Heterogeneous vehicle queue cooperative cruise control method oriented to fuel economy and comfort

Abstract

The invention discloses a fuel economy and comfort-oriented heterogeneous vehicle queue cooperative cruise control method, which comprises the following steps of: establishing a three-order discrete time state-space equation of a vehicle, and constructing a model prediction control optimization model of a leading vehicle in a heterogeneous vehicle queue based on the three-order discrete time state-space equation of the vehicle; solving the model prediction control optimization model to obtain the optimal control input of the leader vehicle; constructing a distributed linear feedback control protocol and solving through a discrete system frequency domain analysis method to obtain a control gain for ensuring the internal stability and string stability of a vehicle queue; and obtaining control input of the following vehicle according to the control gain, and realizing heterogeneous vehicle queue cooperative cruise control based on the control input of the following vehicle and the optimal control input of the leader vehicle. By optimizing the control strategy, the fuel economy and riding comfort of the heterogeneous vehicle queue are remarkably improved, and meanwhile the stability and safety of the vehicle queue are guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of adaptive cruise control technology, and in particular relates to a heterogeneous vehicle platoon cooperative cruise control method oriented towards fuel economy and comfort. Background Technology

[0002] In recent years, autonomous driving technology has become a hot topic in academia and industry due to its potential to effectively improve travel efficiency, reduce emissions, and prevent traffic accidents. Autonomous driving technologies include cruise control (CC), eco-CC, adaptive cruise control (ACC), eco-ACC, cooperative adaptive cruise control (CACC), and eco-CACC. Cooperative adaptive cruise control (CACC), as a common control technology in autonomous vehicles, can achieve vehicle platoon stability while considering various random factors in practical applications. eco-CACC, while retaining the advantages of CACC, also enables vehicles to operate in a more energy-efficient manner, thus significantly improving the fuel economy of the entire fleet. Given these potential advantages, eco-CACC has received widespread attention from academia and industry.

[0003] While ensuring the consistency of vehicle queuing states, balancing fuel economy and passenger comfort has gradually become the main objective of current eco-CACC algorithm research. Some studies achieve minimum energy consumption by optimizing control inputs through model predictive control. For example, Yanhong Wu et al. proposed a distributed data-driven model predictive control (DDMPC) strategy, which divides the entire queuing system into multiple subsystems, each of which can be controlled using a traditional MPC controller. Although this method can achieve cooperative control between vehicles, the high complexity of the control algorithm and the difficulty in real-time deployment limit its application in complex traffic environments, and it does not directly consider the fuel consumption of the vehicle queuing. Another set of studies establishes a cost function with fuel saving as the primary objective to ensure that the autonomous vehicle queuing maintains the desired dynamic behavior. For example, Peiyu Zhang et al. proposed a multi-objective control framework for a hybrid vehicle queuing system, whose main objectives are to achieve minimum fuel consumption, maximum passenger comfort, and queuing robustness. However, this method is only based on second-order system modeling and cannot fully reflect the real dynamic characteristics of the vehicles, resulting in an inaccurate model. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a heterogeneous vehicle platoon cooperative cruise control method oriented towards fuel economy and comfort, thereby resolving the issues present in the prior art.

[0005] To achieve the above objectives, the present invention provides a heterogeneous vehicle platoon cooperative cruise control method oriented towards fuel economy and comfort, comprising:

[0006] A third-order discrete-time state-space equation for the vehicle is established, and a model predictive control optimization model for the leader vehicle in a heterogeneous vehicle platoon is constructed based on the third-order discrete-time state-space equation for the vehicle.

[0007] Solve the model predictive control optimization model to obtain the optimal control input for the lead vehicle;

[0008] A distributed linear feedback control protocol is constructed and solved using discrete system frequency domain analysis methods to obtain the control gain that ensures the stability of the vehicle queue and the series stability.

[0009] The control input of the following vehicle is obtained based on the control gain. Based on the control input of the following vehicle and the optimal control input of the lead vehicle, the heterogeneous vehicle platoon cooperative cruise control is realized.

