Ramp converging method based on roadside cooperative control in network connection mixed traffic environment

Abstract

The invention discloses a ramp confluence method based on roadside cooperative control in a network-connected mixed traffic environment, and the method comprises the steps: determining an optimal confluence point at which a confluence vehicle converges from a ramp lane to an adjacent main lane, and recognizing a potential cooperative vehicle on the main lane; a safety efficiency evaluation model is constructed, and the optimal behavior of the cooperative vehicle is evaluated and decided in combination with the vehicle type and the driver response behavior; and in combination with the kinematic characteristics of the vehicle, planning a vehicle confluence trajectory and generating a control command, thereby realizing trajectory tracking and control of the vehicle in the confluence process. According to the method, dynamic confluence point prediction of ramp vehicles and intelligent behavior guidance of main lane cooperative vehicles can be accurately realized in real time, and the smoothness and safety of traffic flow are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation, and in particular to a ramp merging method based on roadside collaborative control in a networked mixed traffic environment. Background Art

[0002] In recent years, connected vehicle (IoV) and autonomous driving technologies have developed rapidly, and the application prospects of connected autonomous vehicles (ICVs) in transportation systems are becoming increasingly broad. In merging scenarios on multi-lane highway entrance ramps, vehicles must merge smoothly and safely from the ramp onto the main road. This process is crucial for traffic efficiency and safety. Traditional traffic management methods, which rely primarily on static signal control and fixed-cycle scheduling, are difficult to adapt to complex traffic conditions, especially when dealing with mixed traffic flows, lacking flexibility and real-time adjustment capabilities.

[0003] In mixed traffic flows, the driving behavior of human-driven vehicles (HDVs) is uncertain, while ICVs offer vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication capabilities. Existing methods fail to fully utilize the communication advantages of ICVs, resulting in inefficient vehicle collaborative scheduling and right-of-way allocation in dynamic traffic environments, making it difficult to meet rapidly changing traffic demands. Furthermore, existing scheduling control methods often focus on single-vehicle behavior or static signal control, lacking effective multi-vehicle collaborative scheduling strategies. This is particularly true in on-ramp merging scenarios, where the advantages of roadside collaborative control units are not fully utilized.

[0004] Therefore, to solve the above problems, a ramp merging method based on roadside collaborative control in a connected mixed traffic environment is needed. It can realize the dynamic merging point prediction of ramp vehicles and the intelligent behavior guidance of cooperative vehicles in the main lane in real time and accurately, greatly improving the smoothness and safety of traffic flow. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to overcome the defects in the existing technology and provide a ramp merging method based on roadside collaborative control in a networked mixed traffic environment, which can realize the dynamic merging point prediction of ramp vehicles and the intelligent behavior guidance of cooperative vehicles in the main lane in real time and accurately, greatly improving the smoothness and safety of traffic flow.

[0006] The ramp merging method based on roadside coordinated control in a networked mixed traffic environment of the present invention includes:

[0007] Determine the optimal merging point for merging vehicles from the ramp lane to the adjacent main lane and identify potential cooperative vehicles in the main lane;

[0008] Construct a safety and efficiency evaluation model that combines vehicle type and driver response behavior to evaluate and decide on the optimal behavior of cooperative vehicles;

[0009] Combined with the kinematic characteristics of the vehicle, the vehicle's merging trajectory is planned and control commands are generated to achieve trajectory tracking and control of the vehicle during the merging process.

[0010] Furthermore, the potential cooperative vehicle V in the main lane is determined according to the following formula: candidate (t):

[0011] V candidate (t) = {j|x j (t)≤x MV (t)+L comm ∧x j (t)≥x MV (t)-L comm};

[0012] Among them, x j (t) and x MV (t) represent the longitudinal positions of vehicle j and the merging vehicle at time t, L comm is the communication range length.

[0013] Furthermore, the predicted longitudinal spacing of vehicle j at time t is determined according to the following formula:

[0014] Among them, x merge\_start is the vertical coordinate of the starting point of the confluence area, x j (t) represents the longitudinal position of vehicle j at time t, v j (t) is the speed of vehicle j at time t, It is the predicted time for the merging vehicle to reach the starting point of the merging area at its current speed.

