A primitive modeling method for hybrid vehicle groups near the signal zone

Abstract

This invention discloses a primitive modeling method for mixed vehicle groups in near-signal zones, comprising the following steps: using roadside sensing equipment to detect vehicles within the sensing area, thereby acquiring acceleration, speed, and position information of connected autonomous vehicles and human-driven vehicles; determining whether a vehicle has entered the near-signal zone; treating vehicles within the near-signal zone as nodes in both physical and information spaces; determining the vehicle type in the near-signal zone; establishing a primitive model of human-driven vehicles in the near-signal zone based on the collected information of vehicles surrounding the current human-driven vehicle; establishing a primitive model of connected autonomous vehicles in the near-signal zone based on the collected information of vehicles surrounding the current connected autonomous vehicle; and establishing a primitive model of mixed vehicle groups in the near-signal zone. This invention can establish a cyber-physical primitive model for multi-lane mixed traffic in near-signal zones, providing theoretical support for alleviating traffic problems in near-signal zones.

Description

Technical Field

[0001] This invention relates to the field of modeling in mixed traffic scenarios, specifically to a primitive modeling method for mixed vehicle groups near signal zones. Background Technology

[0002] With the rapid development of information and communication technologies and their widespread application in transportation systems, transportation systems exhibit typical characteristics of Cyber-Physical Systems (CPS). In Transportation Cyber-Physical Systems (T-CPS), vehicles can exchange data with each other and with roadside equipment using onboard communication devices. However, the full intelligentization and automation of transportation cannot be achieved overnight. For some time to come, traditional human-driven vehicles and autonomous vehicles will coexist, forming a new type of hybrid transportation. The near-signal zone refers to all areas upstream of road intersections affected by traffic light regulation; it plays a crucial role in the normal operation of urban transportation systems. Within this zone, vehicles are prone to frequent acceleration, deceleration, and stop-and-go behavior due to the periodic regulation of traffic lights and lane-changing actions, often leading to traffic congestion and severely impacting overall traffic efficiency. Because heterogeneous vehicles in new mixed traffic systems differ significantly in the type, scope, and real-time nature of their information acquisition, it is necessary to fully analyze the differences in cyber-physical interaction between CAVs (Carrier Aerial Vehicles) and HVs (Hard Vehicles) in near-signal zone traffic scenarios. Starting with constructing a theoretical model that reflects the differences between heterogeneous traffic subjects, and comprehensively considering factors such as the penetration rate of heterogeneous vehicles, this study aims to reveal the complex interaction mechanism of information and physical factors during vehicle cooperative driving under near-signal zone mixed traffic conditions, providing a new approach to improving near-signal traffic efficiency.

[0003] A review of relevant patents and papers reveals that current research on signal approach zones primarily focuses on the coordinated control between traffic lights and vehicles in connected environments, with few studies modeling traffic phenomena in signal approach zones under connected conditions. Existing research on the micro-behavior of vehicles in mixed traffic mainly considers the influence of vehicles in front and behind on the following behavior of a target vehicle in a single lane. However, signal approach zones are often multi-lane driving scenarios. Due to different target lanes for vehicles, forced lane changes occur in signal approach zones, and vehicles in adjacent lanes have a significant impact on the longitudinal driving behavior of target vehicles. Simultaneously, traffic lights in signal approach zones also influence driver behavior, meaning the factors determining vehicle longitudinal behavior are no longer one-dimensional.

[0004] To address the above problems, this invention focuses on studying the microscopic longitudinal behavior of mixed traffic in near-signal zones, providing theoretical support for alleviating traffic problems in these areas. This invention fully considers the multi-dimensional differences between CAV (Carrier Availability) and HV (Hybrid Vehicle Availability) in terms of information, physics, and cyber-physical interaction, revealing the mechanism of the complex interaction between cyber and physical factors during vehicle movement, and ultimately establishing a cyber-physical primitive model of mixed traffic in multi-lane near-signal zones. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a method for modeling primitives of mixed vehicle groups in the near-signal zone. This invention aims to solve the problems of inconsistent methods, poor remodeling capabilities, high complexity, and difficulty in collaborative modeling and verification in existing methods.