[0010] Optionally, the third-order discrete-time state-space equation of the vehicle is:

[0011]

[0012] Where, p i ,v i ,a i ,u i Let τ be the position, velocity, acceleration, and control input of the i-th vehicle. i T represents the actuator lag time constant and sampling period of vehicle i, respectively.

[0013] Optionally, the cost function of the model predictive control optimization model for the lead vehicle in a heterogeneous vehicle platoon is:

[0014]

[0015] Where N is the prediction time domain, x desire (k) represents the desired target state, a confort For passenger comfort, FuelC0 represents the fuel consumption rate of the lead vehicle, x i (k) is the transpose of the matrix consisting of the position, velocity, and acceleration of the i-th vehicle, u i (k) represents the control input for vehicle i, Q is a matrix composed of the weights of position, speed, and acceleration, and w1, w2, and w3 are the weights of control input, fuel consumption rate, and ride comfort, respectively.

[0016] Optionally, a probabilistic ensemble trajectory sampling algorithm based on a reinforcement learning cross-entropy framework is used to solve the model predictive control optimization model. The process includes:

[0017] Initialize the probability distribution parameters of the control sequence; generate multiple candidate control sequence samples within the constraints of control input and speed; during the forward simulation, calculate the cumulative reward for each sample step by step in the prediction time domain based on the known environmental dynamics model and cost function; select the elite sample set with the highest cumulative reward, update the probability distribution parameters based on the elite sample set, and define a new sampling distribution; repeat the above process for iterative optimization until the convergence condition is met, and output the mean parameter of the optimal control sequence as the optimal control input for the leader vehicle.

[0018] Optional, vehicle i fuel consumption rate FuelC i The formula is:

[0019]

[0020] Among them, b0, b1, b2, b3 and c0, c1, c2 are fuel consumption parameters.

[0021] Optionally, the distributed linear feedback control protocol is:

[0022] u i (k)=k ip e ip (k)+k iv e iv (k)+k ia e ia (k);

[0023] Where, k ip ,k iv ,k ia These are the distance errors e ip (k), velocity error e iv (k) and acceleration error e ia The control gain of (k).

[0024] Optionally, the process of constructing a distributed linear feedback control protocol and solving it using discrete system frequency domain analysis methods to obtain the control gain that ensures the stability within the vehicle queue and the series stability includes:

[0025] Substituting the distributed linear feedback control protocol into the third-order discrete state-space equation of the following vehicle, the open-loop transfer function and closed-loop transfer function are derived through Z-transform.

[0026] The frequency response of the open-loop transfer function is analyzed using the Nyquist stability criterion to obtain a constraint inequality for the control gain that ensures the stability of the vehicle's internal structure; the amplitude-frequency characteristics of the closed-loop transfer function are constrained to derive a constraint inequality for the control gain that ensures the stability of the vehicle platoon.

[0027] By simultaneously solving the constraint inequality of the control gain that ensures stability within the vehicle and the constraint inequality of the control gain that ensures stability of the vehicle platoon, the control gain that guarantees stability within the vehicle platoon and stability of the platoon can be obtained.

[0028] The present invention also provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the steps of the above method.

[0029] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0030] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0031] Compared with the prior art, the present invention has the following advantages and technical effects:

[0032] The probabilistic ensemble trajectory sampling algorithm proposed in this invention comprehensively considers vehicle fuel efficiency and passenger comfort by combining the advantages of reinforcement learning and model predictive control. Compared with existing algorithms, the main advantages of this algorithm are: ① While optimizing the tracking error of the lead vehicle and the control input, it further optimizes vehicle fuel efficiency and passenger comfort; ② The following vehicles can track the lead vehicle through a distributed control protocol, which not only optimizes the tracking error, control input, fuel efficiency, and passenger comfort of the entire vehicle, but also reduces the computational complexity of the algorithm.

[0033] This invention proposes a fast solution method for distributed linear feedback control parameters. The control parameters that meet the requirements can be obtained by directly substituting them into the formula. Its main advantage is that it can avoid the defects of high computational complexity and inability to meet the real-time effectiveness of the solution in existing methods. Attached Figure Description

[0034] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0035] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0036] Figure 2 This is a schematic diagram of a simulation test scenario according to an embodiment of the present invention;

[0037] Figure 3 This is a block diagram of a vehicle simulation model according to an embodiment of the present invention;

[0038] Figure 4 The simulation results for Example 1 of this invention are shown in (a) for vehicle spacing error, (b) for vehicle speed error, and (c) for vehicle acceleration error.