[0015] Further, when and When , it indicates that vehicle j and its rear vehicle j+1 are located downstream and upstream of the starting point of the merging area, respectively. At this time, vehicle j is determined to be the leading cooperative vehicle, and its rear vehicle j+1 is determined to be the rear cooperative vehicle.

[0016] The minimum safety distance constraints between the front and rear cooperative vehicles are set as follows:

[0017]

[0018] in, and are the minimum acceptable distances between the front and rear cooperative vehicles at time t, respectively, and are calculated as follows:

[0019]

[0020]

[0021] Among them, v MV (t) and v RCV (t) are the speeds of the merging vehicle and the following cooperative vehicle at time t, is the minimum time interval for collaborative merging.

[0022] Furthermore, the safety efficiency evaluation model includes a safety efficiency evaluation function and evaluation constraints; the safety efficiency evaluation function is:

[0023]

[0024] Among them, U option1 (t) and U option2 (t) represents the utility value of the two choices of the cooperative vehicle at time t; and They represent the normalized values ​​of the safety index of the cooperative vehicle and its following vehicle, the preceding vehicle, and the following vehicle in the target lane at time t; and are the normalized acceleration values ​​of the cooperative vehicle at the current moment and the previous moment respectively; α, β, γ, ζ, η are weight coefficients;

[0025] E local\_efficiency (t) is the local efficiency evaluation index;

[0026] The evaluation constraints include safety constraints and efficiency constraints;

[0027] The safety constraints are:

[0028] TT safe ≥TT min ;

[0029] Among them, TT safe TT is the time it takes for the rear vehicle to collide with the front vehicle if it continues to travel at the current speed and acceleration. min The safety threshold is set;

[0030] The efficiency constraint is:

[0031] E local\_efficiency (t)≥E min ;

[0032] Among them, E min is the set minimum efficiency threshold.

[0033] Furthermore, the local efficiency evaluation index E is determined according to the following formula: local\_efficiency (t):

[0034]

[0035] Where N is the total number of vehicles in the cooperative area, v i (t) is the speed of vehicle i at time t, v desired (t) is the desired speed.

[0036] Furthermore, the vehicle merging trajectory is planned, including:

[0037] Vehicle longitudinal motion planning includes:

[0038] Determine the vehicle's longitudinal velocity v long (t+Δt):

[0039] v long (t+Δt)=v long (t)+a long (t)·Δt;

[0040] Among them, v long (t) is the longitudinal velocity of the vehicle at time t, a long (t) is the longitudinal acceleration, Δt is the time step;

[0041] Determine the longitudinal position x of the vehicle long (t+Δt):

[0042] x long (t+Δt)=x long (t)+v long (t)·Δt;

[0043] Among them, x long (t) is the longitudinal position of the vehicle at time t;

[0044] When vehicles merge into the vehicle, set the minimum safety time interval g min , and calculate the expected speed v of the vehicle based on this desired (t):

[0045] v desired (t)=min(v long (t)+a max ·Δt,v equilibrium );

[0046] Among them, v equilibrium is the equilibrium speed, a max is the maximum permissible acceleration;

[0047] Vehicle lateral motion planning includes:

[0048] Determine the lateral position y of the vehicle lat (t+Δt) is:

[0049] y lat (t+Δt)=y lat(t)+v lat (t)·Δt;

[0050] Among them, y lat (t) is the lateral position of the vehicle at time t, v lat (t) is the lateral velocity;

[0051] Determine the lateral acceleration a lat (t) Restrictions:

[0052] a lat (t)≤a lat\_max ;

[0053] Among them, a lat\_max is the maximum permissible lateral acceleration.

[0054] Further, control commands are generated, including:

[0055] Based on the longitudinal and lateral motion planning, the control commands are generated as follows:

[0056] For longitudinal control, generate acceleration commands:

[0057] a cmd\_long (t) = min(max(a long (t),a min ),a max );

[0058] Among them, a min and a max are the minimum and maximum permissible longitudinal acceleration, respectively;

[0059] For lateral control, generate steering commands:

[0060]

[0061] Among them, δ cmd\_lat (t) is the lateral steering command.

