A hierarchical multi-modal arterial coordination control optimization method for connected and automated vehicles (CAVs) in mixed traffic environment

Abstract

The application relates to the technical field of intelligent traffic management and control, and discloses a CAV hierarchical multi-modal trunk coordination control optimization method in a mixed traffic environment, and the specific steps are as follows: a head vehicle CAV communicates with a roadbed facility, judges the distance from the intersection, selects trajectory control or trajectory and signal cooperative control, simultaneously optimizes the trunk phase coordination quantity, minimizes the offset quantity of the trunk coordination, reduces the number of red light stop on the trunk, and improves the global traffic efficiency. The optimization control is specifically divided into three levels of a vehicle layer, an intersection layer and a trunk layer. The signal offset optimization quantity of the trunk layer is transmitted to the intersection layer, the intersection layer adjusts the signal lamp duration accordingly, and the vehicle layer further optimizes the driving trajectory of the driving vehicle according to the adjusted signal lamp duration. The application aims to realize efficient traffic of vehicles through the coordination control of the three levels, maximally reduce the number of stop caused by red light, reduce the traffic stop caused by the non-coordination of signals, and improve the traffic efficiency.

Description

Technical Field

[0001] This invention relates to the field of intelligent traffic management and control technology, and in particular to a CAV hierarchical multimodal trunk line coordinated control optimization method in a mixed traffic environment. Background Technology

[0002] With the acceleration of global urbanization, traffic problems have increasingly become one of the major challenges facing modern cities. The complexity of urban road networks and the continuous growth in the number of vehicles have placed unprecedented pressure on traffic management. Traditional traffic signal control methods mostly rely on fixed signal cycles and manual intervention, which proves inadequate when traffic flow is dynamically changing. Especially near intersections, the lack of coordination between traffic lights often leads to traffic congestion and prolonged vehicle stops, further exacerbating traffic delays, wasting significant amounts of energy, and increasing air pollution. This situation not only affects residents' travel efficiency but also has a negative impact on the urban economy.

[0003] Meanwhile, connected vehicles (CAVs) have made significant progress in recent years, offering new possibilities for the development of intelligent transportation systems. CAVs can acquire information on road conditions, traffic signals, and other vehicles through real-time communication between vehicles and infrastructure, thereby enabling more precise traffic control. Compared to traditional traffic management systems, CAVs can flexibly respond to fluctuations in traffic flow, avoid the limitations of traditional traffic lights, reduce stoppages and queues caused by traffic lights, and thus improve traffic flow and road efficiency.

[0004] While CAVs offer significant advantages in vehicle control and localized traffic optimization, relying solely on a single vehicle control or traffic light optimization strategy is insufficient to address the underlying problems of the entire transportation system. This is particularly true in urban road networks with multiple intersections, where traffic light coordination, traffic flow variations, and inter-level coordination all require comprehensive consideration.

[0005] Therefore, how to combine vehicle trajectory control with traffic signal optimization to form a multi-level, coordinated and efficient traffic flow control system has become a key issue in the research of intelligent transportation systems. Summary of the Invention

[0006] To address the shortcomings of traditional signal control, which relies on fixed cycles and cannot dynamically respond to changes in traffic flow, leading to excessively long queues and increased delays at intersections, and the fact that existing CAV (Consumer Vehicle) collaborative solutions often focus on optimizing single vehicles or single intersections and lack global coordination at the arterial level, making it difficult to achieve green wave traffic flow, this invention proposes a hierarchical multimodal arterial coordinated control optimization method for CAVs in mixed traffic environments. By optimizing the trajectory control and signal coordination of intelligent connected vehicles (CAVs), this method minimizes traffic pauses caused by traffic light signals on arterial roads, especially optimizing the green light time when vehicles pass through intersections, achieving smoother traffic flow, reducing traffic congestion, and improving overall road safety and traffic efficiency.