[0039] Figure 5 The simulation results for Example 2 of this invention are shown in (a) for vehicle spacing error, (b) for vehicle speed error, and (c) for vehicle acceleration error.

[0040] Figure 6 The simulation results for Example 3 of this invention are shown in (a) for vehicle spacing error, (b) for vehicle speed error, and (c) for vehicle acceleration error.

[0041] Figure 7 The simulation results for Example 4 of this invention are shown in (a) for vehicle spacing error, (b) for vehicle speed error, and (c) for vehicle acceleration error.

[0042] Figure 8 This is a comparison chart of fuel consumption at different reference speeds according to an embodiment of the present invention;

[0043] Figure 9 This is a block diagram of the following control system for vehicle i according to an embodiment of the present invention. Detailed Implementation

[0044] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0045] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0046] Example 1

[0047] like Figure 1 As shown, this embodiment provides a heterogeneous vehicle platoon cooperative cruise control method oriented towards fuel economy and comfort, including:

[0048] (a) Reinforcement learning cross-entropy probabilistic ensemble trajectory sampling algorithm;

[0049] ① Establish a fuel consumption and ride comfort optimization model;

[0050] The third-order discrete-time state-space equation of vehicle i is as follows:

[0051]

[0052] Where, pi ,v i ,a i ,u i Let τ be the position, velocity, acceleration, and control input of the i-th vehicle. i T represents the actuator lag time constant and sampling period of vehicle i, respectively.

[0053] Vehicle fuel consumption rate (FuelC) i The formula is shown below:

[0054]

[0055] Among them, b0, b1, b2, b3 and c0, c1, c2 are fuel consumption parameters.

[0056] The ride comfort function is:

[0057]

[0058] Among these, acceleration and velocity should be limited to a reasonable range.

[0059] a min ,a max These are the lower and upper limits of the desired acceleration, v. min ,v max These are the minimum and maximum speeds allowed, respectively.

[0060] Let x i (k+1)=[p i (k+1),v i (k+1),a i (k+1)] T The state-space equation for vehicle i can be transformed into:

[0061] x i (k+1)=A i x i (k)+B i u i (k) (4)

[0062] in,

[0063] The cost function of model predictive control for the lead vehicle can be expressed as:

[0064]

[0065] Where N is the prediction time domain, x desire(k) represents the desired target state, Q is a matrix consisting of the weights of position, velocity, and acceleration, and w1, w2, and w3 are the weights of state, control input, fuel consumption, and ride comfort.

[0066] Define the reward function as follows:

[0067] R = -J (6)

[0068] Therefore, the optimization model for fuel consumption and passenger comfort of the lead vehicle is as follows:

[0069]

[0070] ② The reinforcement learning cross-entropy probabilistic ensemble trajectory sampling algorithm is used to solve for the optimal control input;

[0071] In this invention, the environmental dynamics model is known, so the Model Predictive Control (MPC) framework from the PETS algorithm is adopted, and the control sequence is optimized using the Cross-Entropy (CEM) method, thereby realizing a model-based reinforcement learning process. The specific algorithm solution process is as follows:

[0072]

[0073]

[0074] (ii) Distributed linear feedback control protocol;

[0075] ① Control protocol derivation;

[0076] The expected distance d of vehicle i i,des (k) is:

[0077] d i,des (k)=v i (k)·h i (8)

[0078] Among them, h i It is a positive time constant.

[0079] Distance error e between vehicle i and the vehicle in front ip (k), velocity error e iv (k) and acceleration error e ia (k) can be described as:

[0080]

[0081] Where, p i-1 (k) represents the rear of the (i-1)th vehicle. L i Let be the length of the i-th vehicle.

[0082] The distributed linear feedback control protocol for vehicle i is as follows:

[0083] u i (k)=k ip e ip (k)+k iv e iv (k)+k ia e ia (k) (10)

[0084] Where, k ip ,k iv ,k ia It is the distance error e ip (k), velocity error e iv (k) and acceleration error e ia The control gain of (k).

