A Joint Optimization Method and System for Vehicle Platoon Resource Allocation and Control

Abstract

The present invention discloses a joint optimization method for vehicle platoon resource allocation and control. The method includes: for each control cycle, obtaining the motion states of each member vehicle at the beginning of the control cycle; based on the motion states of all member vehicles, a pre-constructed vehicle network communication model, a vehicle dynamics model, and a model predictive control (MPC)-based platoon control model, establishing a joint optimization problem for sub-channel allocation and control input parameters of the vehicle platoon; solving the joint optimization problem in the first stage to obtain the sub-channel allocation results of each member vehicle; solving the joint optimization problem in the second stage to obtain the optimal control input parameter sequence for each member vehicle; using the solution results of the second stage to control the motion states of the corresponding member vehicles, and continuing to process the next control cycle when the stop condition is not satisfied. The present invention jointly optimizes the communication and control of the vehicle platoon considering the interference and uncertainty in vehicle dynamics, and the optimization results are closer to the actual situation.

Description

Technical Field

[0001] The present invention belongs to the field of communications, and particularly relates to a method and system for jointly optimizing vehicle platoon resource allocation and control. Background Art

[0002] With the development of the automotive industry and urbanization, more and more vehicles are traveling on highways connecting surrounding cities. It is estimated that there are currently more than 1 billion registered motor vehicles worldwide. Therefore, a series of key problems in modern transportation systems have become increasingly serious, such as traffic congestion, traffic accidents, energy waste, and pollution. To solve these problems, an effective method is to change the driving mode from traditional individual driving to platoon-based driving.

[0003] A vehicle platoon is a group of vehicles sharing a common movement pattern. In a vehicle platoon, each member vehicle follows the leader vehicle and maintains a target inter-vehicle distance from the preceding member vehicle. In a vehicle platoon, the inter-vehicle distance and speed of each member vehicle are adjusted by the controller of the leader vehicle, which relies on vehicle-to-vehicle (V2V) communication to regularly collect information about the motion states of the member vehicles. Obviously, a vehicle platoon is a complex network control system integrating technologies such as communication and control.

[0004] In the past few decades, many studies have focused separately on vehicle platoon communication or control. For communication protocol design, some advanced inter-vehicle communication protocols for propagating periodic beacon messages and event-driven safety messages have been proposed. For radio resource allocation, some sub-channel allocation schemes and power control mechanisms for vehicle platoons have been studied. On the other hand, in recent years, some advanced vehicle platoon control methods have been proposed, including cooperative adaptive cruise control (CACC), sliding mode control (SMC), and model predictive control (MPC). There are also a small number of studies that jointly optimize vehicle platoon communication and control, and design platoon-based V2V communication mechanisms according to the stability requirements of vehicle platoon control.

[0005] Existing research solutions that jointly optimize vehicle platoon communication and control focus on designing platoon-based V2V communication mechanisms according to the stability requirements of vehicle platoon control, but do not optimize the control performance of vehicle platoons, or even if they optimize the control performance of vehicle platoons, the optimization effect is too idealized relative to the actual situation of vehicles, resulting in insufficient accuracy. Summary of the Invention

[0006] To solve the problems existing in the prior art, embodiments of the present invention provide a method and system for jointly optimizing vehicle platoon resource allocation and control. The specific technical solutions are as follows:

[0007] In a first aspect, an embodiment of the present invention provides a joint optimization method for vehicle platoon resource allocation and control, which is applied to the controller of the leading vehicle in a vehicle platoon. The method includes:

[0008] For each control period, obtain the motion states of each member vehicle in the vehicle platoon at the start of the control period; based on the motion states of all member vehicles, as well as a pre-constructed vehicle network communication model, vehicle dynamics model, and MPC-based platoon control model, establish a joint optimization problem for sub-channel allocation and control input parameters of the vehicle platoon; wherein, the motion states include position and speed; the control input parameters characterize the acceleration of the member vehicle; the MPC-based platoon control model takes into account the actual motion states of the member vehicles.

[0009] Perform a first-stage solution to the joint optimization problem to obtain the first-stage solution result as the sub-channel allocation results of each member vehicle within the control period.

[0010] Based on the first-stage solution result, perform a second-stage solution to the joint optimization problem for the control input parameters to obtain the second-stage solution result including the optimal control input parameter sequence of each member vehicle within the control period; and use the second-stage solution result to control the motion states of the corresponding member vehicles; wherein, the optimal control input parameter sequence includes the optimal control input parameters for multiple future control periods starting from the current control period.

[0011] When the stop condition is not satisfied, for the next control period, return to execute the step of obtaining the motion states of each member vehicle in the vehicle platoon at the start of the control period.

[0012] In an embodiment of the present invention, the pre-constructed vehicle network communication model includes:

[0013] Excluding the leading vehicle in the vehicle platoon, the number of member vehicles is M; the vehicle spacing is constant and equal; within one control period, there are a total of B sub-channels for channel resources, and B ≤ M; in each control period, the member vehicles assigned sub-channels are used to send their motion states to the leading vehicle through V2V communication; the controller of the leading vehicle is used to perform joint optimization of sub-channel allocation and platoon control input parameters based on the periodically received motion states, and broadcast the optimization results to the member vehicles.

[0014] In an embodiment of the present invention, the construction process of the vehicle dynamics model includes:

[0015] For any member vehicle m, construct a discrete-time dynamics model as:

[0016]

[0017] Among them, x m (t) and v m (t) respectively represent the position and speed of the member vehicle m at time t; T represents the discrete time interval, which is the time length of a control period; u m (t) represents the control input parameter of the member vehicle m at time t;

[0018] The discrete-time dynamic model is simplified and rewritten in the form of the motion state as:

[0019]

[0020] Among them, y m (t) = [x m (t), v m (t)] T represents the motion state of the member vehicle m at time t; represents the vehicle dynamics function.

[0021] In an embodiment of the present invention, the pre-constructed MPC-based platoon control model includes:

[0022] Based on the MPC platoon control model, the following constraint is added: for a member vehicle m, if the member vehicle m is assigned a sub-channel at time t, the predicted motion state and the assumed motion state of the member vehicle m at time t are the actual motion states of the member vehicle m sent to the leader vehicle using the assigned sub-channel at time t; if the member vehicle m is not assigned a sub-channel at time t, the predicted motion state and the assumed motion state of the member vehicle m at time t are the predicted motion states of the member vehicle m in the previous control period; where, in the MPC-based platoon control model, for each time t, the future control input parameters are predicted for N control periods, and k represents the kth of the N control periods.