[0007] The CAV hierarchical multimodal trunk line coordinated control optimization method proposed in this invention includes:

[0008] Step 1: The lead vehicle (CAV) communicates with the roadbed facilities to determine the distance to the intersection, selects trajectory control or trajectory and signal coordinated control, and optimizes the phase coordination of the trunk line to minimize the offset of the trunk line coordination, reduce the number of red light vehicles on the trunk line, and improve the overall traffic efficiency. Specifically, the optimization control is carried out at three levels: vehicle layer, intersection layer and trunk line layer.

[0009] Step 2: Based on real-time traffic flow and traffic signals, the vehicle layer CAV selects appropriate trajectory control to reduce unnecessary stops or decelerations, enabling vehicles to pass through intersections without stopping.

[0010] Step 3: At the intersection level, the signal light duration is adjusted in real time based on the number of vehicles, traffic flow, and estimated arrival time to ensure smooth traffic and avoid congestion.

[0011] Step 4: The trunk layer summarizes the signal phase offset information of each intersection, coordinates the signal phase offset, and takes into account the number of queues at each intersection and the delay time to pass through each intersection to find the optimal signal timing scheme.

[0012] Step 5: The signal offset optimization amount of the trunk line layer is transmitted to the intersection layer. The intersection layer adjusts the signal light duration accordingly, and the vehicle layer further optimizes the driving trajectory of vehicles based on the adjusted signal light duration. Through the coordinated control of the three levels, the aim is to maximize the traffic efficiency of vehicles on the trunk line.

[0013] Furthermore, in step 1, when the intersection spacing is greater than 300 meters, the lead vehicle (CAV) has sufficient time and space to perform independent trajectory optimization. Through communication with road infrastructure, the lead vehicle can obtain information such as road conditions and traffic flow ahead, thereby planning the optimal route and speed to improve driving efficiency and fuel economy. When the intersection spacing is less than 300 meters, the mutual influence between vehicles and the control of traffic lights become more important. At this time, the lead vehicle (CAV) needs to coordinate with the traffic lights to ensure that vehicles can pass through the intersection smoothly, avoiding unnecessary stops and accelerations, thereby reducing traffic congestion and emissions.

[0014] Furthermore, in step 1, while performing vehicle trajectory control or coordinated control of vehicle trajectory and traffic lights, the system also needs to consider the phase coordination of the main road. Phase coordination includes parameters such as red light duration and optimized signal phase delay to ensure that vehicles on the main road can travel smoothly and reduce delays caused by stopping at red lights;

[0015] To achieve the above objectives, the system is divided into three levels for optimized scheduling: the vehicle level, the intersection level, and the arterial level. The vehicle level is mainly responsible for the real-time control and adjustment of vehicle trajectories; the intersection level is responsible for the optimization and adjustment of traffic light phases; and the arterial level is responsible for the optimization and coordination of traffic flow along the entire arterial road.

[0016] In the vehicle layer of step 2, when both the time and space distances of the road segment are sufficient, the CAV vehicles select two-mode switching formation control to optimize the acceleration of the lead CAV, as expressed by equation (1):

[0017] (1)

[0018] In equation (1), This indicates the moment when the lead vehicle (CAV) enters the intersection area. This indicates the time when the first vehicle arrives at the stop line at the intersection. The terminal cost function represents the vehicle's speed error, position error, and acceleration derivative. The process cost function represents the total energy consumption of the hybrid fleet of vehicles, including CAV power consumption and HDV fuel consumption.

[0019] Represented as equation (2):

[0020] (2)

[0021] In equation (2), Indicates that the lead CAV is in Location at any given moment For the desired final position of the lead car, Indicates vehicle exist The speed of time, This represents the desired speed of the mixed convoy. Represents the derivative of acceleration. , and These are the error weighting coefficients for position, velocity, and acceleration, respectively.