[0006] To achieve the above objectives, the present invention provides a method for modeling primitives of mixed vehicle groups in near-signal zones, comprising the following steps:

[0007] S1. Use roadside sensing equipment to detect vehicles in the sensing area, thereby obtaining acceleration, speed, and location information of connected autonomous vehicles and human-driven vehicles;

[0008] S2. Determine whether the vehicle has entered the signal close zone. If yes, proceed to step S3; otherwise, proceed to step S1.

[0009] S3. Vehicles within the near-signal zone are considered nodes in both physical and information spaces;

[0010] S4. Determine the type of vehicle near the signal zone. If it is a human-driven vehicle, proceed to step S5; if it is a connected autonomous vehicle, proceed to step S6.

[0011] S5. Based on the surrounding vehicle information collected in step S1, establish a near-signal zone human-driving primitive model;

[0012] S6. Based on the vehicle information around the current connected autonomous vehicle collected in step S1, establish a basic model of the connected autonomous vehicle in the near-signal zone;

[0013] S7. Combining steps S5 and S6, establish a basic model of the mixed vehicle group in the near signal zone.

[0014] Furthermore, in step S1, the detection interval of the roadside sensing device is 0.1 seconds to 2 seconds.

[0015] Furthermore, step S3 includes the following sub-steps:

[0016] S3.1 Treat vehicles as nodes, and the influence of vehicle i on vehicle j as a directed edge from node i to node j, and use the weight of the directed edge to represent the degree of influence.

[0017] S3.2 abstracts the communication topology between vehicles into a directed graph.

[0018] Furthermore, in step S3.2, the directed graph is represented as: G = (V, E, W);

[0019] The expression for calculating V is: V = {v1, v2, ..., v} n}

[0020] The expression for calculating E is:

[0021] The expression for calculating W is: W = [w nl ] N×N

[0022]

[0023] w nl The calculation expression is:

[0024]

[0025] In the formula, V is the set of nodes in the directed graph G; v n Let E be the speed of the nth vehicle at time t; E be the edge set between any two nodes in the directed graph G; W represent the weight set; w nl ={0,1} represents the communication connection between node n and node l, w nl =0 indicates that node l has no effect on node n, w nl =1 indicates that node l has an influence on node n; R is the communication distance; x n x1 represents the position of the nth car; x2 represents the position of the lead car.

[0026] Furthermore, step S5 includes the following sub-steps:

[0027] S5.1 Considering the impact of surrounding vehicles on the main vehicle, establish a primitive model a with the driver as the main vehicle. oh (t), the primitive model with the human-driven vehicle as the main vehicle is used to characterize the influence of surrounding vehicles on the longitudinal behavior of the target vehicle;

[0028] S5.2 For the target person driving, calculate the acceleration a generated by the potential force of the traffic lights in the near signal zone. signal ;

[0029] S5.3 Establishing a primitive model of human-vehicle interaction in the near-signal zone a' oh (t), the model is described as follows:

[0030] a' oh (t)=a oh (t)-Ψa signal

[0031] In the formula, Ψ = {0, 1}, where Ψ = 0 indicates that the current traffic light is in the green phase and the traffic light has no effect on drivers, and Ψ = 1 indicates that the current traffic light is in the red phase and the traffic light generates a repulsive force on drivers.

[0032] Furthermore, step S6 includes the following sub-steps:

[0033] S6.1 For vehicles in the same lane within the target connected autonomous vehicle communication range, calculate the primitive model of vehicles in the connecting lanes;

[0034] In the multi-lane scenario of S6.2, considering the impact of surrounding vehicles on the target connected autonomous vehicle, a primitive model a for connected autonomous vehicles near the signal zone is established. oc (t).

[0035] Furthermore, in step S7, the near-signal zone mixed vehicle group primitive model a o The description of (t) is as follows:

[0036]

[0037] In the formula, Ξ={0,1}, Ξ=0 indicates that the target vehicle is a connected autonomous vehicle, and Ξ=1 indicates that the target vehicle is a traditional human-driven vehicle; φ represents the vehicle sensitivity coefficient, and φ represents the traffic light sensitivity coefficient.