[0023] In an embodiment of the present invention, the expression of the joint optimization problem of vehicle platoon resource allocation and control includes:

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] Among them,

[0035] e m = e m,m-1 + e m,0

[0036]

[0037]

[0038] Among them, e m,m-1 represents the dynamic error between adjacent member vehicles m and m - 1 at time t, where m = 2, 3, …, M during calculation; C m,m-1 represents the weight coefficient between adjacent member vehicles m and m - 1; N represents the prediction for N control cycles; k represents the kth of the N control cycles; D represents the ideal spacing between adjacent vehicles; represents the two - norm; e m,0 represents the dynamic error between member vehicle m and leading vehicle 0 at time t, where m = 1, 2, 3, …, M during calculation; C m,0 represents the weight coefficient between member vehicle m and leading vehicle 0; x0(t) represents the position of the leading vehicle at time t; v0(t) represents the speed of the leading vehicle at time t; e m represents the tracking error of member vehicle m at time t, which is used as the objective function of member vehicle m, where m = 1, 2, 3, …, M during calculation; l m,t = {0, 1} is a binary variable representing the sub - channel allocation result of member vehicle m at time t, l m,t = 0 indicates that it is not allocated a sub - channel, l m,t = 1 indicates that it is allocated a sub - channel; Ω l,t = {l m,t , m = 1, 2, …, M} represents the sub - channel allocation results of all member vehicles at time t; Ω u,t = {u m{(t), m = 1, 2, …, M} represents the control input parameters of all member vehicles at time t; s.t. represents multiple constraints that hold for m = 1, 2, …, M; represents the predicted motion state of member vehicle m at time t + (k - 1)T; represents the predicted position of member vehicle m at time t + (k - 1)T; represents the predicted speed of member vehicle m at time t + (k - 1)T; represents the assumed motion state of member vehicle m at time t + (k - 1)T; represents the assumed position of member vehicle m at time t + (k - 1)T; represents the assumed speed of member vehicle m at time t + (k - 1)T; represents the predicted control input parameters of member vehicle m at time t + (k - 1)T; represents the assumed control input parameters of member vehicle m at time t + (k - 1)T; represents the predicted motion state of member vehicle m at time t; y m (t) represents the actual motion state of member vehicle m at time t; represents the predicted motion state of member vehicle m in the previous control period at time t; u min represents the minimum control input parameter value; u max represents the maximum control input parameter value; represents the predicted motion state of member vehicle m at time t + NT; represents the assumed motion state of member vehicle m at time t + NT.

[0039] In an embodiment of the present invention, in the first - stage solution of the joint optimization problem, the first - stage solution result is the sub - channel allocation result of each member vehicle in this control period, including:

[0040] According to the scheduling strategy based on the tracking error, obtain B member vehicles sorted from large to small in terms of tracking error, and allocate B sub - channels to the B member vehicles one by one;

[0041] Among them, the scheduling strategy based on the tracking error is expressed as:

[0042]

[0043]

[0044] Among them, the scheduling strategy based on the tracking error is for time t, e' m represents the tracking error of member vehicle m for time t - T.

[0045] In one embodiment of the present invention, based on the solution result of the first stage, the second stage of solving the joint optimization problem for the control input parameters is performed to obtain the solution result of the second stage, including the optimal control input parameter sequence of each member vehicle within the control period; and the motion state of the corresponding member vehicle is controlled by using the solution result of the second stage, including:

[0046] According to the solution result of the first stage, the joint optimization problem is simplified again, and the MPC algorithm is used to solve it, obtaining the optimal control input parameter sequence of each member vehicle, and the motion state of the corresponding member vehicle is controlled by using the optimal control input parameter sequence of each member vehicle;

[0047] Among them, the joint optimization problem after being simplified again is expressed as:

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] Among them, the optimal control input parameter sequence of member vehicle m for time t is expressed as

[0058] In one embodiment of the present invention, the controlling the motion state of the corresponding member vehicle by using the optimal control input parameter sequence of each member vehicle includes:

[0059] For member vehicle m, its motion state is controlled by using the first element in its optimal control input parameter sequence.

[0060] In one embodiment of the present invention, when the stop condition is not satisfied, before returning to execute the step of obtaining the motion states of the member vehicles in the vehicle formation at the start of the control period for the next control period, the method further includes:

[0061] Calculate the hypothetical control input parameters for the next control cycle as follows:

[0062]

[0063] wherein, represents the elements of the optimal control input parameter sequence of the member vehicle m except the first element.

[0064] In a second aspect, an embodiment of the present invention provides a joint optimization system for vehicle formation resource allocation and control, which is applied to any vehicle formation. The vehicle formation includes a leading vehicle and multiple member vehicles. The system includes:

[0065] The member vehicles are used to report their own motion states to the leading vehicle at the beginning of each control cycle of the leading vehicle;

[0066] The leading vehicle is used to, for each control cycle, obtain the motion states of the member vehicles in the vehicle formation at the beginning of the control cycle; based on the motion states of all member vehicles, as well as the pre-constructed vehicle network communication model, vehicle dynamics model, and MPC-based platoon control model, establish a joint optimization problem for sub-channel allocation and control input parameters of the vehicle formation; perform a first-stage solution to the joint optimization problem to obtain the first-stage solution result as the sub-channel allocation result of each member vehicle in the control cycle; based on the first-stage solution result, perform a second-stage solution to the joint optimization problem for the control input parameters to obtain the second-stage solution result including the optimal control input parameter sequence of each member vehicle in the control cycle; and use the second-stage solution result to control the motion states of the corresponding member vehicles; when the stop condition is not met, for the next control cycle, return to execute the step of obtaining the motion states of the member vehicles in the vehicle formation at the beginning of the control cycle; wherein, the motion state includes position and speed; the control input parameter characterizes the acceleration of the member vehicle; the MPC-based platoon control model takes into account the actual motion states of the member vehicles; the optimal control input parameter sequence includes the optimal control input parameters for multiple future control cycles starting from this control cycle.

