A connected vehicle cooperative control method based on road network traffic information

Abstract

The application provides a networked vehicle cooperative control method based on road network traffic information, and relates to the technical field of networked vehicle cooperative control, and comprises the following steps: establishing a vehicle dynamics model and a road network traffic flow model; at the upper intersection control layer, designing a consistency controller based on a Lagrange traffic flow model, adjusting the expected speed of an approaching vehicle platoon according to the traffic density information of adjacent intersections, so that the traffic density entering each intersection is consistent, and determining the upper limit of the communication time delay based on a system time-delay differential equation to ensure the consistency of the road network; at the lower tracking control layer, based on the vehicle dynamics model, using a leader-follower communication topology, designing a distributed tracking controller for a follower vehicle, so that the follower vehicle tracks the expected speed of a leader vehicle at an expected inter-vehicle distance, and determining the upper limit of the communication time delay based on a subsystem characteristic equation to ensure the stability of the vehicle platoon; and verifying the internal stability of the vehicle platoon and the stability of the platoon.

Description

Technical Field

[0001] This invention relates to the technical field of cooperative control of connected vehicles, and more particularly to a cooperative control method for connected vehicles based on road network traffic flow information. Background Technology

[0002] As an important branch of intelligent transportation systems, vehicle platooning systems have been explored and studied for decades. Studies have shown that platooning can effectively increase road throughput, alleviate traffic pressure, reduce the probability of traffic accidents, reduce harmful gas emissions, improve the environment and reduce fuel consumption[1]. As a result, a large number of control strategies have been developed, such as PID control, sliding mode control, linear feedback control, model predictive control, neural network control, etc. H Cong et al.[2] proposed a robust vehicle platooning control method based on MPC, which can track the vehicle in front under acceleration constraints and parameter uncertainties while ensuring safety and resource management; Reference [3] proposed a nonlinear control algorithm that considers the interaction between vehicles; Reference [4] proposed a nonlinear control algorithm based on a novel adaptive backstepping method, which effectively copes with dynamic uncertainties and external disturbances; Reference [5] designed a robust controller for platooning with uncertain communication delays; Reference [6] designed a platooning controller with time delay and analyzed the platooning stability conditions; Reference [7] proposed a CACC and ACC switching control strategy based on sliding mode control for heterogeneous platooning under the influence of communication interruption and time delay.

[0003] It is worth noting that in most existing studies, the speed of the lead vehicle in the vehicle queue is set manually and lacks practicality. Autonomous Intersection Management (AIM) can plan the speed trajectory of approaching vehicles, avoid collisions, and coordinate road network traffic by sharing traffic information with adjacent intersections. Dresner proposed an AIM strategy to replace traditional intersection signal control[8]. Torsten implemented an AIM strategy for a single intersection[9]. Hausknecht extended the AIM control strategy to multiple intersections

[10] . Since then, a large number of studies related to AIM have been proposed, such as the literature

[11] which allows optimization of entering and exiting lanes to significantly improve traffic; in the literature

[12] , the AIM system is responsible for controlling the speed of vehicles to achieve a better balance of traffic flow between intersections.

[0004] References

[0005] [1]M. R. Hidayatullah, J. C. Juang. Centralized and DistributedControl Framework Under Homogeneous and Heterogeneous Platoon. in IEEEAccess, vol. 9, pp. 49629-49648, 2021. [2]H Cong, S Quan, D Weiping, et al. A robust MPC approach forplatooning control of automated vehicles with constraints on acceleration[J].Control Engineering Practice, 139, 2023. [3]L Haifeng, C Zhenping, F Baochuan, et al. Nonlinear Control ofHeterogeneous Vehicle Platoon with Time-varying Delays and LimitedCommunication Range[J]. International Journal of Control, Automation andSystems, 21(6), 2023. [4]F Jianbo, H Liyang, W Yanxue, et al. Backstepping method basedcontroller design for third-order truck platoon robust to dynamic uncertaintyand external disturbance[J]. Journal of Mechanical Science and Technology, 37(3), 2023. [5]J Ploeg, H Xing, H Nijmeijer. Robust CACC in the presence ofuncertain delays[J]. IEEE Transactions on Vehicular Technology, 71(4): 3507-3518, 2022. [6]Y Liu, H Gao, C Zhai, et al. Internal stability and stringstability of connected vehicle systems with time delays[J]. IEEE Transactionson Intelligent Transportation Systems, 22(10): 6162-6174, 2021. [7]G Guo, J Kang, H Lei, et al. Finite-time stabilization of acollection of connected vehicles subject to communication interruptions[J].IEEE Transactions on Intelligent Transportation, 20(4): 101-109, 2021. [8]K Dresner, P Stone. A multiagent approach to autonomousintersection management[J]. Journal of Artificial Intelligence Research, 31(1): 591- 656, 2008. [9]B Torsten, T Ansgar, “Kreuzungmanagement: Trajektorienplanungmittels Dynamischer Programmierung,” Automatisierungtechnik,OldenbourgWissenchaftverlag, pp. 253-261, 2009.