[0022] Represented as equation (3):

[0023] (3)

[0024] In equation (3), This indicates the total energy consumption of new energy intelligent connected vehicles (CAVs) within a hybrid fleet. Total fuel consumption of conventionally fuel-powered manually driven vehicles (HDVs) within a hybrid fleet. This indicates the number of new energy intelligent connected vehicles (CAVs) in a hybrid fleet. This indicates the number of conventionally fuel-powered, manually driven vehicles (HDVs) in a hybrid fleet. and Indicates the weighting coefficient;

[0025] In the vehicle layer of step 2, when there are too many disturbances on the road and formation control cannot be effectively implemented, single-vehicle control is used to optimize the trajectory of each CAV on the road. For single-vehicle control, the predictive cruise control algorithm (PCC) is used to optimize the speed of the lead CAV, that is, by obtaining the traffic light timing in advance to adjust the CAV speed. The selection of the speed range is shown in equation (8):

[0026] (8)

[0027] In equation (8), and These are the start times for the next red and green lights, respectively. and These are the minimum and maximum speeds of the CAV (Car Access Vehicle). Taking the intersection of these two values ​​yields the desired fastest arrival time.

[0028] The intersection layer in step 3 is mainly responsible for estimating and predicting traffic conditions, optimizing traffic signal parameters, and the arrival time of CAVs. The estimate of the maximum number of vehicles that can pass through during the current green light time can be expressed by equations (9) and (10):

[0029] (9)

[0030] (10)

[0031] In equations (9) and (10), This indicates the stable headway between two adjacent workshops. This indicates the maximum number of vehicles that can pass through during the current green light period.

[0032] Furthermore, regarding the time it takes for the CAV to arrive at the intersection... Based on the distance between the lead vehicle's CAV and the stop line and target speed To determine this, we can express it using equation (11):

[0033] (11)

[0034] For intersections in the road section The calculation of the number of vehicles that can proceed straight through during the green light period is based on the previous intersection. Entering the intersection The number of vehicles going straight and the intersection The number of vehicles turning right minus the number of vehicles at the intersection The number of vehicles changing lanes to the left and right is composed of the number of vehicles changing lanes to the right, specifically represented by equation (12):

[0035] (12)

[0036] In equation (12), Indicates an intersection The number of vehicles going straight. and They represent the intersections. The number of vehicles going straight and turning right. and These represent the number of lane changes a vehicle makes to the left or right, respectively.

[0037] When entering the intersection There are too many vehicles, and vehicles in the section of road are waiting for the fixed green light time. If the passage is not fully completed, adjust the green light duration. Extend the green light time appropriately.

[0038] Let the maximum adjustable green light time be ,when At that time, the green light duration will be extended to The convoy can If all passes within the allotted time, the green light duration will be extended to [time period missing]. At this point, team n cannot be in If all passes within the time limit, they will be divided into rows, with the front row arranged according to... Optimize the trajectory of the mixed convoy through intersections, with rear vehicles adjusting their arrival speed based on the arrival time of the next green light, and stopping to wait when necessary;

[0039] The constraint on the green light time is expressed as follows (13):

[0040] (13)

[0041] To enable vehicles to pass through intersections continuously without stopping, a sequential coordinated control method is used to adjust the signal phase difference. The appropriate phase is determined based on the desired vehicle speed and distance to the intersection, and the light illuminates again when the green light appears, and so on, allowing vehicles to smoothly cross intersections along the main road. Phase difference The calculation is expressed by formula (14):

[0042] (14)

[0043] In equation (14), This represents the phase difference between two adjacent signals. Indicates an intersection Intersection The distance between them;

[0044] The trunk layer in step 4 is mainly responsible for coordinating the offset of the trunk phase to promote bidirectional coordination. While optimizing the overall offset of the trunk layer, the number of queues at each intersection and the delay time through each intersection are considered to find the optimal signal timing scheme.

