A driving control method for connected vehicles at intersections without signal lights considering input constraints

Abstract

The present invention discloses a method for controlling the travel of networked vehicles at an intersection without signal lights considering input constraints, so as to solve the challenge faced by autonomous driving vehicles in safely and cooperatively passing through an intersection without signal lights under input constraints. The method comprises the following steps: obtaining real-time status information and roadside environmental information of the vehicle; establishing a vehicle dynamics model considering input constraints; developing a geometric topological structure to model the traffic relationship of vehicles, and then defining a communication topology for information exchange between vehicles, and converting the intersection vehicle coordination problem into a consistency problem of conflicting vehicles through state transitions; designing a distributed control method to ensure the global consistency and stability of the vehicle cooperative control system; finally, proposing a time-based trigger mechanism to improve vehicle traffic efficiency and driving safety and reduce the operating load of the system.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation networked vehicle control technology, and in particular to a method for controlling a networked vehicle traveling at an intersection without signal lights taking input constraints into consideration. Background Art

[0002] As the global demand for smart transportation continues to grow, challenges related to managing traffic flows, ensuring road safety, and achieving environmental sustainability are becoming increasingly critical. In response to these growing concerns, connected autonomous vehicles have emerged as a transformative solution, with their implementation significantly enhancing road capacity, improving vehicle safety, and reducing fuel consumption. Urban intersections are key traffic bottlenecks where multiple vehicle flows converge, and effectively allocating road rights is a major challenge. Traditionally, intersection control relies on traffic lights, regulatory signs, and stop signs. Although many efforts have been made to develop advanced signal control strategies to optimize time planning, these approaches often result in unnecessary stops and idling, reducing traffic efficiency. In contemporary times, real-time connected environments deployed with V2X communication technology provide a promising alternative for efficient intersection traffic for autonomous vehicles. This advancement makes the coordination of autonomous vehicles between unsignalized intersections seamless, thereby improving driving safety and reducing energy consumption.

[0003] At present, the coordination control of autonomous vehicles at intersections without signal lights mainly focuses on heuristic methods and optimization-based methods. Heuristic methods mainly focus on collision avoidance, and usually use the first-come-first-served rule to determine the order of passage. This scheme requires the central control unit to communicate with vehicles frequently, which has a large amount of communication overhead and may even cause the risk of communication deadlock. The optimization-based method formulates the vehicle coordination problem as a multi-objective optimization problem. This method can not only avoid collisions, but also improve traffic fluidity and energy efficiency. However, issues such as ensuring fairness in traffic management and the huge computational complexity associated with these methods limit their practical application.

[0004] The above two methods analyze the cooperative control problem of vehicles passing through intersections, and rarely consider the influence of vehicle input constraints and external interference. Actual vehicles have bounded input constraints and are subject to external interference, which may lead to instability of the closed-loop control system. Therefore, a method for controlling connected vehicles at intersections without signal lights considering input constraints is proposed to solve the technical problem in the prior art that the vehicle dynamics are affected by input constraints, which causes the destruction of the stability of the closed-loop system in the actual application of the system and leads to unreasonable control schemes. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method for controlling the driving of a connected vehicle at an intersection without signal lights taking into account input constraints, aiming to solve the problem of coordinated control of connected autonomous driving vehicles passing through intersections and improve vehicle traffic efficiency.

[0006] The present invention adopts the following technical solution: a method for controlling the driving of a networked vehicle at an intersection without a signal light considering input constraints is provided, comprising the following steps:

[0007] Step S1: Acquire the real-time status information and roadside environment information of the autonomous driving vehicle, and accurately locate the autonomous driving vehicle in real time in combination with the positioning system;

[0008] Step S2: uploading the real-time status information of the autonomous driving vehicle and the roadside environment information obtained in step S1 to a central control unit (abbreviation: ICU) and an on-board control unit;

[0009] Step S3: establishing a dynamics model of an autonomous driving vehicle taking into account input constraints, performing intersection conflict analysis on the autonomous driving vehicle, and establishing a vehicle cooperative control topology structure;

[0010] Step S4: Establishing the cooperative driving goal of the autonomous driving vehicles: that is, by regulating each pair of conflicting vehicles, ensuring that the autonomous driving vehicles with the possibility of conflict pass through the intersection safely and avoid collision;

[0011] Step S5: Design a time-triggered controller design method under input saturation constraints to evaluate the cooperative driving control performance of the autonomous driving vehicle, especially under input constraints.