[10] M Hausknecht, CT Au, P Stone. Autonomous intersectionmanagement: multi-intersection optimization[C] / / International Conference onIntelligent Robots and Systems. San Francisco: IEEE, 2011: 4581-4586

[11] W. Wu, Y. Liu, W. Liu, F. Zhang, V. Dixit and ST Waller, "Autonomous Intersection Management for Connected and Automated Vehicles: ALane-Based Method," in IEEE Transactions on Intelligent TransportationSystems, vol. 23, no. 9, pp. 15091-15106, 2022.

[12] C. Wuthishuwong, A. Traechtler, “Consensus-based local information coordination for the networked control of the autonomous intersection management,” Complex Intell. Syst., vol. 3, pp. 1–16, 2016. Summary of the Invention In view of the technical problems mentioned in the background section, a method for cooperative control of connected vehicles based on road network traffic flow information is provided.

[0006] The technical means employed in this invention are as follows: A network-connected vehicle cooperative control method based on road network traffic flow information adopts a hierarchical control framework, including an upper-level intersection control layer and a lower-level vehicle queue tracking control layer. The method includes the following steps: Step 1: Establish vehicle dynamics model and road network traffic flow model; Step 2: In the upper-level intersection control layer, a consistency controller is designed based on the Lagrange traffic flow model. The expected speed of the approaching convoy is adjusted according to the traffic density information of adjacent intersections to make the traffic density of each intersection consistent. The upper bound of the communication delay to ensure the consistency of the road network is determined based on the system's time-delay differential equation. Step 3: In the lower tracking control layer, based on the vehicle dynamics model, a leader vehicle-leader vehicle following communication topology is adopted. A distributed tracking controller is designed for the following vehicles so that the following vehicles track the leader vehicle's desired speed at the desired vehicle spacing. The upper bound of the communication delay to ensure the stability of the vehicle platoon is determined based on the subsystem characteristic equation. Step 4: Verify the internal stability and queue stability of the vehicle queue.

[0007] Furthermore, the consistency controller is designed based on a control protocol that includes road network communication delay. The upper bound of the communication delay that guarantees road network consistency is determined by analyzing the system's delay-containing differential equations through Laplace transform and combining the Laplace matrix.

[0008] Furthermore, the Lagrange traffic flow model is an LWR traffic flow model, in which the traffic speed state is linear at low density and parabolic at high density, and the traffic density state is parabolic at low density and linear at high density.

[0009] Furthermore, the road network comprises multiple autonomous intersection managers, each corresponding to an intersection and having multiple inbound and outbound traffic flows. Adjacent intersections communicate traffic information through roadside units.

[0010] Furthermore, the tracking controller is a distributed PID controller, whose control input is determined based on the position error, speed error, and acceleration error between the following vehicle and the lead vehicle in the vehicle platoon, and the control input includes the vehicle platoon communication delay.

[0011] Furthermore, based on the feedback linearization method, the first The third-order longitudinal dynamics model of the following vehicle is as follows: ; ; ; in, Indicates the vehicle's dynamic inertial delay. Indicates the system's control input, , and They represent the first The position, velocity, and acceleration of the following vehicle; definition For the first The state vector of the following vehicle, then the first... The dynamic state-space expression of the following vehicle is: ; in, ; ; Definition of the first The tracking error between the following vehicle and the lead vehicle is: ; ; ; in, , and They represent the first The position, speed, and acceleration errors of the following vehicles and the lead vehicle; Indicates the first The desired distance between the following vehicle and the lead vehicle is determined, and a constant distance strategy is adopted. .

[0012] Furthermore, the verification of internal stability includes: decomposing the vehicle queuing system into multiple subsystems through a similarity transformation, judging the stability of each subsystem using the Routh-Hurwitz stability criterion, and determining the internal stability of the vehicle queuing system when all subsystems are Hurwitz stable.