[0045] The objective function at the trunk line level is expressed by equation (15):

[0046] (15)

[0047] In equation (11), Indicates the first Signal offset at each intersection This indicates the number of people queuing at each intersection on the main road. This indicates the delay time for a convoy to pass through a single intersection. , and These are weighting coefficients. Indicates the number of intersections;

[0048] The constraint on the signal period is expressed as equation (16):

[0049] (16)

[0050] In equation (16), Indicates the first The cycle of each intersection, and They represent the first The duration of green and red lights at each intersection;

[0051] Delay time This can be expressed using equation (17):

[0052] (17)

[0053] In equation (17), This indicates the green light ratio, which is the ratio of effective green light time to the cycle. Indicates saturation; Indicates the number of vehicles in the queue; This indicates a correction factor for mixed traffic.

[0054] Transforming the trunk line signal coordination problem into an optimization problem allows for the simultaneous optimization of overall offset, number of stops, and delay time, achieving two-way coordination and effectively improving trunk line traffic efficiency while reducing congestion and delays.

[0055] Compared with related technologies, the CAV hierarchical multimodal trunk line coordinated control optimization method proposed in this invention has the following advantages:

[0056] This invention constructs a hierarchical multimodal arterial coordinated control optimization method for CAVs in mixed traffic environments. Through hierarchical progressive optimization at the vehicle, intersection, and arterial levels, it aims to maximize traffic efficiency. At the vehicle level, a combination of dual-mode platooning control and single-vehicle trajectory planning is employed to enable CAVs to pass through intersections without stopping. At the intersection level, signal timing is dynamically adjusted based on real-time traffic flow to eliminate local congestion points. At the arterial level, phase offset optimization achieves green wave coordination, reducing the number of global stops and delays. This three-layer architecture forms a closed-loop optimization through information sharing, ensuring smooth vehicle operation while achieving global coordination at the arterial level, minimizing vehicle stalls caused by red lights, thereby improving regional traffic efficiency. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of the simulation scenario of the present invention;

[0058] Figure 2 A diagram illustrating the CAV hierarchical multimodal trunk line coordinated control optimization method under mixed traffic conditions;

[0059] Figure 3 A schematic diagram of the vehicle trajectory for the lead CAV;

[0060] Figure 4 Flowchart for adjusting green light time. Detailed Implementation

[0061] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0062] Figure 1 This is a schematic diagram of a simulation scenario for the present invention, in which several intersections with varying distances are distributed along the main road. Given the differences in distance between these intersections, vehicles will adopt corresponding differentiated control strategies under different distance conditions. Figure 2 The diagram illustrates the CAV hierarchical multimodal trunk line coordinated control optimization method for mixed traffic environments according to the present invention. The specific steps are as follows:

[0063] Step 1: The lead vehicle (CAV) communicates with the roadbed infrastructure to determine the distance to the intersection, selects trajectory control or trajectory and signal coordinated control, and optimizes the arterial phase coordination to minimize the offset of arterial coordination, reduce the number of red-light traffic on the arterial line, and improve overall traffic efficiency. Specifically, optimization control is carried out at three levels: vehicle layer, intersection layer, and arterial line layer.

[0064] Step 2: Based on real-time traffic flow and traffic signals, the vehicle layer CAV selects appropriate trajectory control to reduce unnecessary stops or decelerations, enabling vehicles to pass through intersections without stopping.

[0065] Step 3: At the intersection level, the signal light duration is adjusted in real time based on the number of vehicles, traffic flow, and estimated arrival time to ensure smooth traffic and avoid congestion.

[0066] Step 4: The trunk layer summarizes the signal phase offset information of each intersection, coordinates the signal phase offset, and takes into account the number of queues at each intersection and the delay time to pass through each intersection to find the optimal signal timing scheme.