[0062] The beneficial effects of the present invention are as follows: the present invention discloses a ramp merging method based on roadside collaborative control in a networked mixed traffic environment, which realizes real-time negotiation and intelligent scheduling of road rights by integrating the communication and collaboration between networked autonomous driving vehicles and connected manually driven vehicles. By analyzing real-time traffic data, vehicle dynamics and road condition changes, the vehicle speed, traffic order and road right allocation are dynamically adjusted, thereby effectively reducing traffic conflicts, reducing safety hazards, and ensuring traffic flow in complex environments such as merging areas. In particular, in the process of multi-vehicle collaborative scheduling, according to different vehicle characteristics and driving behaviors, a precise and real-time control strategy is provided, which significantly improves road capacity, optimizes the traffic efficiency of road sections, and reduces traffic delays caused by uncoordinated driving behaviors. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0064] Figure 1 Schematic diagram of the ramp merging method of the present invention;

[0065] Figure 2 This is a schematic diagram of the confluence area of ​​the present invention;

[0066] Figure 3 Schematic diagram of the principle framework of the ramp merging method of the present invention. DETAILED DESCRIPTION

[0067] The present invention is further described below with reference to the accompanying drawings, as shown in the drawings:

[0068] This embodiment discloses a ramp merging method based on roadside coordinated control in a connected mixed traffic environment, including the following steps:

[0069] Determine the optimal merging point for merging vehicles from the ramp lane to the adjacent main lane and identify potential cooperative vehicles in the main lane;

[0070] Construct a safety and efficiency evaluation model that combines vehicle type and driver response behavior to evaluate and decide on the optimal behavior of cooperative vehicles;

[0071] Combined with the kinematic characteristics of the vehicle, the vehicle's merging trajectory is planned and control commands are generated to achieve trajectory tracking and control of the vehicle during the merging process.

[0072] This invention addresses the challenge of collaborative merging control for intelligent connected vehicles (ICVs) in mixed traffic scenarios on multi-lane highways, particularly in entrance ramp merging areas. It aims to achieve multi-vehicle collaborative scheduling and dynamic right-of-way allocation to improve traffic efficiency and safety. This invention enables safe and efficient multi-lane merging control in mixed traffic environments, not only improving traffic efficiency but also significantly reducing the risk of traffic accidents. It provides strong technical support for the popularization and application of ICVs and promotes the development of modern transportation systems towards greater intelligence and efficiency.

[0073] In this implementation, existing expected position search algorithms are first applied. For example, RNNs are used to predict potential vehicle trajectories, combined with A* to plan merging points, and MPC is used to optimize speed control strategies. Alternatively, trajectory prediction based on motion models can be used directly to predict the optimal merging point for ramp vehicles and accurately identify potential cooperating vehicles. By comprehensively considering multiple factors such as vehicle speed, inter-vehicle spacing, and traffic flow density, complex mathematical modeling and intelligent algorithms are used to determine the optimal merging position for ramp vehicles in advance, laying the foundation for subsequent coordinated control.

[0074] Among them, the expected position search aims to determine the expected position of the merging vehicle (MV) merging from the ramp lane to the adjacent main lane and assign tasks to the cooperative vehicle (CV) on the main lane.

[0075] like Figure 2 As shown, at a certain moment, the cooperative vehicle V on the main lane in the potential merging area candidate (t) is:

[0076] V candidate (t) = {j|x j (t)≤x MV (t)+L comm ∧x j (t)≥x MV (t)-L comm};

[0077] Among them, x j (t) and x MV (t) represent the longitudinal positions of vehicle j and the merging vehicle at time t, L comm is the communication range length.

[0078] The predicted longitudinal distance of vehicle j at time t is determined according to the following formula:

[0079]

[0080] Among them, x merge\_start is the vertical coordinate of the starting point of the confluence area, x j (t) represents the longitudinal position of vehicle j at time t, v j (t) is the speed of vehicle j at time t, It is the predicted time for the merging vehicle to reach the starting point of the merging area at its current speed.