[0013] Furthermore, the verification of queue stability includes: establishing an error transfer function between the following vehicle and the lead vehicle, analyzing the gain of the error transfer function, and determining that the vehicle queue system satisfies queue stability when the gain is less than 1.

[0014] Compared with the prior art, the present invention has the following advantages: This invention, taking into account the communication delay of the road network and vehicle queue, designs two controllers in a hierarchical control framework based on consistency theory, traffic flow theory, and feedback control theory. This aims to achieve traffic flow balance of the road network in the upper control framework and vehicle speed tracking and distance maintenance in the lower control framework, and provides a demonstration of stability.

[0015] This invention employs the LWR traffic flow model to plan the desired speed and spacing of the lead vehicle based on the balanced traffic density of the road network, ensuring stable vehicle platooning. The platoon stability of the system under time delay conditions is proven, and bounds for the road network communication delay and vehicle platoon communication delay that ensure system stability are obtained. Finally, numerical simulation experiments are designed to verify the effectiveness of the control scheme. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the layered control of the present invention.

[0018] Figure 2 This is a schematic diagram of the road network equivalent model of the present invention.

[0019] Figure 3 This is a schematic diagram of the LPF communication topology vehicle queue of the present invention.

[0020] Figure 4 In this embodiment of the invention, the total traffic density is defined as the number of vehicles entering an intersection without communication delay between adjacent intersections.

[0021] Figure 5 As described in the embodiments of the present invention The total traffic density entering the intersection at that time.

[0022] Figure 6 This illustrates the relationship between density and time in the LWR traffic flow model during a certain time period, as presented in this embodiment of the invention.

[0023] Figure 7 This illustrates the relationship between traffic flow speed and traffic density in an embodiment of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] like Figures 1-7 As shown, this invention provides a networked vehicle cooperative control method based on road network traffic flow information, employing a hierarchical control framework, including: an upper-level intersection control layer and a lower-level vehicle queue tracking control layer. For example... Figure 1 This is a diagram of a network-based vehicle cooperative control framework. In the upper-level control, an intersection manager based on the Lighthill-Whitham-Richards (LWR) traffic flow model adjusts the speed of approaching convoys to balance traffic flow between intersections and ensure network stability. In the lower-level control framework, vehicles traveling on urban roads... Vehicle platooning control ensures the convoy follows the lead vehicle steadily at the same speed and given intervals. In a city, roads intersect to form a road network. Edges and nodes in the road network consist of "streets" and "intersections." The road network studied in this paper consists of nine intersections, each with four inbound traffic flows and four outbound traffic flows. The Autonomous Intersection Manager (AIM) is considered an intelligent centralized controller where every two intersections connected by streets can communicate. The equivalent model of the road network is as follows: Figure 2 As shown.

[0027] A road network model was established using graph theory to characterize the interconnections between road networks. A graph consists of a set of nodes and a set of edges, defined as follows: ,in It is an undirected graph. Represents a set of nodes, and each node is defined as follows: , , Denotes the set of edges and Each edge is defined as , Adjacency matrix , This indicates communication between intersections, where Indicates an intersection and intersections They must be adjacent, meaning that communication is possible between the two intersections; otherwise... Undirected graph The degree matrix is ​​defined as , .

[0028] Based on the mechanism of traffic information communication between adjacent intersections in a road network, the density of vehicles entering and exiting intersections is calculated using roadside units. Traffic state is defined based on the total density of vehicles entering the intersection. (1); in, Indicates entering the The number of lanes at each intersection. It is worth noting that since vehicles exiting an intersection are generally considered as entering other intersections, the traffic density exiting the intersection is not considered in equation (1). Therefore, the dynamic equation for the intersection is shown in equation (2): (2); in, It is a control protocol based on local information feedback.

[0029] The method includes the following steps: Step 1: Establish vehicle dynamics model and road network traffic flow model; Step 2: In the upper-level intersection control layer, a consistency controller is designed based on the Lagrange traffic flow model. The expected speed of the approaching convoy is adjusted according to the traffic density information of adjacent intersections to make the traffic density of each intersection consistent. The upper bound of the communication delay to ensure the consistency of the road network is determined based on the system's time-delay differential equation. Step 3: In the lower tracking control layer, based on the vehicle dynamics model, a leader vehicle-leader vehicle following communication topology is adopted. A distributed tracking controller is designed for the following vehicles so that the following vehicles track the leader vehicle's desired speed at the desired vehicle spacing. The upper bound of the communication delay to ensure the stability of the vehicle platoon is determined based on the subsystem characteristic equation. Step 4: Verify the internal stability and queue stability of the vehicle queue.