[0067] The beneficial effects of the present invention:

[0068] In the solution provided by the embodiments of the present invention, under the condition of limited channel resources, a vehicle network communication model, a vehicle dynamics model, and a platoon control model based on MPC are pre-constructed. For each control cycle, the motion states of the member vehicles in the vehicle platoon at the beginning of the control cycle are obtained. A joint optimization problem of sub-channel allocation and control input parameters for the vehicle platoon is established by using the motion states of all member vehicles and the pre-constructed multiple models. The sub-channel allocation results of the member vehicles and the optimal control input parameter sequence are obtained successively through two-stage optimization. Furthermore, the member vehicles are controlled by using the optimal control input parameter sequence, so as to maintain the target inter-vehicle distance and enable the platoon to drive safely and stably. Since the embodiments of the present invention consider the joint optimization of resource allocation and control as well as the interference and uncertainty in vehicle dynamics, compared with the prior art, the model is more complete and the optimization result is closer to the actual situation. Description of the Drawings

[0069] Figure 1 It is a schematic flowchart of a joint optimization method for vehicle platoon resource allocation and control provided by an embodiment of the present invention;

[0070] Figure 2 It is a schematic diagram for understanding the MPC-based platoon control model for member vehicle m according to an embodiment of the present invention;

[0071] Figure 3 It is a schematic structural diagram of a joint optimization system for vehicle platoon resource allocation and control provided by an embodiment of the present invention;

[0072] Figure 4(a) is a graph showing the variation of the vehicle speed of all vehicles with time in Experiment 1 of an embodiment of the present invention;

[0073] Figure 4(b) is a graph showing the variation of the spacing error of all member vehicles relative to the leading vehicle with time in Experiment 1 of an embodiment of the present invention;

[0074] Figure 4(c) is a graph showing the variation of the spacing error of all member vehicles relative to the leader vehicle with time in Experiment 1 of an embodiment of the present invention;

[0075] Figure 5(a) is a graph showing the variation of the spacing error between a member vehicle and the leader vehicle with time under the global scheduling strategy in Experiment 2 of an embodiment of the present invention;

[0076] Figure 5(b) is a graph showing the variation of the spacing error between a member vehicle and the leader vehicle with time under the tracking error-based scheduling strategy of an embodiment of the present invention in Experiment 2;

[0077] Figure 5(c) is a graph showing the variation of the spacing error between a member vehicle and the leader vehicle with time under the polling scheduling strategy in Experiment 2 of an embodiment of the present invention;

[0078] Figure 6This is a comparison chart of the cumulative interval error under different scheduling strategies and different numbers of sub-channels in Experiment 3 of the embodiments of the present invention. Detailed implementation manners

[0079] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0080] Next, a joint optimization method for vehicle formation resource allocation and control provided by the embodiments of the present invention will be introduced first. As Figure 1 shown, a joint optimization method for vehicle formation resource allocation and control provided by the embodiments of the present invention may include the following steps:

[0081] S1. For each control cycle, obtain the motion states of the member vehicles in the vehicle formation at the beginning of the control cycle; based on the motion states of all member vehicles, as well as the pre-constructed vehicle network communication model, vehicle dynamics model, and MPC-based vehicle formation control model, establish a joint optimization problem for sub-channel allocation and control input parameters of the vehicle formation.

[0082] To facilitate the understanding of the scenarios and solutions of the embodiments of the present invention, the pre-construction processes of the vehicle network communication model, vehicle dynamics model, and MPC-based vehicle formation control model will be described first.

[0083] (1) Vehicle network communication model

[0084] The scenario considered in the embodiments of the present invention is: there is a vehicle formation including M + 1 vehicles on a horizontal road, and the vehicle formation includes a leader vehicle and M member vehicles.

[0085] The pre-constructed vehicle network communication model includes:

[0086] In the vehicle formation, except for the leading vehicle, the number of member vehicles is M. The vehicle formation adopts an equal-spacing strategy, that is, the vehicle spacing is constant, aiming to maintain a constant inter-vehicle distance. Under the condition of limited channel resources, there are B sub-channels in the channel resources within one control period, and B ≤ M. Both B and M are natural numbers greater than 0. To achieve formation control, the sub-channels allocated to member vehicles can communicate with the leading vehicle to transmit their motion states. Therefore, the communication mechanism within the vehicle formation is designed as follows: In each control period, the member vehicles allocated sub-channels are used to send their motion states to the leading vehicle through V2V communication; among them, the motion state includes position and speed; then, the controller of the leading vehicle is used to perform joint optimization of sub-channel allocation and fleet control input parameters based on the periodically received motion states, and broadcast the optimization results to the member vehicles. The fleet control input parameters are used to adjust the acceleration or deceleration of each member vehicle; among them, the control input parameters characterize the acceleration of the member vehicles. And the above process is repeated in each control period to keep the fleet driving safely and stably.

[0087] (2) Vehicle dynamics model

[0088] The vehicle dynamics model captures the relationship between vehicle dynamics states and control variables. The vehicle dynamics state is the motion state. The control variable is the control input parameter, specifically the acceleration.

[0089] In an optional implementation manner, the construction process of the vehicle dynamics model includes:

[0090] 1) For any member vehicle m, construct a discrete-time dynamics model as:

[0091]

[0092] where x m (t) and v m (t) respectively represent the position and speed of member vehicle m at time t; T represents the discrete time interval, which is the time length of one control period. Since control is performed at the beginning of each control period, it also represents the time interval between two control moments; u m (t) represents the control input parameter of member vehicle m at time t, that is, the acceleration;

[0093] 2) Simplify and rewrite the discrete-time dynamics model in the form of motion state as:

[0094]

[0095] where y m (t) = [x m (t), v m (t)]T Indicates the motion state of the member vehicle m at time t; Indicates the vehicle dynamics function.

[0096] (3) MPC-based platoon control model

[0097] The platoon control model determines how to calculate the control input parameters of the vehicle in each control cycle. In the embodiment of the present invention, a new platoon control model is proposed after improving the MPC platoon control model, which is a platoon control model based on MPC.

[0098] The pre-constructed MPC-based platoon control model includes:

[0099] On the basis of the MPC platoon control model, the following constraint is added: for a member vehicle m, if the member vehicle m is assigned a sub-channel at time t, the predicted motion state and the assumed motion state of the member vehicle m at time t are the actual motion state of the member vehicle m itself sent to the leading vehicle using the assigned sub-channel at time t; if the member vehicle m is not assigned a sub-channel at time t, the predicted motion state and the assumed motion state of the member vehicle m at time t are the predicted motion state of the member vehicle m in the previous control cycle; wherein, in the MPC-based platoon control model, for each time t, the control input parameters in the future are predicted with N control cycles, and k represents the k-th of the N control cycles.

[0100] It can be seen that, compared with the existing MPC platoon control model, in the embodiment of the present invention, when determining the predicted motion state and the assumed motion state of the member vehicle m at time t, the actual motion state of the member vehicle is considered.

[0101] It should be noted that, considering the interference and uncertainty in vehicle dynamics, in the simulation experiment scenario and the actual scenario of the embodiment of the present invention, the actual motion state of the member vehicle can be replaced by the predicted motion state plus a Gaussian noise.

[0102] To facilitate the understanding of the MPC-based platoon control model, it is described in conjunction with Figure 2 as follows. Figure 2 This is a schematic diagram for understanding the MPC-based platoon control model of the member vehicle m in the embodiment of the present invention.