[0081] when and When , it indicates that vehicle j and its rear vehicle j+1 are located downstream and upstream of the starting point of the merging area, respectively. At this time, vehicle j is determined to be the leading cooperative vehicle, and its rear vehicle j+1 is determined to be the rear cooperative vehicle.

[0082] The minimum safety distance constraints between the front cooperative vehicle (LCV) and the rear cooperative vehicle (RCV) are set as follows:

[0083]

[0084] in, and are the minimum acceptable distances between the front and rear cooperative vehicles at time t, respectively, and are calculated as follows:

[0085]

[0086] Among them, v MV (t) and v RCV (t) are the speeds of the merging vehicle and the following cooperative vehicle at time t, is the minimum time interval for collaborative merging.

[0087] When the above constraints are not met, the rear cooperative vehicle or the front cooperative vehicle needs to be adjusted according to the safety efficiency evaluation model to ensure that the merging vehicles can merge safely.

[0088] In this embodiment, a carefully designed safety and efficiency assessment model, combined with vehicle type and driver response behavior, evaluates and determines the optimal behavior for vehicles in the main lane. This model intelligently determines whether vehicles in the main lane need to change lanes or adjust their operating status, providing precise and optimal guidance for these vehicles, ensuring efficient and safe traffic flow.

[0089] Among them, the safety efficiency evaluation model is used to determine the best choice of cooperative vehicles (CVs) between maintaining longitudinal driving and changing lanes to optimize the merging process.

[0090] The safety efficiency evaluation model includes a safety efficiency evaluation function and evaluation constraints;

[0091] The safety efficiency evaluation function is:

[0092]

[0093]

[0094] Among them, U option1 (t) and U option2 (t) represents the utility value of the two choices of the cooperative vehicle at time t; and They represent the normalized values ​​of the safety index of the cooperative vehicle and its following vehicle, the preceding vehicle, and the following vehicle in the target lane at time t; and are the normalized acceleration values ​​of the cooperative vehicle at the current moment and the previous moment respectively; α, β, γ, ζ, η are weight coefficients;

[0095] The safety index of the cooperative vehicle and its following vehicle, the preceding vehicle, and the following vehicle in the target lane at time t can be calculated using the following formula:

[0096] (1) Safety indicators of cooperative vehicles and their following vehicles (Indicates whether the following vehicle has enough space and reaction time to avoid rear-ending the cooperative vehicle):

[0097]

[0098] v coop (t) is the speed of the cooperative vehicle at time t, v rear (t) is the speed of the vehicle behind it, x coop (t),x rear (t) are the longitudinal positions of the corresponding vehicles, L coop The captain of the cooperative vehicle;

[0099] (2) Safety indicators of cooperative vehicles and their preceding vehicles (Represents the risk of a cooperative vehicle rear-ending the vehicle in front):

[0100]

[0101] v lead (t) is the speed of the preceding vehicle, x lead (t) is the position of the preceding vehicle, L lead The length of the vehicle in front;

[0102] (3) Safety indicators of the vehicle behind the target lane (Indicates the safe distance between the cooperative vehicle and the vehicle behind it when merging into the target lane to avoid the "cut-in-induced sudden braking" phenomenon):

[0103]

[0104] v target (t) is the speed of the vehicle behind the target lane, x target (t) is the position of the vehicle behind in the target lane.

[0105] Among them, the maximum value max1 is selected from the safety indicators of several cooperative vehicles and their following vehicles, and the safety indicators of the current cooperative vehicle and its following vehicle are calculated. Normalize and get the normalized value for Similarly, the normalized values ​​of other safety indicators can be obtained.