[0067] Step 5: The signal offset optimization amount of the trunk line layer is transmitted to the intersection layer. The intersection layer adjusts the signal light duration accordingly, and the vehicle layer further optimizes the driving trajectory of vehicles based on the adjusted signal light duration. Through the coordinated control of the three levels, the aim is to maximize the traffic efficiency of vehicles on the trunk line.

[0068] Furthermore, in step 1, when the intersection spacing is greater than 300 meters, the lead vehicle (CAV) has ample time and space to perform independent trajectory optimization. Through communication with road infrastructure, the lead vehicle can obtain information such as road conditions and traffic flow ahead, thereby planning the optimal speed trajectory to improve driving efficiency and fuel economy. When the intersection spacing is less than 300 meters, the mutual influence between vehicles and the control of traffic lights become more important. At this time, the lead vehicle (CAV) needs to coordinate with the traffic lights, appropriately adjusting the signal duration to ensure vehicles can smoothly pass through the intersection, avoiding unnecessary stops and accelerations / decelerations, and reducing traffic congestion and emissions.

[0069] Furthermore, in step 1, while performing vehicle trajectory control or coordinated control of vehicle trajectory and traffic lights, the system also needs to consider the phase coordination of the trunk line. Phase coordination optimization, in addition to optimizing the overall offset, also needs to consider parameters such as the number of vehicles queuing at each intersection and the delay time at each intersection, to ensure that vehicles on the trunk line can travel smoothly and reduce delays caused by stopping at red lights.

[0070] To achieve the above goals, the road segment is optimized and scheduled at three levels: the vehicle level, the intersection level, and the arterial level. The vehicle level is mainly responsible for the real-time control and adjustment of vehicle trajectories; the intersection level is responsible for the optimization and adjustment of traffic light phases; and the arterial level is responsible for the optimization and coordination of traffic flow along the entire arterial road.

[0071] In step 2, at the vehicle level, the lead vehicle (CAV) selects an appropriate trajectory control based on road conditions. Figure 3 The diagram shows the trajectory of the lead vehicle CAV. The purpose of trajectory control is to ensure that the convoy led by the lead vehicle CAV passes through the intersection just as the green light turns on, achieving non-stop passage through the intersection. Specifically, there are two scenarios: when both the time and space distances of the road segment are sufficient, the CAV vehicles select a two-mode switching convoy control to optimize the acceleration of the lead vehicle CAV, expressed by equation (1):

[0072] (1)

[0073] Furthermore, Represented as equation (2):

[0074] (2)

[0075] In equation (2), Indicates that the lead CAV is in Location at any given moment For the desired final position of the lead car, Indicates vehicle exist The speed of time, This represents the desired speed of the mixed convoy. Represents the derivative of acceleration. , and These are the error weighting coefficients for position, velocity, and acceleration, respectively.

[0076] Furthermore, Represented as equation (3):

[0077] (3)

[0078] In equation (3), This indicates the total energy consumption of new energy intelligent connected vehicles (CAVs) within a hybrid fleet. Total fuel consumption of conventionally fuel-powered manually driven vehicles (HDVs) within a hybrid fleet. This indicates the number of new energy intelligent connected vehicles (CAVs) in a hybrid fleet. This indicates the number of conventionally fuel-powered, manually driven vehicles (HDVs) in a hybrid fleet. and Indicates the weighting coefficient;

[0079] In equation (3), and The relationship satisfies the following equation (4):

[0080] (4)

[0081] In equation (4), Indicates the number of mixed fleets;

[0082] Furthermore, the total power consumption of new energy intelligent connected vehicles (CAVs) in the hybrid fleet in equation (3) It can be expressed as the following formula (5):

[0083] (5)

[0084] In equation (5), Indicates vehicle mass. Represents gravitational acceleration. Indicates the rolling resistance coefficient of the vehicle. Indicates the road slope. Indicates the air drag coefficient. Indicates the frontal area of ​​a car. This indicates the vehicle mass conversion factor. Indicates the efficiency of the vehicle's transmission system;