[0030] intersection Entering the intersection Traffic density, traffic flow, and traffic speed in this direction are defined as follows: , and According to traffic flow theory, the relationship between the three is as shown in equation (3): (3); The LWR traffic flow model describes the changes in traffic conditions over time, utilizing time... Coordinate system This is the cumulative count of vehicles. The traffic state variable in the LWR model is the inter-vehicle spacing. Expected traffic speed .

[0031] (4); Traffic speed exhibits a linear pattern at low density and a parabolic pattern at high density; traffic density exhibits a parabolic pattern at low density and a linear pattern at high density. Therefore: (5); in, Represents the average free flow velocity. Indicates the critical velocity. Indicates the critical density. This indicates the blockage density.

[0032] Consider a A convoy of vehicles, such as Figure 3 As shown, the lead vehicle and the following vehicles are represented by 0 and 1, 2, 3… respectively. The vehicle platoon communicates using a Leader-Predecessor-Following (LPF) topology, where arrows indicate information exchange between vehicles. Compared to other unidirectional communication topologies, platoons using the LPF topology exhibit greater platoon stability.

[0033] Based on the feedback linearization method, the first The third-order longitudinal dynamics model of the following vehicle is as follows: (6) in, Indicates the vehicle's dynamic inertial delay. This represents the system's control input. , and They represent the first The position, velocity, and acceleration of the following vehicle. Definition For the first The state vector of the following vehicle, then the first... The dynamic state-space expression of the following vehicle is: (7); in, ; ; Definition of the first The tracking error between the following vehicle and the lead vehicle is: (8) (9) (10) in, , and They represent the first Errors in position, speed, and acceleration between the following vehicle and the lead vehicle. Indicates the first The desired distance between the following vehicle and the lead vehicle is determined, and a constant distance strategy is adopted. .

[0034] The goal of connected vehicle platoon control is to ensure that the platoon follows the lead vehicle stably at the same speed and given spacing. This application establishes a hierarchical vehicle platoon control framework, considering communication latency, and utilizes traffic flow theory to plan the desired speed and spacing for the platoon based on the traffic density at equilibrium in the upper-level road network. A controller is designed to achieve the following objectives: The traffic density entering each intersection reaches a uniform level, that is: (11) (2) (Internal Stability) The spacing error, speed error, and acceleration error between any vehicle in the convoy and the lead vehicle approach zero over time, that is: (12) (3) (Queue Stability) Vehicle Queue Spacing Error Transfer Function satisfy (13) To ensure that the traffic density entering each intersection is consistent, i.e., to satisfy equation (11), a consistency controller is designed in the upper-level control framework as follows: (14) in, This is a communication delay parameter between intersections. (Definition) Combining the Laplace matrix, it can be expressed as ,in It is a picture The corresponding Laplace matrix.

[0035] Substituting the time-delay controller equation (14) into the system dynamic equation (2), we obtain the following system differential equation with time delay: (15) By performing a Laplace transform on both sides of equation (15), we can obtain: (16) Combining the Laplace matrix, we have (17) in It is the transfer function related to the edge. In equation (17) The initial state of the system (considered as input to the state equations), If we consider this as the final state (as the output of the state equation), then according to the definition of the transfer function, the ratio of the two values ​​is the system's transfer function: (18) Based on the stability condition of a linear system, the transfer function The poles are all strictly located in the left half-plane or satisfy the following conditions: .definition Then find The extreme point is equivalent to finding The zero point. Let the Laplace matrix... eigenvalues The corresponding feature vector is For a connected graph ,matrix The eigenvalues ​​are related .

[0036] First of all hour, Therefore, in Direction Time system transfer function One of the extremes. When When, assume other poles are ,Right now To facilitate analysis, we will use matrices. eigenvectors At this point, equation (18) holds true.