[0012] Further, in step S1, the real-time status information of the vehicle includes the speed, acceleration and position of the autonomous driving vehicle; the roadside environment information includes determining whether the autonomous driving vehicle has entered the communication range and whether the autonomous driving vehicle has reached the stop line of the intersection.

[0013] Furthermore, in step S3, an autonomous driving vehicle dynamics model considering input constraints is established, and a vehicle intersection conflict analysis is performed to establish a vehicle cooperative control topology structure, which specifically includes:

[0014] Step S31: adopting a second-order linear vehicle model considering input saturation constraints:

[0015] (Formula 1)

[0016] In the formula represents the displacement of the vehicle, represents the derivative of displacement, represents the speed of the vehicle, express The derivative of represents the control input, is defined as a saturation function where: The boundaries are as follows:

[0017] (Formula 2)

[0018] In the formula, represents the lower bound of the saturation function, represents the upper bound of the saturation function, when When <0, it indicates the vehicle deceleration behavior in the actual scene. When >0, it indicates the vehicle acceleration behavior in the actual scene;

[0019] According to Formula 1, the vehicle dynamics model can be expressed in the following state space form:

[0020] (Formula 3)

[0021] In the formula, For output, is the state variable, C and D are parameter matrices;

[0022] in, , , ;

[0023] Step S32: Performing traffic conflict analysis on the driving behavior of the autonomous driving vehicle at a typical intersection and establishing a traffic conflict diagram;

[0024] Step S33: Based on the traffic conflict graph analysis, a conflict relationship graph between the autonomous driving vehicles at the intersection is established;

[0025] Step S34: establishing a traffic relationship between the conflicting vehicles based on the conflict relationship analysis between the vehicles;

[0026] Step S35: Establishing traffic relations between vehicles, including constructing geometric topology and communication topology. The geometric topology is used to simulate the spatial arrangement of vehicles, and the communication topology is used to define information interaction between vehicles.

[0027] Further, in step S35, a communication topology model is established based on the geometric topology using a bidirectional channel as follows:

[0028] (Formula 4)

[0029] In the formula, is a collection of vehicles, is the flow direction of information, G is the flow characteristic of the model, which can be represented by the matrix A and the Laplace matrix L. , ,in:

[0030] (Formula 5)

[0031] (Formula 6)

[0032] Where: Represents the set of correlation matrices of the communication topology graph G, Represents the set of related Laplace matrices of the communication topology graph G, i,j∈V M ， They represent the nodes of the vehicle geometric topology graph, is the set of real numbers.

[0033] Further, step S4: establishing the autonomous driving vehicle cooperative driving control target, specifically:

[0034] Step S41: Control each pair of conflicting vehicles to ensure that the vehicles pass through the intersection safely and achieve the desired motion state. The specific control method is as follows:

[0035] Determine the relative positions of conflicting vehicles i and j in different entrance lanes, define a virtual lane, and map the conflicting vehicles to the virtual lane. The expected relative distance between vehicle i and vehicle j is:

[0036] (Formula 7)

[0037] Where: Indicates the length of the vehicle, represents the desired spacing, , is the vehicle speed, is the time interval;

[0038] (Formula 8)

[0039] Where: is the minimum safe distance between two vehicles. and Respectively represent vehicles i and vehicles j The safe distance from the stop line to the conflict point, represents the set of all vehicles that enter the communication range, The maximum safety compensation distance for vehicles in conflict within the intersection;

[0040] (Formula 9)

[0041] For vehiclesi and vehicles j The distance from the starting point to the stop line, .

[0042] Step S42: Formulate a control objective function and design a distributed controller.

[0043] (Formula 10)

[0044] Where: and Respectively represent vehicles i and vehicles j The above control objectives enable vehicles to pass through the intersection without changing speed or spacing, reduce the risk of collision, and promote smooth traffic flow.