[0085] Furthermore, the total fuel consumption of conventional fuel-consuming manually driven vehicles (HDVs) within the mixed fleet in equation (3) Expressed as equations (6) and (7):

[0086] (6)

[0087] (7)

[0088] In equations (6) and (7), This indicates a constant idle fuel rate. An efficiency parameter that links fuel consumption to the energy provided by the engine. An efficiency parameter that relates the fuel consumed during positive acceleration to the performance of inertial properties and acceleration. This represents the total traction force required to drive the vehicle; it is the sum of resistance, inertial force, and gradient force. Indicates the rolling coefficient. Indicates engine parameters. This represents the aerodynamic drag parameter. This represents the percentage slope where the downhill slope is negative.

[0089] In the vehicle layer of step 2, when there are too many disturbances on the road and formation control cannot be effectively implemented, single-vehicle control is used to optimize the trajectory of each CAV on the road. For single-vehicle control, the predictive cruise control algorithm (PCC) is used to optimize the speed of the lead CAV, that is, by obtaining the traffic light timing in advance to adjust the CAV speed. The selection of the speed range is shown in equation (8):

[0090] (8)

[0091] In equation (8), and These are the start times for the next red and green lights, respectively. and These are the minimum and maximum speeds of the CAV (Car Access Vehicle). Taking the intersection of these two values ​​will give the desired fastest arrival time.

[0092] After obtaining the desired vehicle speed in equation (8), the vehicle's second-order kinematics are modeled, and the desired speed is tracked using MPC. The Gaussian pseudospectral method is then used to solve the problem.

[0093] The intersection layer in step 3 is mainly responsible for estimating and predicting traffic conditions, optimizing traffic signal parameters, and determining the arrival time of CAVs. The estimate of the maximum number of vehicles that can pass through during the current green light period can be expressed using equations (9) and (10):

[0094] (9)

[0095] (10)

[0096] In equations (9) and (10), This indicates the stable headway between two adjacent workshops. This indicates the maximum number of vehicles that can pass through during the current green light period;

[0097] Furthermore, regarding the time it takes for the CAV to arrive at the intersection... Based on the distance between the lead vehicle's CAV and the stop line and target speed To determine this, we can express it using equation (11):

[0098] (11)

[0099] For intersections in the road section The calculation of the number of vehicles that can proceed straight through during the green light period is based on the previous intersection. Entering the intersection The number of vehicles going straight and the intersection The number of vehicles turning right minus the number of vehicles at the intersection The number of vehicles changing lanes to the left and right is composed of the number of vehicles changing lanes to the right, specifically represented by equation (12):

[0100] (12)

[0101] In equation (12), Indicates an intersection The number of vehicles going straight. and They represent the intersections. The number of vehicles going straight and turning right. and These represent the number of lane changes a vehicle makes to the left or right, respectively.

[0102] When entering the intersection There are too many vehicles, and vehicles in the section of road are waiting for the fixed green light time. If the passage is not possible within the designated area, the green light duration should be adjusted. The number of vehicles passing through reaches its maximum within the adjustable time period. Figure 3 Here is a flowchart for adjusting green light time;

[0103] Let the maximum adjustable green light time be ,when At that time, the green light duration will be extended to The convoy can If all passes within the allotted time, the green light duration will be extended to [time period missing]. At this point, team n cannot be in If all passes within the time limit, they will be divided into rows, with the front row arranged according to... Optimize the trajectory of the mixed convoy through intersections, with rear vehicles adjusting their arrival speed based on the arrival time of the next green light, and stopping to wait when necessary;

[0104] The constraint on the green light time is expressed as follows (13):

[0105] (13)

[0106] To enable vehicles to pass through intersections continuously without stopping, a sequential coordinated control method is used to adjust the signal phase difference. The appropriate phase is determined based on the desired vehicle speed and distance to the intersection, and the light illuminates again when the green light appears, and so on, allowing vehicles to smoothly cross intersections along the main road. Phase difference The calculation is expressed by formula (14):

[0107] (14)

[0108] In equation (14), This represents the phase difference between two adjacent signals. Indicates an intersection Intersection The distance between them;

[0109] The trunk layer in step 4 is mainly responsible for coordinating the offset of the trunk phase to promote bidirectional coordination. While considering the overall offset of the trunk layer, it also takes into account the number of queues at each intersection and the delay time at each intersection to find the optimal signal timing scheme.