[0106] In addition, according to the actual operating conditions of the vehicle, the maximum acceleration value of the cooperative vehicle is set. a 6m / s 2 , then the acceleration of the cooperative vehicle at the current moment is Normalized value for Similarly, the acceleration of the cooperative vehicle at the previous moment Normalized value

[0107] For the weight coefficients: α, β, γ, ζ, η, among which α is used to measure the impact of safety. The more negative it is, the more attention it pays to safety. α = -0.6; β reflects the impact of the acceleration change of the following vehicle on the stability of the system. β = 0.3; γ represents the current vehicle's own acceleration benefit. γ = 0.3; ζ represents the comfort impact caused by the acceleration mutation. ζ = -0.2; η represents the impact of the local traffic efficiency benefit. η = 0.2.

[0108] E local\_efficiency (t) is the local efficiency evaluation index, which is the ratio of the average speed to the expected speed under multi-vehicle cooperation; the local efficiency evaluation index E is determined according to the following formula local\_efficiency (t):

[0109]

[0110] Where N is the total number of vehicles in the cooperative area, v i (t) is the speed of vehicle i at time t, v desired (t) is the desired speed.

[0111] The evaluation constraints include safety constraints and efficiency constraints;

[0112] To ensure lane change safety, the safety constraints are:

[0113] TT safe ≥TT min ;

[0114] Among them, TT safe TT is the time it takes for the rear vehicle to collide with the front vehicle if it continues to travel at the current speed and acceleration. min The safety threshold is set;

[0115] In the multi-vehicle collaboration scenario, a local maximum efficiency evaluation constraint is set, that is, the efficiency constraint is:

[0116] E local\_efficiency (t)≥E min ;

[0117] Among them, E min is the set minimum efficiency threshold.

[0118] Then, the cooperative vehicle selects the behavior with higher utility according to the value of the safety efficiency evaluation function, namely:

[0119]

[0120] In this embodiment, the Roadside Cooperative Control Unit (RCCU) plays a central role in the entire control process. The RCCU periodically senses the surrounding traffic environment, collects vehicle status information in real time, and performs efficient data processing and calculations. Based on this collected data and pre-set control logic, it generates corresponding control instructions and promptly broadcasts these instructions to the relevant vehicles. This periodic, unified planning and instruction distribution mechanism ensures the coordinated and stable operation of the entire transportation system.

[0121] Among them, through the unified scheduling of roadside units (RSUs) and combined with the kinematic characteristics of vehicles, a vehicle merging trajectory planning and control command generation module is constructed, aiming to achieve precise trajectory tracking and stable control of vehicles during the merging process.

[0122] Plan the vehicle's merging trajectory, including:

[0123] The purpose of longitudinal vehicle motion planning is to ensure that vehicles maintain a safe distance and reasonable speed during merging. The longitudinal vehicle motion planning includes:

[0124] Determine the vehicle's longitudinal velocity v long (t+Δt):

[0125] v long (t+Δt)=v long (t)+a long (t)·Δt;

[0126] Among them, v long (t) is the longitudinal velocity of the vehicle at time t, a long (t) is the longitudinal acceleration, Δt is the time step;

[0127] Determine the longitudinal position x of the vehicle long (t+Δt):

[0128] x long (t+Δt)=x long (t)+v long (t)·Δt;

[0129] Among them, x long (t) is the longitudinal position of the vehicle at time t;

[0130] In order to ensure the safe distance between vehicles during the merging process, a minimum safe time interval g is set. min , and calculate the expected speed v of the vehicle based on this desired (t):

[0131] v desired (t)=min(v long (t)+a max·Δt,v equilibrium );

[0132] Among them, v equilibrium is the equilibrium speed, a max is the maximum permissible acceleration;

[0133] The vehicle's lateral motion planning aims to ensure a smooth transition from the ramp to the main lane. The vehicle's lateral motion planning includes:

[0134] Determine the lateral position y of the vehicle lat (t+Δt) is:

[0135] y lat (t+Δt)=y lat (t)+v lat (t)·Δt;

[0136] Among them, y lat (t) is the lateral position of the vehicle at time t, v lat (t) is the lateral velocity;

[0137] In order to achieve a smooth lane change process, determine the lateral acceleration a lat (t) Restrictions:

[0138] a lat (t)≤a lat\_max ;

[0139] Among them, a lat\_max is the maximum permissible lateral acceleration.