[0045] Further, step S5 establishes a time-triggered controller design under input saturation constraint conditions to evaluate the cooperative driving control performance of the autonomous driving vehicle, specifically including:

[0046] Step S51: Performing stability analysis of the intelligent networked vehicle cooperative control system under input saturation constraint conditions;

[0047] Step S52: The control strategy based on time triggering is as follows:

[0048] = (Formula 11)

[0049] In the formula >0 and >0 is the controller gain, and It is a vehicle i and vehicles j The distance to the stop line, and It is a vehicle i and vehicles j speed;

[0050] make , , , , , ,

[0051] The vehicle dynamics model considering input saturation constraints can be transformed into the following form:

[0052] (Formula 12)

[0053] Where: is an M-dimensional identity matrix, and , represents the vehicle speed, represents the derivative of the vehicle displacement, represents the derivative of velocity, L is the Laplace matrix, is the displacement, is the speed; the above model can achieve the global consistency of the cooperative control system of networked vehicles by constructing the Lyapunov function.

[0054] The control objective described in Equation 10 is achieved by using the control protocol in Equation 11, thereby ensuring that the vehicle passes through the intersection safely and smoothly while achieving the desired motion state.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] Unlike most existing studies, which mostly ignore the intrinsic properties of intersection vehicle coordination systems, this paper proposes a consensus-based collaborative control method for connected vehicles traveling at unsignalized intersections by applying model transformation. This method can effectively solve the complexity of conflict relationships between autonomous driving vehicles at unsignalized intersections, and only redefines the controller gain when the topology changes, reducing the calculation process and improving system stability and robustness.

[0057] The present invention takes into account the dynamic characteristics of the vehicle in actual driving. By considering the input constraints of the vehicle, the upper and lower limits of the disturbance input are ensured, and the anti-interference ability of the control system is improved. This design can improve the stability of the control system and the safety and comfort of the coordinated driving of the vehicles, thereby ensuring the efficiency of traffic.

[0058] While taking traffic safety into consideration, the control method of the present invention enables the autonomous driving vehicle to pass through the intersection at a faster speed without requiring other vehicles to stop and wait for a long time, thereby improving the energy efficiency of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a schematic diagram of the traffic situation of a connected autonomous driving vehicle at an intersection without traffic lights;

[0060] Figure 2 It is a traffic conflict diagram of connected autonomous vehicles passing through intersections;

[0061] Figure 3 It is a flow chart proposed by the present invention;

[0062] Figure 4 It is a schematic diagram of the control strategy framework proposed by the present invention. DETAILED DESCRIPTION

[0063] The specific implementation of the present invention is described below in conjunction with the accompanying drawings so that those skilled in the art can better understand the present invention. It should be noted that for a typical four-way symmetrical intersection, such as Figure 1 As shown, each entrance includes a left-turn lane and a straight lane. Independently set right turns that do not conflict with other lanes are not within the scope of analysis. Vehicles turning left and going straight at the intersection are named L1, L2, L3, L4, L5, L6, L7, and L8. After the vehicles enter the communication area with a radius of R, the vehicles exchange real-time information through V2X communication. After entering the communication area, each vehicle does not change lanes or overtake, and the communication network has no delays and data packet loss. ICU refers to the central control unit located in the center of the intersection.

[0064] Figure 2 The figure shows the traffic conflict diagram between vehicles passing through the intersection. For example, L1 vehicle may conflict with L3, L6, L7, L8, L2 vehicle may conflict with L3, L4, L5, L8, and so on.

[0065] Figure 3 The system proposed by the method of the present invention is shown, including an environmental information acquisition module, a model module and a collaborative control module.

[0066] The environmental information acquisition module mainly includes acquiring the real-time status information of the autonomous driving vehicle and the roadside environmental information. The real-time status information of the vehicle specifically includes: vehicle speed, vehicle acceleration and vehicle position information; the roadside environmental information specifically includes: judging whether the vehicle enters the communication range and the stop line position.

[0067] The model module includes a whole vehicle model and a traffic environment model based on vehicle information and map information. Specifically, the whole vehicle model is a second-order dynamic model of the vehicle, and the traffic environment model includes the vehicle's position information, speed information and lane stop line position model.

[0068] The cooperative control system module includes two types: time-triggered cooperative control method and stability analysis. It refers to a vehicle cooperative traffic control method that uses Lyapunov function to prove the stability of the system under external disturbance when the vehicle dynamics are affected by input saturation nonlinear conditions.