[0085] According to the control protocol in formula (10), the block diagram of the following control system of vehicle i is as follows: Figure 9 As shown.

[0086] Next, we need to use the frequency domain analysis method for discrete systems to solve for the control gain k that can guarantee the stability of the vehicle platoon and the series stability. ip ,k iv ,k ia .

[0087] ② Find the transfer function;

[0088] Applying the Z-transform to equation (1) yields:

[0089]

[0090] The open-loop transfer function and closed-loop transfer function of vehicle i are as follows:

[0091]

[0092] in,

[0093] ③ Solve for the range of control gain that ensures stability inside the vehicle;

[0094] After obtaining the open-loop transfer function of the vehicle i-following control system, the sufficient condition for the internal stability of the following control system is obtained using the Nyquist stability criterion. Let... Substitute T io (z) yields:

[0095]

[0096] Let ω = jυ, then the open-loop transfer function T io The frequency response of (z) is:

[0097]

[0098] in,

[0099]

[0100] T io The phase angle of (jυ) is:

[0101]

[0102] in,

[0103]

[0104] Let C4 = 0, that is:

[0105] 2Tk ia =T 2 k ip h i +T 2 k iv (16)

[0106] Then equation (15) can be simplified to:

[0107]

[0108] Phase angle φ i The first and second derivatives of (jυ) are as follows:

[0109]

[0110] in,

[0111] Note make have to:

[0112] h i >T (19)

[0113] Once the conditions of equations (16) and (19) are satisfied, it can be known that C3 is positive. Let C2 < 0, then:

[0114]

[0115] Since C2 < 0 and C3 > 0, we can conclude that:

[0116]

[0117] Noting that C0 > 0, C1 > 0, C2 < 0 and C3 > 0, we obtain C o1 <0, therefore First it was negative, then positive, and because... and Therefore, as υ increases from 0 to positive infinity, φ... i (jυ) first increases monotonically and then decreases monotonically. Because and There is only one positive υ such that φ(jυ) = -π.

[0118] Then T io The amplitude of (jυ) is:

[0119]

[0120] Next, we will analyze A. mi The monotonicity of (jυ). Due to A mi The monotonicity of (jυ) is equivalent to The monotony makes get:

[0121]

[0122] Formula (23) is equivalent to:

[0123]

[0124] in,

[0125] We can obtain:

[0126]

[0127] in, for A sufficient condition is that all coefficients of υ are negative, which gives:

[0128]

[0129] From 32H6-16H4(2τ) i -T) 2 -6H2(2τ i -T) 4 <0 yields:

[0130]

[0131] in, The sufficient condition for the inequality to hold is:

[0132]

[0133] Therefore, we can conclude that:

[0134]

[0135] From H2 > 0, we get:

[0136]

[0137] The sufficient condition for the inequality to hold is:

[0138]

[0139] ④ Determine the range of control gain that ensures the stability of the vehicle platoon;

[0140] Using bilinear transformation Substituting the closed-loop transfer function T ic (z)

[0141]

[0142] Let ω = jυ, then the closed-loop transfer function T ic The frequency response of (z) is:

[0143]

[0144] in, The sufficient condition for the series stability of a control system is:

[0145]

[0146] Due to formula (33), |T ic (jυ)| 2 It can be simplified to:

[0147]

[0148] By a i >0, if b i >0 It can be guaranteed.

[0149] And because:

[0150] b i =B 6i υ 6 +B 4i υ 4 +B 2i υ 2 (36)

[0151] in, Get b i A sufficient condition for >0 is B. 6i >0, B 4i >0 and B 2i>0, therefore, we can conclude that:

[0152]

[0153] ⑤ Effective control gain k ip ,k iv ,k ia scope;

[0154] Based on the analyses in ③ and ④ above, the final effective control gain k can be obtained. ip ,k iv ,k ia The scope is as follows:

[0155]

[0156] The process begins with inputs k1 = 0, N, and the initial state x. i Length L i Actuator hysteresis time constant τ i The parameters such as sampling period T and total running time t are then processed in parallel: ① The optimal control input u0 is solved by establishing an optimization model for the leader vehicle (7) and using the probabilistic integrated trajectory sampling algorithm (Algorithm 1); ② The control gain parameter of the following vehicle is first calculated by formula (39), and then u is obtained by combining the tracking error. i Then, through the state equation x i =A i x i +B i u i Update vehicle status (both the lead vehicle and following vehicles update their respective vehicle status according to the state equation), increment the iteration counter k1 by 1, and the process ends when k1>t / T, otherwise return to the loop to calculate control input and update status.