[0103] The controller of the leading vehicle predicts the control input with N control cycles, and these N control cycles are the prediction window, Figure 2For simplicity, it is written as N cycles; where N is a natural number greater than 0. It can be understood that predicting the control input at a control moment actually predicts the control inputs for N control cycles starting from that control moment.

[0104] Taking time t as an example (i.e., the control moment is t), two motion states of the member vehicle m are defined, which are respectively and The former is the predicted motion state of the member vehicle m at time t+(k - 1)T, and the latter is the assumed motion state of the member vehicle m at time t+(k - 1)T.

[0105] Specifically, for k = 1, that is, the predicted motion state of the member vehicle m at time t at the start of the control cycle If the member vehicle m is assigned a sub-channel at time t, is the actual motion state of the member vehicle m at time t; if the member vehicle m is not assigned a sub-channel at time t, is the predicted motion state of the member vehicle m in the previous control cycle, that is

[0106] Specifically, for k = 1, that is, the assumed motion state of the member vehicle m at time t at the start of the control cycle If the member vehicle m is assigned a sub-channel at time t, is the actual motion state of the member vehicle m at time t; if the member vehicle m is not assigned a sub-channel at time t, is the predicted motion state of the member vehicle m in the previous control cycle, that is

[0107] For each k from k = 2 to N + 1, for According to the assumed motion state of the member vehicle m at time t The assumed control input parameter of the member vehicle m at time t and formula (2), according to the recurrence relation, the

[0108] for the subsequent N control cycles can be calculated. Among them, the assumed control input parameter of the member vehicle m at time t is the predicted control input parameter of the previous control cycle m (t - T) is Sent from the leader vehicle to the member vehicle. The subsequent (where k = 2, 3,..., N) are used as the assumed control input parameters for the next control cycle (where k = 1, …, N - 1),

[0109] For each k from k = 2 to N + 1, for in terms of the predicted motion state of the member vehicle m at time t and formula (2), according to the recurrence relation, the each can be represented by and is the optimization variable in the optimization problem.

[0110] The specific process can be understood in combination with Figure 2 and the control process of the existing MPC vehicle platoon control model.

[0111] In an optional implementation manner, the expression of the joint optimization problem of vehicle platoon resource allocation and control includes:

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120] Among them,

[0121] e m = e m,m-1 + e m,0 (4)

[0122]

[0123]

[0124] Among them, e m,m-1 represents the dynamic error between adjacent member vehicles m and m - 1 at time t, where m = 2, 3, …, M when calculating; C m,m-1denotes the weight coefficient between adjacent member vehicles m and m-1; N denotes the prediction for N control cycles; k denotes the k-th of the N control cycles; D denotes the ideal spacing between adjacent vehicles; denotes the two-norm; e m,0 denotes the dynamic error between member vehicle m and leader vehicle 0 at time t, where m = 1, 2, 3, …, M during calculation; C m,0 denotes the weight coefficient between member vehicle m and leader vehicle 0; x0(t) denotes the position of the leader vehicle at time t; v0(t) denotes the speed of the leader vehicle at time t; e m denotes the tracking error of member vehicle m at time t, which is used as the objective function of member vehicle m, where m = 1, 2, 3, …, M during calculation; l m,t = {0, 1} is a binary variable denoting the sub-channel allocation result of member vehicle m at time t, l m,t = 0 indicates that it is not allocated a sub-channel, l m,t = 1 indicates that it is allocated a sub-channel; Ω l,t = {l m,t , m = 1, 2, …, M} denotes the sub-channel allocation results of all member vehicles at time t; Ω u,t = {u m (t), m = 1, 2, …, M} denotes the control input parameters of all member vehicles at time t; s.t. denotes multiple constraint conditions that hold for m = 1, 2, …, M; denotes the predicted motion state of member vehicle m at time t+(k-1)T; denotes the predicted position of member vehicle m at time t+(k-1)T; denotes the predicted speed of member vehicle m at time t+(k-1)T; denotes the assumed motion state of member vehicle m at time t+(k-1)T; denotes the assumed position of member vehicle m at time t+(k-1)T; denotes the assumed speed of member vehicle m at time t+(k-1)T; denotes the predicted control input parameter of member vehicle m at time t+(k-1)T; denotes the assumed control input parameter of member vehicle m at time t+(k-1)T; denotes the predicted motion state of member vehicle m at time t; y m (t) denotes the actual motion state of member vehicle m at time t; denotes the predicted motion state of member vehicle m in the previous control cycle at time t; u min denotes the minimum value of the control input parameter; u max denotes the maximum value of the control input parameter; Represents the predicted motion state of the member vehicle m at time t+NT; Represents the assumed motion state of the member vehicle m at time t+NT.

[0125] The following briefly describes the construction process of the joint optimization problem represented by the above formula (3).

[0126] In the embodiment of the present invention, the control objective of the vehicle formation is that each member vehicle tracks the speed of the leading vehicle and maintains an ideal distance interval from the previous vehicle. Since the radio channel resource allocation and the formation control will affect the performance of the formation at the same time, the embodiment of the present invention aims to jointly process the sub-channel allocation and control of each member vehicle, and maintain a constant inter-vehicle distance by minimizing the tracking error of each member vehicle. Therefore, for each member vehicle m, formula (4) is obtained using formulas (5) and (6). Formula (4) is used as the objective function of the member vehicle m, characterizing its optimization objective.

[0127] Then, based on the objective function of each member vehicle, the objective function of the vehicle formation can be defined as the sum of the tracking errors of each member vehicle. Therefore, in each control cycle, the expression of the joint optimization problem of the vehicle formation resource allocation and control represented by formula (3) is obtained. Among them, the following explanations are made for the constraint conditions:

[0128] and respectively represent that the predicted motion state and the assumed motion state of the member vehicle m are composed of the position and speed of the member vehicle m.

[0129] and respectively represent that the predicted motion state and the assumed motion state need to satisfy the vehicle dynamics function, that is, formula (2).

[0130] and respectively represent the influence of sub-channel allocation on the predicted motion state and the assumed motion state.

[0131] Ensure that the number of sub-channels allocated to the member vehicle is B, where the multiplexing of sub-channels is not considered, and each member vehicle can be allocated at most one sub-channel.

[0132] Represents the limit of the control input parameters of each member vehicle.

[0133] Represents that it is a convergence constraint.

[0134] S2, perform the first - stage solution for the joint optimization problem, and the obtained first - stage solution is the sub - channel allocation result of each member vehicle within this control period.