[0069] This application proposes a method for controlling the driving of a connected vehicle at an intersection without a signal light considering input constraints, comprising the following steps:

[0070] Step S1 obtains the real-time status information and roadside environment information of the vehicle, and accurately locates the vehicle in real time in combination with the positioning system;

[0071] Step S2: uploading the obtained information to the central control unit (abbreviation: ICU) and the vehicle control unit;

[0072] Step S3: establishing a vehicle dynamics model taking into account input constraints, performing vehicle intersection conflict analysis, and establishing a vehicle cooperative control topology structure;

[0073] Step S4: Establishing the vehicle cooperative driving goal: that is, by regulating each pair of conflicting vehicles, ensuring that vehicles with the possibility of conflict pass through the intersection safely and avoid collision;

[0074] Step S5: design a time-triggered controller design method under input saturation constraints to evaluate the cooperative driving control performance of the autonomous driving vehicle;

[0075] Steps S3-S5, see the overall schematic diagram Figure 4 , specifically as follows: establish a vehicle dynamics model considering input constraints, conduct vehicle intersection conflict analysis, and establish a vehicle cooperative control topology structure.

[0076] Step S31: adopting a second-order linear vehicle model considering input saturation constraints:

[0077] (Formula 1)

[0078] In the formula represents the displacement of the vehicle, represents the derivative of displacement, represents the speed of the vehicle, express The derivative of represents the control input, is defined as a saturation function.

[0079] Saturation function bounds as follows:

[0080] (Formula 2)

[0081] In the formula, represents the lower bound of the saturation function, represents the upper bound of the saturation function, <0 indicates the vehicle deceleration behavior in the actual scene, >0 indicates the vehicle acceleration behavior in actual scenarios. The saturation function is designed to limit the vehicle control input, taking into account the physical limitations of the vehicle power system, driving comfort and safety, and can more accurately characterize the driving behavior.

[0082] According to Formula 1, the vehicle dynamics model can be expressed in the following state space form:

[0083] (Formula 3)

[0084] In the formula, For output, is the state variable, C and D are parameter matrices;

[0085] in, , , ;

[0086] Step S32: Performing traffic conflict analysis on the driving behavior of the autonomous driving vehicle at a typical intersection and establishing a traffic conflict diagram;

[0087] Step S33: Based on the traffic conflict graph analysis, a conflict relationship graph between autonomous driving vehicles at the intersection is established;

[0088] Step S34: Based on the conflict relationship analysis between the vehicles, a traffic relationship between the conflicting vehicles is established;

[0089] Step S35: establishing a traffic relationship between vehicles, including constructing a geometric topology and a communication topology, wherein the geometric topology is used to simulate the spatial arrangement of vehicles, and the communication topology is used to define information interaction between vehicles;

[0090] Model the communication topology using bidirectional channels based on geometric topology:

[0091] (Formula 4)

[0092] In the formula, is a collection of vehicles, is the flow direction of information. G is the flow characteristic of the model, which can be represented by the matrix A and the Laplace matrix L. , ,in:

[0093] (Formula 5)

[0094] (Formula 6)

[0095] Where: Represents the set of correlation matrices of the communication topology graph G, Represents the set of related Laplace matrices of the communication topology graph G, i,j∈V M ， They represent the nodes of the vehicle geometric topology graph, is the set of real numbers.

[0096] Step S4: Establishing the vehicle cooperative driving control target, specifically:

[0097] Step S41: Control each pair of conflicting vehicles to ensure that the vehicles pass through the intersection safely and achieve the desired motion state, as follows:

[0098] Determine the relative positions of conflicting vehicles i and j in different entrance lanes, define a virtual lane, and map the conflicting vehicles to the virtual lane. The expected relative distance between vehicle i and vehicle j is:

[0099] (Formula 7)

[0100] In the formula Indicates the length of the vehicle, represents the desired spacing, , is the vehicle speed, is the time interval;

[0101] (Formula 8)

[0102] In the formula is the minimum safe distance between two vehicles. and represents the safe distance between vehicle i and vehicle j from the stop line to the conflict point, represents the set of all vehicles that enter the communication range, The maximum safety compensation distance for vehicles in conflict within the intersection;

[0103] (Formula 9)

[0104] vehicle i and vehicles j The distance from the starting point to the stop line, .

[0105] Step S42: Formulate a control objective function and design a distributed controller.