[0038] The beneficial effects of this invention are as follows:

[0039] This invention discloses a primitive modeling method for mixed vehicle groups in near-signal zones. Focusing on the study of microscopic longitudinal behavior in mixed traffic near signal zones, it fully considers the multi-dimensional differences between CAVs (Concurrent Ambient Vehicles) and HVs (Hybrid Vehicles) in terms of information, physics, and cyber-physical interaction, establishing cooperative driving models for each. Then, by studying the coupling relationships between each of these mixed traffic entities, the cyber-physical interaction mechanism between heterogeneous mixed traffic entities is obtained, thereby establishing a cyber-physical primitive model for multi-lane mixed traffic in near-signal zones. This provides theoretical support for alleviating traffic problems in near-signal zones.

[0040] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0041] Figure 1 This is a flowchart of a method for modeling a hybrid vehicle group near the signal zone according to the present invention;

[0042] Figure 2 A diagram illustrating the physical relationships between a person driving a car;

[0043] Figure 3 This is a cyber-physical relationship diagram under mixed traffic conditions. Detailed Implementation

[0044] To make the technical solutions, advantages, and objectives of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the protection scope of this application.

[0045] This embodiment proposes a primitive modeling method for mixed vehicle groups near signal zones. By applying vehicle-road cooperative technology, it aims to characterize mixed traffic driving behavior in near signal zones and establish a longitudinal vehicle driving model. This embodiment is a three-lane near signal zone scenario, with a 40% mixing rate of connected autonomous vehicles.

[0046] like Figure 1 As shown, the specific steps of the near-signal zone mixed vehicle group primitive modeling method in this embodiment are as follows:

[0047] S1. Utilize roadside sensing units (RSUs) to detect vehicles within the sensing area, thereby obtaining acceleration, speed, and location information of connected autonomous vehicles and human-driven vehicles;

[0048] The detection interval of the roadside sensing device is 0.1 seconds to 2 seconds.

[0049] S2. Determine whether the vehicle has entered the signal close zone. If yes, proceed to step S3; otherwise, proceed to step S1.

[0050] S3. Vehicles within the near-signal zone are considered nodes in both physical and information spaces, such as... Figure 2 and Figure 3 As shown;

[0051] S3.1 Treat vehicles as nodes, and the influence of vehicle i on vehicle j as a directed edge from node i to node j, and use the weight of this directed edge to represent the degree of influence.

[0052] S3.2 abstracts the communication topology between vehicles into a directed graph, which is represented as: G = (V, E, W);

[0053] The expression for calculating V is: V = {v1, v2, ..., v} n}

[0054] The expression for calculating E is:

[0055] The expression for calculating W is: W = [w nl ] N×N Construct an adjacency matrix to describe the communication connections in the queue;

[0056]

[0057] In the formula, V is the set of nodes in the directed graph G; v n Let be the speed of the nth vehicle at time t; E be the edge set between any two nodes in the directed graph G; and W be the weight set.

[0058] Considering that the number of cooperative vehicles for each connected autonomous vehicle is affected by the communication distance between them, the target vehicle can only receive information from the vehicle in front if the distance between two vehicles is less than the communication range. Therefore, the number of cooperative vehicles for each connected autonomous vehicle at a given time depends on their location and communication range. The adjacency matrix is ​​set as a time-varying matrix that dynamically changes over time; the communication topology at time t is determined by w. nl Decision, w nl The calculation expression is as follows:

[0059]

[0060] In the formula, w nl ={0,1} represents the communication connection between node n and node l, w nl =0 indicates that node l has no effect on node n, w nl =1 indicates that node l has an influence on node n; R is the communication distance; x n x1 represents the position of the nth car; x2 represents the position of the lead car.

[0061] S4. Determine the type of vehicle near the signal zone. If it is a human-driven vehicle, proceed to step S5; if it is a connected autonomous vehicle, proceed to step S6.

[0062] S5. Based on the surrounding vehicle information collected in step S1, establish a near-signal zone human-driving primitive model;

[0063] S5.1 Considering the impact of surrounding vehicles on the main vehicle, establish a primitive model a with the driver as the main vehicle. oh (t), the primitive model with the human-driven vehicle as the main vehicle is used to characterize the influence of surrounding vehicles on the longitudinal behavior of the target vehicle;

[0064] The model is described as follows:

[0065] a oh (t)=θ c1 η c1 (1-Λ side (Δy o,r1 (t)))(1-Λ side (Δy o,l1(t)))×Λ side (Dy o,c1 (t))(λ c1 (V(Δx o,c1 (t))-v o (t))+k c1 Δv o,c1 (t))+θ r1 or r1 L side (Dy) o,r1 (t))(λ r1 (V(Δx o,r1 (t))-v o (t))+k r1 Δv o,r1 (t))+θ l1 or l1 L side (Dy o,l1 (t))(λ l1 (V(Δx o,l1 (t))-v o (t))+k l1 Δv o,l1 (t))+θ c2 or c2 (1-L side (Dy r2,o (t)))(1-Λ side (Dy) l2,o (t)))×Λ rear (Δx c2,o (t))Λ side (Dy) c2,o (t))(λ c2 (V(Δx c2,o (t))-v o (t))+k c2 Δv c2,o (t))+θ r2 or r2 L rear (Δx r2,o (t))Λ side (Dy r2,o (t))×(λ r2 (V(Δx r2,o (t))-v o (t))+k r2 Δv r2,o (t))+θ l2 or l2 L rear (Δx l2,o (t))Λ side (Dy l2,o (t))×(λ l2(V(Δx l2,o (t))-v o (t))+κ l2 Δv l2,o (t))

[0066] In the formula, o, c1, l1, r1, c2, l2, and r2 represent the numbers of the main vehicle, the front vehicle, the left front vehicle, the right front vehicle, the rear vehicle, the left rear vehicle, and the right rear vehicle at a certain time t, respectively; x o (t), Let be the longitudinal positions of vehicles o, c1, l1, r1, c2, l2, and r2 at time t; y o (t), Let v be the lateral positions of vehicles o, c1, l1, r1, c2, l2, and r2 at time t; o (t), Let be the velocities of vehicles o, c1, l1, r1, c2, l2, and r2 at time t; Δx oc1 (t)=x c1 (t)-x o (t), Δx ol1 (t)=x l1 (t)-x o (t), Δx or1 (t)=x r1 (t)-x o (t), Δx c2o (t)=x o (t)-x c2 (t), Δx l2o (t)=x o (t)-x l2 (t), Δx r2o (t)=x o (t)-x r2 (t) represents the longitudinal position difference between the surrounding vehicles and the main workshop; Δy oc1 (t)=y c1 (t)-y o (t), Δy ol1 (t)=y l1 (t)-y o (t), Δy or1 (t)=y r1 (t)-y o (t), Δy c2o (t)=y o (t)-y c2 (t), Δy l2o (t)=y o (t)-y l2(t), Δy r2o (t)=y o (t)-y r2 (t) represents the lateral position difference between the surrounding vehicles and the main workshop; Δv oc1 (t)=v c1 (t)-v o (t), Δv ol1 (t)=v l1 (t)-v o (t), Δv or1 (t)=v r1 (t)-v o (t), Δv c2o (t)=v o (t)-v c2 (t), Δv l2o (t)=v o (t)-v l2 (t), Δv r2o (t)=v o (t)-v r2 (t) represents the speed difference between the surrounding vehicles and the main vehicle; a0 represents the acceleration of the central vehicle;

[0067] λ c1 , λ r1 , λ l1 , λ c2 , λ r2 , λ l2 For; k c1 k r1 k l1 k c2 k r2 k l2 These are the parameters to be calibrated;

[0068] θ=[θ c1 ,θ r1 ,θ l1 ,θ c2 ,θ r2 ,θ l2 [ ] represents the physical connection matrix of the target vehicle. For example... Figure 3 In the three-lane scenario shown, for the vehicle in the middle lane, it has physical connections with all six adjacent vehicles, so θ = [1,1,1,1,1,1]. For the vehicle in the leftmost lane, there is no road to its left, so the vehicle only has physical connections with vehicles in its own lane and the adjacent right lane, so θ = [1,1,0,1,1,0].

[0069] a0(t) is the acceleration of the main vehicle, η c1 η r1 η l1 ηc2 η r2 η l2 These are the driver's sensitivity coefficients to the vehicle in front, the vehicle to the right front, the vehicle to the left front, the vehicle behind, the vehicle to the right rear, and the vehicle to the left rear, respectively.

[0070] Λ side The lateral activation function for the driver is defined as follows:

[0071]

[0072] Where Δy is the lateral position difference between the main vehicle and surrounding vehicles; d is the lane width; and q1, q2, and q3 are parameters to be determined.