[0157] To demonstrate the effectiveness of the above method, consider a heterogeneous convoy of 5 vehicles, with initial vehicle states as shown in Table 1. A vehicle simulation system is built using the professional vehicle simulation software Prescan. Figure 2 The experimental environment shown is a road with traffic lights, and a Simulink-based system is built as follows: Figure 3 The vehicle dynamics model shown.

[0158] Table 1

[0159]

[0160] Example 1: The vehicle queue parameters are shown in Table 2, and the simulation results are as follows. Figure 4 As shown.

[0161] Table 2

[0162]

[0163] Example 2, the vehicle queue parameters are shown in Table 3, and the simulation results are as follows. Figure 5 As shown.

[0164] Table 3

[0165]

[0166] Example 3, the vehicle queuing parameters are shown in Table 4, and the simulation results are as follows. Figure 6 As shown.

[0167] Table 4

[0168]

[0169] ④ Example 4, the vehicle queue parameters are shown in Table 5, and the simulation results are as follows. Figure 7 As shown.

[0170] Table 5

[0171]

[0172]

[0173] Furthermore, to verify the effectiveness of the proposed fuel optimization strategy, multiple reference speed scenarios were set up, and the fuel consumption was compared and analyzed with that under traditional control strategies, such as... Figure 8 As shown.

[0174] Depend on Figure 4-7 It can be observed that when different parameters are set, the method proposed in this embodiment can still ensure the stability of the vehicle platoon, while the acceleration error is controlled within [-1m / s²]. 2 1m / s 2 During this period, passenger comfort can be effectively guaranteed. Meanwhile, the figure shows the fuel saving rate at different reference speeds. When the reference speed is set to 5m / s, 10m / s, 14m / s, 15m / s, and 18m / s, fuel consumption is reduced by 2.94%, 6.70%, 7.89%, 7.52%, and 6.36% respectively under the strategy proposed in this embodiment. Therefore, the proposed fuel optimization strategy can significantly improve fuel economy.

[0175] In existing vehicle platooning control research, on the one hand, many distributed model predictive control methods rely on low-precision models, making it difficult to effectively handle nonlinear and fuel optimization problems. Furthermore, nonlinear model predictive control has high computational complexity and is difficult to implement. On the other hand, some studies considering fuel economy are based on continuous systems or simplified second-order models, which struggle to accurately characterize the real dynamics of vehicles, resulting in limited control effectiveness and making them unsuitable for application in control systems. Therefore, this invention proposes a lead vehicle model predictive control strategy considering ride comfort and fuel consumption, as well as a following control protocol based on distributed linear feedback control. The main objectives of this invention are as follows:

[0176] (1) Considering the impact of ride comfort and fuel consumption on the model predictive control of the lead vehicle, a constrained model predictive control optimization problem is established, which is solved by minimizing fuel consumption, optimal comfort, and minimizing the weighted sum of tracking errors. To quickly solve the model predictive control optimization problem, this invention proposes a probabilistic ensemble trajectory sampling algorithm (Cross-Entropy Reinforced PETS) based on a reinforcement learning cross-entropy framework, thereby achieving optimal fuel consumption and ride comfort for the entire vehicle platoon.

[0177] (2) By using the third-order vehicle state space equation and the discrete frequency domain analysis method, the distributed linear feedback control protocol can be solved quickly, avoiding the problems of high computational complexity and difficulty in guaranteeing the effectiveness of the solution in the existing solution method. At the same time, the internal stability and string stability of the vehicle queue are also realized.