[0037] (19) because ,but satisfy To facilitate the use of the Nyquist stability criterion, both sides are divided by... As shown in equation (20), the transformation is as follows: (20) make Then we have: (twenty one) Multiplying the left and right sides of equation (21) respectively, we obtain the following matrix differential equation. (twenty two) According to Euler's formula Equation (22) can be written as: (twenty three) because Assuming Equation (23) can be transformed into: (twenty four) Both terms on the left side of equation (24) are positive semi-definite. The equation holds if and only if both terms are zero, that is: (25) So, (26) in, It is the Laplace matrix The largest eigenvalue.

[0038] (27) Since the root of equation (20) is in Continuous dependencies, and except time Furthermore, all the zeros of this equation lie in the left half-plane of the open loop, and for all... The roots of equation (20) are all in the left half-plane of the open loop. Therefore, when the time delay satisfies condition (27), the controller (14) with time delay can achieve consistency.

[0039] The results obtained from equations (4) and (5) and The speed of the lead car and the spacing between convoy vehicles are represented, respectively. Design a distributed PID controller. This ensures that following vehicles maintain the desired speed and spacing with the lead vehicle, improving the stability and robustness of the vehicle convoy. Then the... The vehicle's controller can be written as: (28) in, , , and These are the position, speed, and acceleration gain of the vehicle queue distributed controller.

[0040] Considering the impact of communication delays between vehicles, (28) can be rewritten by combining equations (8), (9), and (10) as follows: (29) Combining equation (29) and equation (7), the state-space expression is obtained as follows: (30) in express 3D identity matrix and It is the first The tracking error state vector of the following vehicle. For the LPF topology, the Laplace matrix... eigenvalues , , . , It is the controller gain vector.

[0041] In the above state equation, the matrix dimension is... Direct stability analysis involves a large amount of computation. To reduce the difficulty, the system (30) is decomposed into Each subsystem. The decomposition process is as follows: Suppose there exists an invertible matrix Make the matrix It is a diagonal matrix, that is (31) in Construct the following similarity transformation: (32) At this point, system (30) can be represented as: (33) Due to the matrix , and Since they are all block matrices, then we have , , Then system (30) can be broken down into Subsystem: (34) in , . It is the first Communication latency of each subsystem.

[0042] Lemma 1: When communication delay When all eigenvalues ​​of the communication topology matrix are positive real numbers, and the controller gain satisfies the following condition, the matrix... It is Hurwitz stable.

[0043] (35) Proof: When Then, subsystem (34) can be written as: (36) matrix The characteristic equation is as follows: (37) According to the Routh-Hurwitz stability criterion, the Routh array is as shown in equation (38): (38) Obviously, when And when the controller parameters satisfy equation (35), the matrix It is Hurwitz stable. Proof complete.

[0044] Lemma 2: In an LPF communication topology with delay Under certain conditions, the vehicle queuing system guarantees queue stability.

[0045] (39) (40) Proof: Let , The characteristic equation of the subsystem is shown in equation (41) below: (41) Assumption It is a purely imaginary root of the characteristic equation (41), which can be obtained based on conjugate symmetry. It is also a purely imaginary root of equation (41), then equation (41) satisfies: (42) Eliminate exponential terms and ,get: (43) in It is the critical imaginary root of the vehicle queuing system. Substituting it into equation (41) yields... Based on the phase angle condition and the frequency periodicity of the time delay element, we can obtain: (44) Among them, the communication latency of the vehicle queuing system (30) It is the time delay intersection of the stable intervals of all subsystems in equation (34).

[0046] Substitute the controller model (29) into the first... Combining the following vehicle's tracking error with the lead vehicle's equations (8), (9), and (10) with equation (6), we can obtain: (45) From the definition of tracking error (8)(9)(10), we can see that: (46) Substituting the above two equations into equation (45), we get the following equation (47): (47) For the error term in (47) Performing the Laplace transform, we get: (48) in It is a state error The Laplace transform, let Then we have: (49) Based on the stability criterion ,but: (50) because , Substituting this into equation (50) and rearranging, we get: (51) Equation (51) holds if and only if all coefficients are positive, that is: (52) Rearranging equation (52) yields equation (40), which means that the vehicle queuing control system can guarantee queuing stability when it meets condition (40).

[0047] The effectiveness of the proposed control method was verified through numerical experiments. Here, the road network considered consists of nine adjacent intersections. Taking one intersection as an example, each intersection has four adjacent intersections that can communicate with each other. The traffic densities of this intersection and its adjacent intersections are assumed to be 10 (veh / km), 20 (veh / km), 25 (veh / km), 30 (veh / km), and 40 (veh / km), respectively. Figure 4The total traffic density at which vehicles enter the intersection without communication delay between adjacent intersections. Figure 5 for The total traffic density entering the intersection at that time. Figure 6 This is the relationship between traffic density and time in the LWR traffic flow model for a certain period of time. Figure 7 It is the relationship between traffic flow speed and traffic density.