[0073] Λ rear The driver's backward activation function is defined as follows:

[0074]

[0075] Where Δx is the longitudinal position difference between the main vehicle and the surrounding vehicles, ρ1 is the rear sensitivity coefficient, and ρ2, ρ3, and ζ are parameters to be determined.

[0076] S5.2 For the target person driving, calculate the acceleration a generated by the potential force of the traffic lights in the near signal zone. signal ;

[0077]

[0078] In the formula, η is the potential field coefficient of the traffic light; x stop ξ is the stop line position; ε is the speed sensitivity coefficient.

[0079] S5.3 Establishing a primitive model of human-vehicle interaction in the near-signal zone a' oh (t), the model is described as follows:

[0080]

[0081] In the formula, Ψ = {0, 1}, where Ψ = 0 indicates that the current traffic light is in the green phase and the traffic light has no effect on drivers, and Ψ = 1 indicates that the current traffic light is in the red phase and the traffic light generates a repulsive force on drivers.

[0082] S6. Based on the vehicle information around the current connected autonomous vehicle collected in step S1, establish a basic model of the connected autonomous vehicle in the near-signal zone;

[0083] S6.1 For vehicles in the same lane within the target connected vehicle communication range, the primitive model for vehicles in interconnected lanes is:

[0084]

[0085] In the formula, parameter a is the sensitivity coefficient of CAV; parameter b is the speed difference coefficient of the preceding vehicle; parameter c is the speed difference coefficient of the following vehicle, and c < 1; v n (t) represents the speed of the nth vehicle at time t, x n (t) represents the position of the nth vehicle at time t; Δx n =x n+1 (t)-x n (t), representing the distance between the (n+1)th car and the nth car at time t; Δv n =v n+1 (t)-v n (t) represents the speed difference between the (n+1)th car and the nth car at time t; 0≤β nl ,α nl ,γ nl ≤1 indicates the target vehicle's sensitivity to the l-th vehicle in front of it; w nl (t) represents the communication connection between the nth vehicle and the vehicle l in front of it.

[0086] The optimized velocity function V in the model F V B as follows:

[0087]

[0088] In the formula, V1, V2, C1, and C2 are other parameters; c δ1 represents the vehicle length; δ2 represents the optimal speed difference coefficient of the preceding vehicle; δ3 represents the optimal speed difference coefficient of the following vehicle.

[0089] In the multi-lane scenario of S6.2, considering the impact of surrounding vehicles on the target connected autonomous vehicle, a primitive model a for connected autonomous vehicles near the signal zone is established. oc (t), the model is described as follows:

[0090]

[0091] In the formula, σ∈[0,1] are weighting coefficients; i represents the i-th vehicle in front of the target vehicle; m represents the number of vehicles in front that have a communication connection with the target vehicle; the communication topology between the target CAV and the multiple CAVs in front of it is formed by... Decide, The expression is as follows:

[0092]

[0093] S7. Combining steps S5 and S6, establish a basic model of the mixed vehicle group in the near signal zone.

[0094] Basic model of mixed vehicle groups near the signal zone a o The expression for calculating (t) is as follows:

[0095]