[0178] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A heterogeneous vehicle platoon cooperative cruise control method oriented towards fuel economy and comfort, characterized in that, Includes the following steps: A third-order discrete-time state-space equation for the vehicle is established, and a model predictive control optimization model for the leader vehicle in a heterogeneous vehicle platoon is constructed based on the third-order discrete-time state-space equation for the vehicle. Solve the model predictive control optimization model to obtain the optimal control input for the lead vehicle; A distributed linear feedback control protocol is constructed and solved using discrete system frequency domain analysis methods to obtain the control gain that ensures the stability of the vehicle queue and the series stability. The control input of the following vehicle is obtained based on the control gain. Based on the control input of the following vehicle and the optimal control input of the lead vehicle, the heterogeneous vehicle platoon cooperative cruise control is realized.

2. The heterogeneous vehicle platoon cooperative cruise control method for fuel economy and comfort as described in claim 1, characterized in that, The third-order discrete-time state-space equation of the vehicle is: Where, p i ,v i ,a i ,u i Let τ be the position, velocity, acceleration, and control input of the i-th vehicle. i T represents the actuator lag time constant and sampling period of vehicle i, respectively.

3. The heterogeneous vehicle platoon cooperative cruise control method for fuel economy and comfort as described in claim 1, characterized in that, The cost function of the model predictive control optimization model for the lead vehicle in a heterogeneous vehicle platoon is: Where N is the prediction time domain, x desire (k) represents the desired target state, a comfort For passenger comfort, FuelC0 represents the fuel consumption rate of the lead vehicle, x i (k) is the transpose of the matrix consisting of the position, velocity, and acceleration of the i-th vehicle, u i (k) represents the control input for vehicle i, Q is a matrix composed of the weights of position, speed, and acceleration, and w1, w2, and w3 are the weights of control input, fuel consumption rate, and ride comfort, respectively.

4. The heterogeneous vehicle platoon cooperative cruise control method for fuel economy and comfort according to claim 3, characterized in that, The probabilistic ensemble trajectory sampling algorithm, based on an improved reinforcement learning cross-entropy framework, solves the model predictive control optimization model. The process includes: Initialize the probability distribution parameters of the control sequence; generate multiple candidate control sequence samples within the constraints of control input and speed; during the forward simulation, calculate the cumulative reward for each sample step by step in the prediction time domain based on the known environmental dynamics model and cost function; select the elite sample set with the highest cumulative reward, update the probability distribution parameters based on the elite sample set, and define a new sampling distribution; repeat the above process for iterative optimization until the convergence condition is met, and output the mean parameter of the optimal control sequence as the optimal control input for the leader vehicle.

5. The heterogeneous vehicle platoon cooperative cruise control method for fuel economy and comfort according to claim 2, characterized in that, Vehicle fuel consumption rate (FuelC) i The formula is: Among them, b0, b1, b2, b3 and c0, c1, c2 are fuel consumption parameters.

6. The heterogeneous vehicle platoon cooperative cruise control method for fuel economy and comfort according to claim 1, characterized in that, The distributed linear feedback control protocol is as follows: u i (k)=k ip e ip (k)+k iv e iv (k)+k ia e ia (k); Where, k ip ,k iv ,k ia These are the distance errors e ip (k), velocity error e iv (k) and acceleration error e ia The control gain of (k).

7. The heterogeneous vehicle platoon cooperative cruise control method for fuel economy and comfort according to claim 1, characterized in that, The process of constructing a distributed linear feedback control protocol and solving it using discrete system frequency domain analysis methods to obtain the control gain that guarantees the stability within the vehicle platoon and the series stability includes: Substituting the distributed linear feedback control protocol into the third-order discrete state-space equation of the following vehicle, the open-loop transfer function and closed-loop transfer function are derived through Z-transform. The frequency response of the open-loop transfer function is analyzed using the Nyquist stability criterion to obtain a constraint inequality for the control gain that ensures the stability of the vehicle's internal structure; the amplitude-frequency characteristics of the closed-loop transfer function are constrained to derive a constraint inequality for the control gain that ensures the stability of the vehicle platoon. By simultaneously solving the constraint inequality of the control gain that ensures stability within the vehicle and the constraint inequality of the control gain that ensures stability of the vehicle platoon, the control gain that guarantees stability within the vehicle platoon and stability of the platoon can be obtained.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-7.

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