[0048] The relevant parameter values ​​of the LWR traffic flow model in the simulation experiment are shown in Table 1.

[0049] Table 1. Relevant parameters of the LWR traffic flow model

[0050] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. In the above embodiments of the present invention, the descriptions of each embodiment have their own emphasis; parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. It should be understood that the disclosed technical content in the several embodiments provided in this application can be implemented in other ways.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for cooperative control of connected vehicles based on road network traffic flow information, characterized in that, A hierarchical control framework is adopted, comprising an upper-level intersection control layer and a lower-level vehicle queue tracking control layer. The method includes the following steps: Step 1: Establish vehicle dynamics model and road network traffic flow model; Step 2: In the upper-level intersection control layer, a consistency controller is designed based on the Lagrange traffic flow model. The expected speed of the approaching convoy is adjusted according to the traffic density information of adjacent intersections to make the traffic density of each intersection consistent. The upper bound of the communication delay to ensure the consistency of the road network is determined based on the system's time-delay differential equation. Step 3: In the lower tracking control layer, based on the vehicle dynamics model, a leader vehicle-leader vehicle following communication topology is adopted. A distributed tracking controller is designed for the following vehicles so that the following vehicles track the leader vehicle's desired speed at the desired vehicle spacing. The upper bound of the communication delay to ensure the stability of the vehicle platoon is determined based on the subsystem characteristic equation. Step 4: Verify the internal stability and queue stability of the vehicle queue.

2. The connected vehicle cooperative control method based on road network traffic flow information according to claim 1, characterized in that, The consistency controller is designed based on a control protocol that includes road network communication delay. The upper bound of the communication delay that guarantees road network consistency is determined by analyzing the system's delay differential equations through Laplace transform and combining the Laplace matrix.

3. The connected vehicle cooperative control method based on road network traffic flow information according to claim 1, characterized in that, The Lagrange traffic flow model is the LWR traffic flow model, in which the traffic speed state is linear at low density and parabolic at high density, and the traffic density state is parabolic at low density and linear at high density.

4. The connected vehicle cooperative control method based on road network traffic flow information according to claim 1, characterized in that, The road network consists of multiple autonomous intersection managers. Each autonomous intersection manager corresponds to one intersection and has multiple inbound and outbound traffic flows. Adjacent intersections communicate traffic information through roadside units.

5. The connected vehicle cooperative control method based on road network traffic flow information according to claim 1, characterized in that, The tracking controller is a distributed PID controller, and its control input is determined based on the position error, speed error and acceleration error between the following vehicle and the lead vehicle in the vehicle platoon. The control input includes the vehicle platoon communication delay.

6. The connected vehicle cooperative control method based on road network traffic flow information according to claim 1, characterized in that, Based on the feedback linearization method, the first The third-order longitudinal dynamics model of the following vehicle is as follows: ； ； ； in, Indicates the vehicle's dynamic inertial delay. Indicates the system's control input, , and They represent the first The position, velocity, and acceleration of the following vehicle; definition For the first The state vector of the following vehicle, then the first... The dynamic state-space expression of the following vehicle is: ； in, ； ； Definition of the first The tracking error between the following vehicle and the lead vehicle is: ； ； ； in, , and They represent the first Errors in position, speed, and acceleration between the following vehicle and the lead vehicle; Indicates the first The desired distance between the following vehicle and the lead vehicle is determined, and a constant distance strategy is adopted. .

7. The connected vehicle cooperative control method based on road network traffic flow information according to claim 1, characterized in that, The verification of internal stability includes: decomposing the vehicle queuing system into multiple subsystems through a similarity transformation, judging the stability of each subsystem using the Routh-Hurwitz stability criterion, and determining the internal stability of the vehicle queuing system when all subsystems are Hurwitz stable.

8. The connected vehicle cooperative control method based on road network traffic flow information according to claim 1, characterized in that, The verification of queue stability includes: establishing an error transfer function between the following vehicle and the lead vehicle, analyzing the gain of the error transfer function, and determining that the vehicle queue system satisfies queue stability when the gain is less than 1.

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