[0096] In the formula, Ξ={0,1}, Ξ=0 indicates that the target vehicle is a connected autonomous vehicle, and Ξ=1 indicates that the target vehicle is a traditional human-driven vehicle; φ represents the vehicle sensitivity coefficient, and φ represents the traffic light sensitivity coefficient.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. A method for modeling hybrid vehicle groups near the signal zone using primitives, characterized in that, Includes the following steps: S1. Use roadside sensing equipment to detect vehicles in the sensing area, thereby obtaining acceleration, speed, and location information of connected autonomous vehicles and human-driven vehicles; S2. Determine whether the vehicle has entered the signal close zone. If yes, proceed to step S3; otherwise, proceed to step S1. S3. Vehicles within the near-signal zone are considered nodes in both physical and information spaces; S4. Determine the type of vehicle near the signal zone. If it is a human-driven vehicle, proceed to step S5; if it is a connected autonomous vehicle, proceed to step S6. S5. Based on the surrounding vehicle information collected in step S1, establish a near-signal zone human-driving primitive model. Step S5 includes the following sub-steps: S5.1 Considering the impact of surrounding vehicles on the main vehicle, establish a primitive model with the driver as the main vehicle. The primitive model with the human-driven vehicle as the main vehicle is used to characterize the influence of surrounding vehicles on the longitudinal behavior of the target vehicle. S5.2 For the target person driving, calculate the acceleration generated by the potential field force of the traffic lights near the signal zone. ; S5.3 Establishing a primitive model of human-vehicle interaction in the near-signal zone The model is described as follows: In the formula, , This indicates that the traffic light is currently in the green phase, and the traffic light does not affect drivers. This indicates that the traffic light is currently in the red phase, and the traffic light exerts a repulsive force on drivers. S6. Based on the vehicle information surrounding the current connected autonomous vehicle collected in step S1, establish a basic model of the connected autonomous vehicle in the near-signal zone. Step S6 includes the following sub-steps: S6.1 For vehicles in the same lane within the communication range of the target connected automated vehicle, calculate the primitive model of the vehicles in the same lane; In the S6.2 multi-lane scenario, considering the impact of surrounding vehicles on the target connected autonomous vehicle, a basic model of connected autonomous vehicles near the signal zone is established. ; In the formula, , , , , , , These represent the numbers of the main vehicle, the front vehicle, the left front vehicle, the right front vehicle, the rear vehicle, the left rear vehicle, and the right rear vehicle at a certain time t. , , , , , , vehicles , , , , , , The longitudinal position at time t; , , , , , , vehicles , , , , , , The horizontal position at time t; , , , , , , vehicles , , , , , , The velocity at time t; , , , , , This represents the longitudinal positional difference between the surrounding vehicles and the main workshop. , , , , , This refers to the lateral positional difference between the surrounding vehicles and the main workshop. , , , , , The speed difference between surrounding vehicles and the main workshop; The acceleration of the vehicle at the center; The physical connection matrix for the target vehicle; , , , , , These are the driver's sensitivity coefficients to the vehicle in front, the vehicle to the right front, the vehicle to the left front, the vehicle behind, the vehicle to the right rear, and the vehicle to the left rear, respectively. , , , , , These are the parameters to be calibrated; The lateral activation function for the driver is defined as follows: ； in, The difference in lateral position between the main vehicle and surrounding vehicles; Lane width, , , These are parameters to be determined. The driver's backward activation function is defined as follows: ； in, The longitudinal position difference between the main vehicle and the surrounding vehicles. For the back-end sensitivity coefficient, , , These are parameters to be determined. Here are the weighting coefficients; i represents the i-th vehicle in front of the target vehicle; m represents the number of vehicles in front that have a communication connection with the target vehicle. The communication topology between the target CAV and multiple CAVs ahead is as follows: Decide, The expression is as follows: ； S7. Combining steps S5 and S6, establish a basic model of the mixed vehicle group in the near signal zone; In step S7, the near-signal zone mixed vehicle group primitive model The description is as follows: ； In the formula, , This indicates that the target vehicle is a connected autonomous vehicle. This indicates that the target vehicle is driven by a traditional human. For vehicle sensitivity coefficient, This represents the sensitivity coefficient of the traffic light.

2. The method for modeling a hybrid vehicle group near the signal zone according to claim 1, characterized in that: In step S1, the detection interval of the roadside sensing device is 0.1 seconds to 2 seconds.

3. The method for modeling a hybrid vehicle group near the signal zone according to claim 1, characterized in that: Step S3 includes the following sub-steps: S3.1 Treat vehicles as nodes, and the influence of vehicle i on vehicle j as a directed edge from node i to node j, and use the weight of the directed edge to represent the degree of influence. S3.2 abstracts the communication topology between vehicles into a directed graph.

4. The method for modeling a hybrid vehicle group near the signal zone according to claim 3, characterized in that: In step S3.2, the directed graph is represented as follows: ; The calculation expression is: ; The calculation expression is: ; The calculation expression is: ; ； The calculation expression is: ； In the formula, Let G be the set of nodes in the directed graph; Let be the speed of the nth vehicle at time t; Let G be the set of edges between pairs of nodes in a directed graph; Represents the weight set; This represents the communication connection between node n and node l. This indicates that node l has no effect on node n. This indicates that node l has an impact on node n; R is the communication distance; The position of the nth car; This is the position of the lead car.

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