment module for establishing the communication reliability function based on the kinematic equation, combined with the communication link between the formation vehicles and the drones; A second function establishment module for establishing the model predictive control function of the entire formation vehicles based on the kinematic equation; A dual-objective model establishment module, which is used to establish a dual-objective joint optimization model for the communication between the formation vehicle control and the UAV based on the communication reliability function and the model predictive control function; A closed-loop expression derivation module, which is used to derive a closed-loop expression of the upper bound of the communication reliability objective based on the dual-objective joint optimization model; A single-objective model conversion module, which is used to convert the dual-objective joint optimization model into a single-objective model by a constraint optimization method; A state information update module, which is used to execute the MPC rolling optimization strategy based on the single-objective model and update the formation vehicle state information; An output module, which is used to output a formation control signal and a data transmission volume based on the updated formation vehicle state information.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-8.

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