[0140] Generate control commands, including:

[0141] Based on the longitudinal and lateral motion planning, the control commands are generated as follows:

[0142] For longitudinal control, generate acceleration commands:

[0143] a cmd\_long (t) = min(max(a long (t),a min ),a max );

[0144] Among them, a min and a max are the minimum and maximum permissible longitudinal acceleration, respectively;

[0145] For lateral control, generate steering commands:

[0146]

[0147] Among them, δ cmd\_lat(t) is the lateral steering command. The longitudinal direction is along the vehicle's direction of travel; the lateral direction is perpendicular to the vehicle's direction of travel.

[0148] The present invention proposes a collaborative ramp merging control method designed specifically for multi-lane mixed traffic flows. It mainly targets the complex ramp merging problems in scenarios such as highways and urban expressways, and innovatively constructs a hierarchical control architecture for vehicle-road collaboration. It can accurately and accurately predict the dynamic merging points of ramp vehicles and guide the intelligent behavior of collaborative vehicles in the main lane in real time, greatly improving the smoothness and safety of traffic flow. By realizing collaborative information sharing between vehicles and between vehicles and roadside facilities, the communication and collaborative scheduling capabilities of ICVs are fully utilized to optimize the vehicle merging process. Compared with traditional methods, the present invention has the following significant advantages:

[0149] 1. Through the roadside collaborative control unit (RCCU), unified scheduling and coordination of multiple autonomous vehicles and manually driven vehicles are achieved to ensure that vehicles pass in an orderly manner during the merging process. 2. Using V2V and V2I communication technologies, vehicles can exchange information in real time and collaboratively adjust driving paths and speeds, thereby avoiding delays caused by traditional traffic signal control. 3. Based on real-time traffic data, the vehicle's passage sequence and speed are dynamically adjusted to ensure that vehicles can cooperate in the optimal manner in merging scenarios and reduce traffic conflicts. 4. Through intelligent scheduling algorithms, while ensuring traffic safety, the vehicle's passage efficiency is improved, traffic delays are reduced, and potential traffic risks are reduced. 5. By optimizing the vehicle's driving trajectory, the frequency of sudden acceleration and braking is reduced, driving comfort is improved, and the smooth operation of traffic flow is ensured.

[0150] In summary, the goal of this invention is to create an efficient and safe collaborative merging control solution for future intelligent, connected, and hybrid transportation systems. This solution will promote the intelligent upgrade of transportation infrastructure, enhance the resilience of road networks, alleviate traffic congestion, and contribute to a safer, greener, and more intelligent transportation environment. Through rational design and optimization, this solution significantly improves the merging efficiency and safety of entrance ramps on multi-lane highways in mixed traffic environments, demonstrating promising practical application prospects.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A ramp merging method based on roadside coordinated control in a connected mixed traffic environment, characterized by: include: Determine the optimal merging point for merging vehicles from the ramp lane to the adjacent main lane and identify potential cooperative vehicles in the main lane; Construct a safety and efficiency evaluation model that combines vehicle type and driver response behavior to evaluate and decide on the optimal behavior of cooperative vehicles; Combined with the kinematic characteristics of the vehicle, the vehicle's merging trajectory is planned and control commands are generated to achieve trajectory tracking and control of the vehicle during the merging process.

2. The ramp merging method based on roadside coordinated control in a connected mixed traffic environment according to claim 1 is characterized by: Determine the potential cooperative vehicle V in the main lane according to the following formula: candidate (t): V candidate (t)={j|x j (t)≤x MV (t)+L comm ∧x j (t)≥x MV (t)-L comm }; Among them, x j (t) and x MV (t) represent the longitudinal positions of vehicle j and the merging vehicle at time t, L comm is the communication range length.

3. The ramp merging method based on roadside coordinated control in a connected mixed traffic environment according to claim 2 is characterized by: The predicted longitudinal distance of vehicle j at time t is determined according to the following formula: Among them, x merge\_start is the vertical coordinate of the starting point of the confluence area, x j (t) represents the longitudinal position of vehicle j at time t, v j (t) is the speed of vehicle j at time t, It is the predicted time for the merging vehicle to reach the starting point of the merging area at its current speed.