[0110] The objective function at the trunk line level is expressed by equation (15):

[0111] (15)

[0112] In equation (15), Indicates the first Signal offset at each intersection This indicates the number of people queuing at each intersection on the main road. This indicates the delay time for a convoy to pass through a single intersection. , and These are weighting coefficients. Indicates the number of intersections;

[0113] The constraint on the signal period is expressed as equation (16):

[0114] (16)

[0115] In equation (16), Indicates the first The cycle of each intersection, and They represent the first The duration of green and red lights at each intersection;

[0116] Delay time This can be expressed using equation (17):

[0117] (17)

[0118] In equation (17), This indicates the green light ratio, which is the ratio of effective green light time to the cycle. Indicates saturation; Indicates the number of vehicles in the queue; This indicates a correction factor for mixed traffic.

[0119] Transforming the trunk line signal coordination problem into an optimization problem allows for the simultaneous optimization of overall offset, number of stops, and delay time, achieving two-way coordination and effectively improving trunk line traffic efficiency while reducing congestion and delays.

[0120] The signal offset optimization amount of the trunk layer is transmitted to the intersection layer. The intersection layer adjusts the signal light duration accordingly, and the vehicle layer further optimizes the driving trajectory of vehicles based on the adjusted signal light duration. Through the coordinated control of the three levels, efficient vehicle passage is achieved, the frequency of stops caused by red lights is greatly reduced, traffic congestion caused by signal incoordination is eliminated, and the efficiency of traffic operation is comprehensively improved.

[0121] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for CAV hierarchical multi-modal arterial coordination control optimization in a mixed traffic environment, characterized in that, The method comprises the following steps: Step 1: The head vehicle CAV communicates with the roadbed facilities, judges the distance from the intersection, selects trajectory control or trajectory and signal coordinated control, and optimizes the main line phase coordination to minimize the offset of the main line coordination, reduce the number of red light vehicles on the main line, and improve the overall traffic efficiency. The optimization control is specifically divided into three levels of vehicle layer, intersection layer and main line layer; Step 2: The vehicle layer selects appropriate trajectory control according to real-time traffic flow and traffic signals, reduces unnecessary stopping or deceleration, and enables the vehicle to pass through the intersection without stopping; Step 3: The intersection layer adjusts the signal light duration in real time according to the number of vehicles, traffic flow and predicted arrival time to ensure smooth traffic and avoid congestion; Step 4: The main line layer collects the signal phase offset information of each intersection, coordinates the offset of the signal phase, and considers the number of queues at each intersection and the delay time through each intersection to find the best signal timing scheme; Step 5: The signal offset optimization amount of the main line layer is transmitted to the intersection layer, and the intersection layer adjusts the signal light duration accordingly, and the vehicle layer further optimizes the driving trajectory of the vehicle according to the adjusted signal light duration.

2. The CAV hierarchical multi-modal arterial coordination control optimization method in a mixed traffic environment of claim 1, wherein, In step 1, the control method is as follows: When the distance between intersections is greater than 300 meters, the head vehicle CAV optimizes the trajectory independently; when it is less than 300 meters, it is controlled in coordination with the signal light; at the same time, the main line phase coordination is considered, including the green light time and signal phase delay, to ensure smooth vehicle driving; the system is divided into vehicle layer, intersection layer and main line layer, which are responsible for trajectory control, signal light optimization and main line signal phase coordination respectively.