[0106] (Formula 10)

[0107] In the formula and Respectively represent vehicles i and vehicles j speed, establish the time-triggered controller design under the input saturation constraint, evaluate the cooperative driving control performance of the autonomous driving vehicle, and conduct stability analysis of the intelligent networked vehicle cooperative control system under the input saturation constraint;

[0108] Step S5: Propose a time-triggered controller design method under input saturation constraints to evaluate the cooperative driving control performance of the autonomous driving vehicle, specifically including:

[0109] Step S51: Performing stability analysis of the intelligent networked vehicle cooperative control system under input saturation constraint conditions;

[0110] Step S52: The control strategy based on time triggering is as follows:

[0111] = (Formula 11)

[0112] In the formula >0 and >0 is the controller gain, and is the distance between vehicle i and vehicle j relative to the stop line, and are the speeds of vehicles i and j;

[0113] make , , , , , ,

[0114] The vehicle dynamics model considering input saturation constraints can be transformed into the following form:

[0115] (Formula 12)

[0116] Where: is an M-dimensional identity matrix, and , represents the vehicle speed, represents the derivative of the vehicle displacement, represents the derivative of velocity, L is the Laplace matrix, is the displacement, is the speed; the above model can achieve the global consistency of the cooperative control system of networked autonomous driving vehicles by constructing Lyapunov function.

[0117] The control objective described in Equation 10 is achieved by using the control protocol in Equation 11, thereby ensuring that the vehicle passes through the intersection safely and smoothly while achieving the desired motion state.

[0118] The advantages of this example are:

[0119] In order to achieve smooth movement of connected autonomous driving vehicles through intersections, a distributed control protocol considering constraints was proposed to achieve cooperative driving of vehicles. In order to solve the internal stability problem of the system under input saturation, the Lyapunov theory was used to verify the stability of the controller design method.

[0120] In order to evaluate the performance of the proposed distributed control method in the time-triggered framework, the controller gains are varied. k 1 and k 2 , the control system is tested under given input saturation conditions to ensure that the system complies with real-world constraints while meeting the coordination objectives. The simulation results confirm that the proposed time-triggered control method can effectively guide vehicles through intersections safely and smoothly while considering input saturation limitations and achieving stable control, and significantly outperforms the actual benchmark strategy in terms of traffic flow and energy consumption economy.

[0121] The above description is only a preferred feasible implementation example of the present invention, and does not limit the scope of rights of the present invention. All equivalent structural changes made using the contents of the present invention and the drawings are included in the scope of rights of the present invention.

Claims

1. A method for controlling a connected vehicle at an intersection without signal lights considering input constraints, characterized in that: The following steps are involved: Step S1: Acquire the real-time status information and roadside environment information of the autonomous driving vehicle, and accurately locate the autonomous driving vehicle in real time in combination with the positioning system; Step S2: uploading the real-time status information of the autonomous driving vehicle and the roadside environment information obtained in step S1 to the central control unit and the vehicle control unit; Step S3: Establishing an autonomous driving vehicle dynamics model considering input constraints, and conducting intersection conflict analysis of autonomous driving vehicles, establishing a conflict relationship graph, and establishing vehicle traffic relations based on the conflict relationship graph analysis, and then constructing the vehicle's geometric topology and information topology structure; Step S4: Establishing the cooperative driving control target of the autonomous driving vehicles; that is, by regulating each pair of conflicting vehicles, ensuring that the autonomous driving vehicles with the possibility of conflict pass through the intersection safely and avoid collision; Step S5: Propose a time-triggered controller design method under input saturation constraints to evaluate the cooperative driving control performance of autonomous driving vehicles.

2. The method for controlling a connected vehicle traveling at an intersection without a signal light considering input constraints according to claim 1, characterized in that: The vehicle real-time status information described in step S1 includes the speed, vehicle acceleration and position of the autonomous driving vehicle; the roadside environment information includes determining whether the autonomous driving vehicle has entered the communication range and whether the autonomous driving vehicle has reached the stop line of the intersection.