[0135] In actual situations, it is very difficult for the leader vehicle to obtain the global information at the beginning of each control period. The actual motion states of each member vehicle need to be sent to the leader vehicle through sub - channels. Therefore, it is difficult to find its global optimal solution. It is feasible for the embodiment of the present invention to approximately solve the problem using a decomposition method, in which sub - channel allocation and the control of each member vehicle are alternately considered in two stages. In the first stage, the leader vehicle allocates sub - channels according to the tracking error of the member vehicles. In the second stage, the leader vehicle solves the control input parameters of each member vehicle according to the sub - channel allocation result and the platoon control model based on MPC. Although the embodiment of the present invention uses a two - stage method to solve the problem, the optimizations in these two stages are not independent and they will affect each other. Since the optimization of the control input parameters is based on the sub - channel allocation scheme, the solution in the first stage will affect the control optimization in the second stage; the control input parameters optimized in the second stage determine the tracking error, which will affect the sub - channel allocation optimization in the first stage of the next control period.

[0136] For this step, different from traditional communication networks, the channel resource scheduling of radios in vehicle platoons aims to reduce the tracking error. For the leader vehicle, if it can receive the actual motion state of member vehicle m, it can better reduce the tracking error e. m . To minimize the tracking error of each member vehicle, the scheduling tends to allocate sub - channels to member vehicles with larger tracking errors. Based on this core idea, the embodiment of the present invention proposes a scheduling strategy based on tracking error, and the scheduling strategy based on tracking error is expressed as:

[0137]

[0138] wherein, the scheduling strategy based on tracking error is for time t. For the sake of distinction, here, e' m represents the tracking error of member vehicle m for time t - T. This is because the scheduling strategy based on tracking error at time t allocates sub - channels for the current control period of time t according to the tracking error of the previous control period. Therefore, formula (7) uses the tracking error of member vehicle m for time t - T, and only in formula (7) does e' m represent the tracking error of member vehicle m for time t - T. For the rest of the formulas, unless otherwise specified, e mDenote the tracking error of member vehicle m at time t. It can be seen that if member vehicle m has a large tracking error, there is a high probability that the link formed by member vehicle m and the leader vehicle will obtain a sub-channel, and then the leader vehicle can obtain the actual motion state of member vehicle m. Using the actual motion state of member vehicle m, the leader vehicle can better reduce its tracking error.

[0139] Therefore, in this step, the first-phase solution of the joint optimization problem is obtained, and the first-phase solution result is the sub-channel allocation result of each member vehicle within this control period, including:

[0140] According to the scheduling strategy based on the tracking error, obtain B member vehicles sorted from large to small in terms of tracking error, and allocate the B sub-channels to the B member vehicles one by one.

[0141] Among them, for each of the M member vehicles, the sub-channel allocation result of having or not having can be represented by 0 and 1 respectively, as specifically explained in the previous text l m,t 's explanation. The sub-channel allocation results of all member vehicles can be in the form of represented.

[0142] It should be noted that there is no limitation on the way of one-to-one correspondence allocation between the B sub-channels and the B member vehicles here. For example, it can be random allocation, etc.

[0143] S3. Based on the first-phase solution result, perform the second-phase solution of the joint optimization problem for the control input parameters, and the second-phase solution result includes the optimal control input parameter sequence of each member vehicle within this control period; and use the second-phase solution result to control the motion state of the corresponding member vehicle.

[0144] After obtaining the first-phase solution result for the joint optimization problem represented by formula (3), simplify it again. The jointly optimized problem after re-simplification is expressed as:

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153] Among them, the optimal control input parameter sequence of the member vehicle m for time t is expressed as The embodiment of the present invention proves that the joint optimization problem shown in formula (8) is a convex problem, so it can be effectively solved by the MPC algorithm.

[0154] Therefore, in an optional implementation manner, based on the solution result of the first stage, the second stage solution of the joint optimization problem for the control input parameters is performed to obtain the solution result of the second stage, including the optimal control input parameter sequence of each member vehicle within this control period; and the motion state of the corresponding member vehicle is controlled by using the solution result of the second stage, including:

[0155] According to the solution result of the first stage, the joint optimization problem is simplified again, and the MPC algorithm is used to solve it to obtain the optimal control input parameter sequence of each member vehicle, and the motion state of the corresponding member vehicle is controlled by using the optimal control input parameter sequence of each member vehicle.

[0156] Among them, taking any member vehicle m, m = 1, 2,..., M as an example, the process of solving by using the MPC algorithm and realizing the control of the motion state of the member vehicle starting from time t = 0 can include the following steps:

[0157] ① Initialization: At time t = 0, assume that all member vehicles move at a constant speed, and initialize the assumed values of the member vehicle m as:

[0158]

[0159] Among them, can be obtained by iterative calculation using the following formula (10).

[0160]

[0161] Among them, represents the assumed control input parameter of the member vehicle m in the kth future control period at time t = 0; represents the assumed motion state of the member vehicle m in the kth future control period at time t = 0; represents the predicted motion state of the member vehicle m in the kth future control period at time t = 0; represents the predicted motion state of the member vehicle m in the first future control period at time t = 0; y m (0) represents the actual motion state of the member vehicle m at time t = 0; Represents the predicted motion state of the member vehicle m in the (k + 1)-th control period in the future at time t = 0.

[0162] ②MPC Iteration:

[0163] At any time t > 0, for any member vehicle m, the steps to be followed are as follows:

[0164] (a) Based on the current motion state y m (1|t) of the member vehicle m determined by the sub-channel allocation result in the first stage, the assumed motion state of the vehicle in front of the member vehicle m and the motion state (x0(t), v0(t)) of the leading vehicle, T use the interior point method to solve the non-linear constrained optimization problem, and obtain the optimal control sequence of the member vehicle m as:

[0165] wherein, the optimal control input parameter sequence includes the optimal control input parameters for multiple control periods in the future starting from this control period. The total number of multiple control periods in the future starting from this control period is N.

[0166] (b) When the stop condition is not satisfied, calculate the assumed control input parameter for the next control period according to formula (11) as:

[0167]

[0168] wherein, represents the remaining elements of the optimal control input parameter sequence of the member vehicle m except the first element.

[0169] That is to say, in the embodiment of the present invention, when the stop condition is not satisfied, for the next control period, before returning to execute the step of obtaining the motion states of the member vehicles in the vehicle formation at the start of this control period described in S1, it is necessary to calculate the assumed control input parameter for the next control period according to formula (11)

[0170] (c) Control the motion state of the corresponding member vehicle by using the optimal control input parameter sequence of each member vehicle.

[0171] Specifically, for the member vehicle m, control its motion state by using the first element in its optimal control input parameter sequence. That is to say, the control input parameter of the member vehicle m at time t should be:

[0172] (d) Increase the time t, and return to execute step (a) until the stop condition is satisfied.

[0173] Among them, increasing the time t, i.e., the time becomes t + T, and the satisfaction of the stop condition can be to execute the optimization process until the error converges to 0 and the curve converges.