3. The CAV hierarchical multi-modal arterial coordination control optimization method in a mixed traffic environment of claim 1, wherein, The vehicle layer control method in step 2 is as follows: When the time distance and spatial distance of the road section are sufficient, the CAV selects two-mode switching platoon control to optimize the acceleration of the head vehicle CAV: ， wherein, denotes the time when the lead CAV enters the intersection region, denotes the time when the lead vehicle reaches the stop line at the intersection, denotes the terminal cost function, which considers the speed error, position error, and acceleration derivative of the vehicle, denotes the process cost function, which is the total energy consumption of the mixed platoon vehicles, including the CAV electric consumption and the HDV fuel consumption; When there are too many disturbances on the road and the distance between intersections is short, platoon control cannot be effectively implemented, so single vehicle control is used to optimize the trajectory of each CAV on the road, and the predicted cruise control algorithm (PCC) is selected to optimize the speed of the head vehicle CAV: ， where, and are the start time of the next red and green light, respectively, and are the minimum and maximum values of the CAV's own vehicle speed, respectively, the intersection of which gives the desired optimal speed.

4. The CAV hierarchical multi-modal arterial coordination control optimization method in a mixed traffic environment of claim 1, wherein, The intersection layer control method in step 3 is as follows: The intersection layer is responsible for estimating and predicting the traffic state, calculating the arrival time of CAV, and optimizing the traffic signal parameters; for the estimation of the maximum number of vehicles that can pass through during the current green light time: ， ， In the formula, denotes the stable headway between two adjacent vehicles, denotes the maximum number of vehicles that can pass within the current green light time; Time at which the lead CAV reaches the junction : ， When entering the intersection There are too many vehicles, and vehicles in the section of road are waiting for the fixed green light time. If the passage is not fully completed, adjust the green light duration. Extend the green light time appropriately; let the maximum adjustable green light time be... ,when At that time, the green light duration will be extended to The convoy can If all passes within the allotted time, the green light duration will be extended to [time period missing]. At this point, team n cannot be in If all passes within the time limit, they will be divided into rows, with the front row arranged according to... The mixed convoy trajectories are optimized to pass through intersections, with rear vehicles adjusting their arrival speed based on the arrival time of the next green light, and stopping to wait when necessary.

5. The CAV hierarchical multi-modal arterial coordination control optimization method in a mixed traffic environment of claim 1, wherein, The main line layer control method in step 4 is as follows: The main line layer is responsible for coordinating the offset of the main line phase to promote two-way coordination; while optimizing the overall offset of the main line layer, the number of queues at each intersection and the delay time through each intersection are considered to find the best signal timing scheme, and the objective function is as follows: ， signal offset amount of the nth intersection, signal offset amount of the nth intersection, queuing number of each intersection on the arterial road, delay time of a vehicle platoon passing through a single intersection, , and are weight coefficients, number of intersections; wherein the delay time is calculated as follows: ， denotes the green ratio, i.e., the ratio of effective green time to cycle; denotes the saturation; denotes the number of queued vehicles; denotes the correction factor of mixed traffic; the arterial signal coordination problem is converted into an optimization problem to simultaneously optimize the overall offset, the number of stops, and the delay time, achieving two-way coordination.

6. The CAV hierarchical multi-modal arterial coordination control optimization method in a mixed traffic environment of claim 1, wherein, The three-level coordinated control optimization in step 5 is as follows: The vehicle layer is responsible for regulating the driving trajectory of CAV, the intersection layer flexibly adjusts the signal timing according to the real-time traffic condition, and the main line layer is responsible for coordinating the signal phase relationship of each intersection to ensure the smoothness of the entire traffic system; The three-level coordinated control realizes efficient traffic of vehicles, reduces the frequency of stopping caused by red light, and eliminates traffic congestion caused by uncoordinated signals.

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