3. The method for controlling a connected vehicle traveling at an intersection without a signal light considering input constraints according to claim 1, characterized in that: The step S3 of constructing the geometric topology and information topology of the vehicle specifically includes: Step S31: According to the longitudinal dynamics of the vehicle, it is concluded that the vehicle will be affected by acceleration resistance, tire force, and air resistance during driving. In view of different vehicle models, the parameters in the model are also different. A second-order linear vehicle dynamics model considering input saturation constraints is adopted: (Formula 1) In the formula represents the displacement of the vehicle, represents the derivative of displacement, represents the speed of the vehicle, express The derivative of represents the control input, is defined as a saturation function where: The boundaries are as follows: (Formula 2) In the formula, represents the lower bound of the saturation function, represents the upper bound of the saturation function, when When <0, it indicates the vehicle deceleration behavior in the actual scene. When >0, it indicates the vehicle acceleration behavior in the actual scene; According to Formula 1, the vehicle dynamics model can be expressed in the following state space form: (Formula 3) In the formula, For output, is the state variable, C and D is the parameter matrix; in, , , ; Step S32: Performing traffic conflict analysis on the driving behavior of the autonomous driving vehicle at a typical intersection and establishing a traffic conflict diagram; Step S33: Based on the traffic conflict graph analysis, a conflict relationship graph between the autonomous driving vehicles at the intersection is established; Step S34: establishing a traffic relationship between the conflicting vehicles based on the conflict relationship analysis between the vehicles; Step S35: Establishing traffic relations between vehicles, including constructing geometric topology and communication topology. The geometric topology is used to simulate the spatial arrangement of vehicles, and the communication topology is used to define information interaction between vehicles.

4. The method for controlling a connected vehicle traveling at an intersection without a signal light considering input constraints according to claim 3, characterized in that: In step S35, a communication topology model is established based on the geometric topology using a bidirectional channel as follows: (Formula 4) In the formula, is a collection of vehicles, is the direction of information flow, G is the flow characteristic of the model, which can be obtained through the matrix A and the Laplacian matrix L Indicates that , ,in: (Formula 5) (Formula 6) Where: Represents the set of correlation matrices of the communication topology graph G, Represents the set of related Laplace matrices of the communication topology graph G, i,j∈V M ， They represent the nodes of the vehicle geometric topology graph, is the set of real numbers.

5. The method for controlling a connected vehicle traveling at an intersection without a signal light considering input constraints according to claim 4, characterized in that: Step S4 establishes the autonomous driving vehicle cooperative driving control target, specifically including: Step S41: Control each pair of conflicting vehicles to ensure that the vehicles pass through the intersection safely and achieve the desired motion state. The specific control method is as follows: Identify conflicting vehicles i and vehicles j At the relative positions of different entrance lanes, a virtual lane is defined and the conflicting vehicles are mapped to the virtual lane. i and vehicles j The expected relative distance between for: (Formula 7) Where: Indicates the length of the vehicle, represents the desired spacing, , is the vehicle speed, is the time interval; (Formula 8) Where: is the minimum safe distance between two vehicles. and Respectively represent vehicles i and vehicles j The safe distance from the stop line to the conflict point, represents the set of all vehicles entering the communication range, The maximum safety compensation distance for conflicting vehicles in the intersection; (Formula 9) Indicates vehicle i and vehicles jStart The distance from the point to the stop line, ; Step S42: Formulate a control objective function and design a distributed controller. (Formula 10) In the formula and Respectively represent vehicles i and vehicles j In time The speed of time.

6. The method for controlling a connected vehicle traveling at an intersection without a signal light considering input constraints according to claim 5, characterized in that: Step S5 proposes a time-triggered controller design method under input saturation constraint conditions to evaluate the cooperative driving control performance of the autonomous driving vehicle, specifically including: Step S51, performing stability analysis of the intelligent networked vehicle cooperative control system under input saturation constraint conditions; Step S52, the control strategy based on time triggering is as follows: = (Formula 11) Where: >0 and >0 is the controller gain, and It is a vehicle i and vehicles j The distance to the stop line, and It is a vehicle i and vehicles j speed; make , , , , , , Then the vehicle dynamics model considering input saturation constraints can be transformed as follows: (Formula 12) Where: is an M-dimensional identity matrix, and , represents the vehicle speed, represents the derivative of the vehicle displacement, represents the derivative of velocity, L is the Laplace matrix, is the displacement, is the speed; the above model can realize the global consistency of the cooperative control system of networked vehicles by constructing the Lyapunov function; The control objective described in Equation 10 is achieved by using the control strategy in Equation 11, thereby ensuring that the vehicle passes through the intersection safely and smoothly while achieving the desired motion state.

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