[0174] S4. When the stop condition is not satisfied, for the next control cycle, return to execute the step of obtaining the motion states of the member vehicles in the vehicle formation to which the current control cycle belongs.

[0175] That is to say, when the stop condition is not satisfied, return to step S1 to perform two-stage solution for the next control cycle.

[0176] In the solution provided by the embodiments of the present invention, under the condition of limited channel resources, a vehicle network communication model, a vehicle dynamics model, and an MPC-based vehicle formation control model are pre-constructed. For each control cycle, the motion states of the member vehicles in the vehicle formation to which the current control cycle belongs are obtained. A joint optimization problem of sub-channel allocation and control input parameters of the vehicle formation is established by using the motion states of all member vehicles and the pre-constructed multiple models. The sub-channel allocation results of the member vehicles and the optimal control input parameter sequence are obtained successively through two-stage optimization. Then, the member vehicles are controlled by using the optimal control input parameter sequence, so as to maintain the target inter-vehicle distance and enable the vehicle formation to travel safely and stably. Since the embodiments of the present invention consider the joint optimization of resource allocation and control as well as the interference and uncertainty in vehicle dynamics, compared with the prior art, the model is more complete and the optimization result is closer to the actual situation.

[0177] In a second aspect, corresponding to the above method embodiments, the embodiments of the present invention further provide a joint optimization system for vehicle formation resource allocation and control, which is applied to any vehicle formation. The vehicle formation includes a leading vehicle and multiple member vehicles. As Figure 3 shown, the system includes:

[0178] The member vehicles are used to report their own motion states to the leading vehicle at the beginning of each control cycle of the leading vehicle;

[0179] The leading vehicle is used to obtain the motion states of each member vehicle in the vehicle formation at the start of each control period; based on the motion states of all member vehicles, as well as the pre-constructed vehicle network communication model, vehicle dynamics model, and MPC-based vehicle platoon control model, establish a joint optimization problem for sub-channel allocation and control input parameters of the vehicle formation; perform a first-stage solution to the joint optimization problem to obtain the first-stage solution result as the sub-channel allocation result of each member vehicle within this control period; based on the first-stage solution result, perform a second-stage solution to the joint optimization problem for the control input parameters to obtain the second-stage solution result including the optimal control input parameter sequence of each member vehicle within this control period; use the second-stage solution result to control the motion state of the corresponding member vehicle, and when the stop condition is not met, for the next control period, return to execute the step of obtaining the motion states of each member vehicle in the vehicle formation at the start of this control period; where the motion state includes position and speed; the control input parameter characterizes the acceleration of the member vehicle; the MPC-based vehicle platoon control model takes into account the actual motion state of the member vehicle; the optimal control input parameter sequence includes the optimal control input parameters for multiple future control periods starting from this control period.

[0180] Figure 3 In the vehicle formation, there are a total of M + 1 vehicles. The leading vehicle is identified by 0, and the member vehicles are identified by 1 to M. In each control period, the member vehicles transmit their motion states to the leading vehicle; after the leading vehicle obtains the optimization result according to the above processing process, it broadcasts the sub-channel allocation scheme and control input parameters to the member vehicles. The above processing process of the leading vehicle is completed by its controller. For the specific process, please refer to the joint optimization method for vehicle formation resource allocation and control described in the first aspect, which will not be elaborated here.

[0181] For the specific content, please refer to the specific content of the joint optimization method for vehicle formation resource allocation and control described in the first aspect, which will not be elaborated here.

[0182] It can be seen that the joint optimization system for vehicle platoon resource allocation and control provided by the embodiments of the present invention, through the information interaction between the member vehicles and the leader vehicle, under the condition of limited channel resources, the controller of the leader vehicle pre-constructs a vehicle network communication model, a vehicle dynamics model, and a platoon control model based on MPC, and for each control cycle, obtains the motion states of the member vehicles in the vehicle platoon at the beginning of the control cycle, and uses the motion states of all member vehicles and the pre-constructed multiple models to establish a joint optimization problem of sub-channel allocation and control input parameters for the vehicle platoon, and successively obtains the sub-channel allocation results of the member vehicles and the optimal control input parameter sequence through two-stage optimization, and then uses the optimal control input parameter sequence to control the member vehicles, so as to maintain the target inter-vehicle distance and make the platoon drive safely and stably. Due to considering the joint optimization of resource allocation and control and the interference and uncertainty in vehicle dynamics, compared with the prior art, the model is more complete and the optimization result is closer to the actual situation.

[0183] To verify the performance of the joint optimization method for vehicle platoon resource allocation and control proposed in the embodiments of the present invention, the following will be described by simulation experiments and comparative experiments.

[0184] (I) Experimental setup

[0185] At time t = 0, the initial state of the vehicle platoon is set to an ideal state: in the motion state of the leader vehicle, the position is x0(0) = 0 and the speed is v0(0) = 20, where the position unit is meters and the speed unit is meters per second. And, the position of the member vehicle m is: x m (0) = -mD, and the speed is v m (0) = 20, m = 1, 2…, M; the ideal spacing D is 10 meters. A Gaussian noise with an average value of 0 and a standard deviation of σ is considered at the actuator end of the member vehicle. Considering the interference and uncertainty in vehicle dynamics, the actual motion state is replaced by the predicted motion state plus this Gaussian noise.

[0186] The trajectory of the leader vehicle is as follows:

[0187]

[0188] Among them, a0 represents the acceleration of the leader vehicle, and the unit is meters per second squared (m / s 2 ).

[0189] The default values of the simulation parameters are shown in Table 1 below.

[0190] Table 1

[0191]

[0192]

[0193] (2) Experimental process

[0194] (1) Experiment 1

[0195] When the number of sub-channels is 6, the platoon control performance of the solution proposed in the embodiment of the present invention is evaluated from the time domain perspective. Figures 4(a), 4(b) and 4(c) respectively plot the vehicle speed, the spacing error of the member vehicle relative to the leading vehicle, and the spacing error of the member vehicle relative to the leader vehicle. Among them, in Figure 4(a), the horizontal axis is speed and the vertical axis is time, PL represents the leading vehicle, and PM1 to PM7 represent 7 member vehicles; in Figures 4(b) and 4(c), the vertical axis spacing error represents the spacing error.

[0196] From Figures 4(a) to 4(c) it can be seen that the solution proposed in the embodiment of the present invention can control the spacing and speed within the normal range. The curve converges to 0, indicating that the solution has achieved the platoon control goal.