4. The ramp merging method based on roadside coordinated control in a connected mixed traffic environment according to claim 3 is characterized by: when and When , it indicates that vehicle j and its rear vehicle j+1 are located downstream and upstream of the starting point of the merging area, respectively. At this time, vehicle j is determined to be the leading cooperative vehicle, and its rear vehicle j+1 is determined to be the rear cooperative vehicle. The minimum safety distance constraints between the front and rear cooperative vehicles are set as follows: in, and are the minimum acceptable distances between the front and rear cooperative vehicles at time t, respectively, and are calculated as follows: Among them, v MV (t) and v RCV (t) are the speeds of the merging vehicle and the following cooperative vehicle at time t, is the minimum time interval for collaborative merging.

5. The ramp merging method based on roadside coordinated control in a connected mixed traffic environment according to claim 1 is characterized by: The safety efficiency evaluation model includes a safety efficiency evaluation function and evaluation constraints; The safety efficiency evaluation function is: Among them, U option1 (t) and U option2 (t) represents the utility value of the two choices of the cooperative vehicle at time t; and They represent the normalized values ​​of the safety index of the cooperative vehicle and its following vehicle, the preceding vehicle, and the following vehicle in the target lane at time t; and are the normalized acceleration values ​​of the cooperative vehicle at the current moment and the previous moment respectively; α, β, γ, ζ, η are weight coefficients; E local\_efficiency (t) is the local efficiency evaluation index; The evaluation constraints include safety constraints and efficiency constraints; The safety constraints are: TT safe ≥TT min ; Among them, TT safe TT is the time it takes for the rear vehicle to collide with the front vehicle if it continues to travel at the current speed and acceleration. min The safety threshold is set; The efficiency constraint is: E local\_efficiency (t)≥E min ; Among them, E min is the set minimum efficiency threshold.

6. The ramp merging method based on roadside coordinated control in a connected mixed traffic environment according to claim 5 is characterized by: The local efficiency evaluation index E is determined according to the following formula: local\_efficiency (t): Where N is the total number of vehicles in the cooperative area, v i (t) is the speed of vehicle i at time t, v desired (t) is the desired speed.

7. The ramp merging method based on roadside coordinated control in a connected mixed traffic environment according to claim 1 is characterized by: Plan the vehicle's merging trajectory, including: Vehicle longitudinal motion planning includes: Determine the longitudinal velocity v of the vehicle long (t+Δt): v long (t+Δt)=v long (t)+a long (t)·Δt; Among them, v long (t) is the longitudinal velocity of the vehicle at time t, a long (t) is the longitudinal acceleration, Δt is the time step; Determine the longitudinal position x of the vehicle long (t+Δt): x long (t+Δt)=x long (t)+v long (t)·Δt; Among them, x long (t) is the longitudinal position of the vehicle at time t; When vehicles merge into the vehicle, set the minimum safety time interval g min , and calculate the expected speed v of the vehicle based on this desired (t): v desired (t)=min(v long (t)+a max ·Δt,v equilibrium ); Among them, v equilibrium is the equilibrium speed, a max is the maximum permissible acceleration; Vehicle lateral motion planning includes: Determine the lateral position y of the vehicle lat (t+Δt) is: y lat (t+Δt)=y lat (t)+v lat (t)·Δt; Among them, y lat (t) is the lateral position of the vehicle at time t, v lat (t) is the lateral velocity; Determine the lateral acceleration a lat (t) Restrictions: a lat (t)≤a lat\_max ; Among them, a lat\_max is the maximum permissible lateral acceleration.

8. The ramp merging method based on roadside coordinated control in a connected mixed traffic environment according to claim 7 is characterized by: Generate control commands, including: Based on the longitudinal and lateral motion planning, the control commands are generated as follows: For longitudinal control, generate acceleration commands: a cmd\_long (t)=min(max(a long (t),a min ),a max ); Among them, a min and a max are the minimum and maximum permissible longitudinal acceleration, respectively; For lateral control, generate steering commands: Among them, δ cmd\_lat (t) is the lateral steering command.

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