[0197] (2) Experiment 2

[0198] This simulation experiment evaluates the impact of radio resource allocation on platoon control performance. For comparison, two other scheduling strategies are simultaneously simulated. The first is the global scheduling strategy. Assuming that the leading vehicle knows the global information (the actual motion state of each member vehicle) at the beginning of each control cycle, then the global optimal solution of this problem can be searched by exhaustively enumerating each sub-channel allocation scheme, which is a lower bound. The second is the polling scheduling strategy, in which each member vehicle is scheduled in sequence. Figure 5 plots the spacing error between the member vehicle and the leading vehicle under different sub-channel allocation strategies. Among them, Figure 5(a) represents the global scheduling strategy; Figure 5(b) represents the tracking error-based scheduling strategy proposed in the embodiment of the present invention; Figure 5(c) represents the polling scheduling strategy; it can be seen from Figure 5 that the global scheduling strategy has the smallest absolute value of the spacing error and eliminates the spacing error shortly after the leading vehicle accelerates to a stable speed. This is because the leading vehicle knows the global information of the member vehicles at the beginning of each control cycle. The tracking error-based scheduling strategy of the embodiment of the present invention is suitable for the actual scenario, and its performance is close to the global scheduling strategy and far superior to the polling scheduling strategy, proving the feasibility of the tracking error-based scheduling strategy of the embodiment of the present invention.

[0199] (3) Experiment 3

[0200] To make the comparison more intuitive, the absolute values of the interval errors of all member vehicles relative to the leading vehicle are added up and accumulated over time to obtain the cumulative interval error, which is used as an index for the platoon control performance under different scheduling strategies and different numbers of sub-channels. Figure 6The cumulative spacing error under different scheduling strategies and different numbers of sub-channels is plotted. Figure 6 In Figure 6 , the vertical axis "cumulative spacing error" represents the cumulative spacing error. The horizontal axis "Number of sub-channels" represents different values of the number of sub-channels B. In the legend, "All vehicles are scheduled" means that all vehicles are arranged, that is, 7 sub-channels are assigned to 7 member vehicles one by one, and are correspondingly marked with "1" above the bar chart. "global SchedulingStrategy" represents the global scheduling strategy, and is marked with "2" on the corresponding bar chart; "Tracking Error BasedScheduling Strategy" represents the scheduling strategy based on tracking error proposed in the embodiment of the present invention, and is marked with "3" on the corresponding bar chart; "Round Robin Scheduling Strategy" represents the round-robin scheduling strategy, and is marked with "4" on the corresponding bar chart. It can be seen that, with the number of sub-channels determined, the performance comparison of different scheduling strategies is the same as before. The performance of the global scheduling strategy is the best, followed by the scheduling strategy based on tracking error in the embodiment of the present invention, and the performance of the round-robin scheduling strategy is the worst. As the number of sub-channels decreases, the control performance of the vehicle fleet gradually deteriorates because the fewer the number of sub-channels, the less actual dynamic state information of the member vehicles that the leading vehicle can obtain in each control cycle.

[0201] In summary, to solve the problem of joint optimization of vehicle fleet resource allocation and control under resource constraints, the embodiment of the present invention designs a joint optimization method and system for vehicle formation resource allocation and control, and proposes a two-stage solution method. Through simulation, the vehicle fleet control performance of the scheme is evaluated, and the impact of radio resource allocation on vehicle fleet control is analyzed. The simulation results verify that the scheme proposed in the embodiment of the present invention can provide good control performance.

[0202] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification.

[0203] The above are only the preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are all included in the protection scope of the present invention.

Claims

1. A joint optimization method for vehicle formation resource allocation and control, characterized in that, Applied to the controller of the leading vehicle in a vehicle platoon, the method includes: For each control cycle, obtain the motion states of each member vehicle in the vehicle platoon at the start of the control cycle; based on the motion states of all member vehicles, as well as a pre-constructed vehicle network communication model, vehicle dynamics model, and MPC-based platoon control model, establish a joint optimization problem for sub-channel allocation and control input parameters of the vehicle platoon; wherein, the motion state includes position and speed; the control input parameter characterizes the acceleration of the member vehicle; the MPC-based platoon control model takes into account the actual motion states of the member vehicles. Perform a first-stage solution to the joint optimization problem to obtain the first-stage solution result as the sub-channel allocation results of each member vehicle within the control cycle. Based on the first-stage solution result, perform a second-stage solution to the joint optimization problem for the control input parameters to obtain the second-stage solution result including the optimal control input parameter sequence of each member vehicle within the control cycle; and use the second-stage solution result to control the motion states of the corresponding member vehicles; wherein, the optimal control input parameter sequence includes the optimal control input parameters for multiple future control cycles starting from this control cycle. When the stop condition is not met, for the next control cycle, return to execute the step of obtaining the motion states of each member vehicle in the vehicle platoon at the start of the control cycle. Among them, the number of member vehicles in the vehicle platoon except the leading vehicle is M; there are a total of B sub-channels for channel resources within one control cycle, and B ≤ M. The performing a first-stage solution to the joint optimization problem to obtain the first-stage solution result as the sub-channel allocation results of each member vehicle within the control cycle includes: According to the scheduling strategy based on the tracking error, obtain B member vehicles sorted in descending order of tracking error, and allocate the B sub-channels to the B member vehicles one by one. Among them, the scheduling strategy based on the tracking error is expressed as: Among them, the scheduling strategy based on the tracking error is for time t, and e' m represents the tracking error of member vehicle m for time t - T, and l m,t = {0, 1} is a binary variable representing the sub-channel allocation result of member vehicle m at time t.

2. The joint optimization method for vehicle platoon resource allocation and control according to claim 1, characterized in that The pre-constructed vehicle network communication model includes: The vehicle spacing is constant at equal intervals; in each control cycle, the member vehicle assigned with a sub-channel is used to send its motion state to the leading vehicle through V2V communication; the controller of the leading vehicle is used to perform joint optimization of sub-channel allocation and platoon control input parameters according to the periodically received motion states, and broadcast the optimization results to the member vehicles.

3. The joint optimization method for vehicle platoon resource allocation and control according to claim 1 or 2, characterized in that, The construction process of the vehicle dynamics model includes: For any member vehicle m, construct a discrete-time dynamics model as: where x m (t) and v m (t) represent the position and speed of the member vehicle m at time t, respectively; T represents the discrete time interval and is the time length of a control period; u m (t) represents the control input parameter of the member vehicle m at time t; Rewrite the discrete-time dynamics model in the form of motion state and simplify it as: where y m (t)=[x m (t), v m (t)] T represents the motion state of the member vehicle m at time t; F[·,·] represents the vehicle dynamics function.

4. The joint optimization method for vehicle platoon resource allocation and control according to claim 3, wherein The pre-constructed MPC-based platoon control model includes: Based on the MPC platoon control model, the added constraint is as follows: for a member vehicle m, if the member vehicle m is assigned a sub-channel at time t, the predicted motion state and the assumed motion state are the actual motion state of the member vehicle m itself sent to the leading vehicle using the assigned sub-channel at time t; if the member vehicle m is not assigned a sub-channel at time t, the predicted motion state and the assumed motion state are the predicted motion state of the member vehicle m in the previous control cycle; where, in the MPC-based platoon control model, for each time t, the future control input parameters are predicted with N control cycles, and k represents the k-th of the N control cycles.

5. The joint optimization method for vehicle platoon resource allocation and control according to claim 4, characterized in that The expression of the joint optimization problem for vehicle platoon resource allocation and control includes: Wherein, e m = e m,m-1 + e m,0 where, e m,m-1 represents the dynamic error between adjacent member vehicles m and m - 1 at time t, where m = 2, 3, …, M when calculating; C m,m-1 represents the weight coefficient between adjacent member vehicles m and m - 1; N represents the prediction over N control cycles; k represents the k-th of the N control cycles; D represents the ideal spacing between adjacent vehicles; ||·||2 represents the second norm; e m,0 represents the dynamic error between member vehicle m and leading vehicle 0 at time t, where m = 1, 2, 3, …, M when calculating; C m,0 represents the weight coefficient between member vehicle m and leading vehicle 0; x0(t) represents the position of the leading vehicle at time t; v0(t) represents the speed of the leading vehicle at time t; e m represents the tracking error of member vehicle m at time t, which is used as the objective function of member vehicle m, where m = 1, 2, 3, …, M when calculating; l m,t = {0, 1} is a binary variable representing the sub-channel allocation result of member vehicle m at time t, where l m,t = 0 indicates that it is not allocated a sub-channel, and l m,t = 1 indicates that it is allocated a sub-channel; Ω l,t = {l m,t , m = 1, 2, …, M} represents the sub-channel allocation results of all member vehicles at time t; Ω u,t = {u m (t), m = 1, 2, …, M} represents the control input parameters of all member vehicles at time t; s.t. represents multiple constraints, which hold for m = 1, 2, …, M; represents the predicted motion state of member vehicle m at time t+(k - 1)T; represents the predicted position of member vehicle m at time t+(k - 1)T; represents the predicted speed of member vehicle m at time t+(k - 1)T; represents the assumed motion state of member vehicle m at time t+(k - 1)T; represents the assumed position of member vehicle m at time t+(k - 1)T; represents the assumed speed of member vehicle m at time t+(k - 1)T; represents the predicted control input parameter of member vehicle m at time t+(k - 1)T; represents the assumed control input parameter of member vehicle m at time t+(k - 1)T; represents the predicted motion state of member vehicle m at time t; y m (t) represents the actual motion state of member vehicle m at time t; Represents the predicted motion state of the member vehicle m in a control period at time t; u min Represents the minimum control input parameter value; u max Represents the maximum control input parameter value; Represents the predicted motion state of the member vehicle m at time t + NT; Represents the assumed motion state of the member vehicle m at time t + NT.

6. The joint optimization method for vehicle platoon resource allocation and control according to claim 5, wherein Based on the first-stage solution result, perform a second-stage solution to the joint optimization problem for the control input parameters to obtain the second-stage solution result including the optimal control input parameter sequence of each member vehicle within the control cycle. And use the solution result of the second stage to control the motion states of the corresponding member vehicles, including: According to the solution result of the first stage, simplify the joint optimization problem again, and use the MPC algorithm to solve it to obtain the optimal control input parameter sequences of each member vehicle, and use the optimal control input parameter sequences of each member vehicle to control the motion states of the corresponding member vehicles; Among them, the joint optimization problem after being simplified again is expressed as: Among them, the optimal control input parameter sequence of the member vehicle m for time t is expressed as 7. The joint optimization method for vehicle platoon resource allocation and control according to claim 6, wherein The use of the optimal control input parameter sequences of each member vehicle to control the motion states of the corresponding member vehicles includes: For member vehicle m, use the first element in its optimal control input parameter sequence to control its motion state.

8. The joint optimization method for vehicle platoon resource allocation and control according to claim 7, wherein Before the step of returning to execute the step of obtaining the motion states of the member vehicles in the vehicle formation at the start of the control cycle for the next control cycle when the stop condition is not satisfied, the method further includes: Calculate the assumed control input parameters for the next control cycle are as follows: Among them, represents the elements other than the first element in the optimal control input parameter sequence of the member vehicle m.

9. A joint optimization system for vehicle platoon resource allocation and control, characterized in that Applied to any vehicle formation, the vehicle formation includes a leader vehicle and multiple member vehicles, and the system includes: The member vehicles are used to report their own motion states to the leader vehicle at the start of each control cycle of the leader vehicle; The leader vehicle is used to, for each control cycle, obtain the motion states of the member vehicles in the vehicle formation to which it belongs at the start of the control cycle; based on the motion states of all member vehicles, as well as the pre-constructed vehicle network communication model, vehicle dynamics model, and MPC-based vehicle platoon control model, establish a joint optimization problem of sub-channel allocation and control input parameters for the vehicle formation; perform a first-stage solution to the joint optimization problem to obtain the first-stage solution result as the sub-channel allocation results of the member vehicles in this control cycle; based on the first-stage solution result, perform a second-stage solution to the joint optimization problem for the control input parameters to obtain the second-stage solution result including the optimal control input parameter sequences of each member vehicle in this control cycle; and use the second-stage solution result to control the motion states of the corresponding member vehicles; when the stop condition is not satisfied, for the next control cycle, return to execute the step of obtaining the motion states of the member vehicles in the vehicle formation to which it belongs at the start of the control cycle; where the motion state includes position and speed; the control input parameter characterizes the acceleration of the member vehicle; the MPC-based vehicle platoon control model takes into account the actual motion states of the member vehicles; the optimal control input parameter sequence includes the optimal control input parameters for multiple future control cycles starting from this control cycle; Among them, except for the leader vehicle in the vehicle formation, the number of member vehicles is M; there are a total of B sub-channels for channel resources in one control cycle, and B ≤ M; The first-stage solution to the joint optimization problem to obtain the first-stage solution result as the sub-channel allocation results of the member vehicles in this control cycle includes: According to the scheduling strategy based on the tracking error, obtain B member vehicles sorted from large to small in terms of tracking error, and allocate the B sub-channels to the B member vehicles one by one; Among them, the scheduling strategy based on the tracking error is expressed as: Among them, the scheduling strategy based on the tracking error is for time t, and e' m represents the tracking error of member vehicle m for time t - T, and l m,t = {0, 1} is a binary variable representing the sub-channel allocation result of member vehicle m at time t.

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