CAV layered multi-mode trunk line coordination control optimization method in mixed traffic environment

Abstract

The invention relates to the technical field of intelligent traffic management and control, and discloses a CAV hierarchical multi-mode trunk line coordination control optimization method in a mixed traffic environment, and the method comprises the following specific steps: enabling a head vehicle CAV to communicate with a roadbed facility, judging the distance between the head vehicle CAV and an intersection, selecting track control or track and signal coordination control, and optimizing the trunk line phase coordination quantity; the offset of trunk line coordination is minimized, the number of red light parking times on the trunk line is reduced, and the global traffic efficiency is improved. Specifically, optimization control is carried out on three levels including a vehicle layer, a signal layer and a trunk line layer. The signal offset optimization quantity of the trunk line layer is transmitted to the signal layer, the signal layer properly adjusts the time length of the signal lamp according to the signal offset optimization quantity, and the vehicle layer further optimizes the driving track of the vehicle according to the adjusted time length of the signal lamp. Through coordination control of three levels, efficient passing of vehicles is achieved, the number of parking times caused by red lights is reduced to the maximum extent, traffic pauses caused by signal discordance are reduced, and the traffic efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation management and control, and particularly to an optimization method for CAV hierarchical multi-modal arterial coordination control in a mixed traffic environment. Background Art

[0002] With the acceleration of the global urbanization process, traffic problems have increasingly become one of the major challenges faced by modern cities. The complexity of urban road networks and the continuous growth of vehicle numbers have imposed unprecedented pressure on traffic management. Most traditional traffic signal control methods rely on fixed signal cycles and manual intervention, which prove ineffective in the face of dynamic traffic flow changes. Especially near intersections, due to the incoordination of signal lights, traffic jams and long vehicle stops often occur, further exacerbating traffic delays, wasting a large amount of energy, and increasing air pollution. This situation not only affects the travel efficiency of residents but also has a negative impact on the urban economy.

[0003] Meanwhile, significant progress has been made in connected and automated vehicles (CAVs) in the past few years, providing new possibilities for the development of intelligent transportation systems. CAVs can obtain road conditions, traffic signals, and information of other vehicles through real-time communication between vehicle-to-vehicle and vehicle-to-infrastructure, and thus achieve more precise traffic control. Compared with traditional traffic management systems, CAVs can flexibly respond to traffic flow fluctuations, avoid the limitations of traditional signal lights, reduce stops and queues caused by traffic signals, and thereby improve traffic flow and road traffic efficiency.

[0004] Although CAVs have significant advantages in single-vehicle control and local traffic optimization, it is still difficult to solve potential problems in the entire traffic system by relying solely on single vehicle control or signal light optimization strategies. Especially in urban road networks with multiple intersections, the coordination of signal lights, traffic flow changes, and cooperative operations between different levels need to be comprehensively considered.

[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 solve the technical problems that traditional signal control relies on fixed cycles and cannot dynamically respond to traffic flow changes, resulting in excessive queuing and increased delays at intersections, and that existing CAV collaborative solutions mostly focus on single-vehicle or single-intersection optimization and lack global coordination at the arterial level, making it difficult to achieve green wave passing, the present invention proposes a hierarchical multi-modal arterial coordination control optimization method for CAVs in a mixed traffic environment. Through the trajectory control and signal coordination optimization of connected and autonomous vehicles (CAVs), it minimizes traffic stops caused by traffic lights on the arterial, especially optimizes the green light time when the traffic flow passes through intersections, achieves smoother traffic flow, reduces traffic congestion, and improves overall road safety and traffic efficiency.

[0007] The hierarchical multi-modal arterial coordination control optimization method for CAVs in a mixed traffic environment proposed by the present invention includes:

[0008] Step 1: The leading CAV communicates with the roadside facilities, judges the distance from the intersection, selects trajectory control or trajectory and signal collaborative control, and optimizes the arterial phase coordination to minimize the offset of the arterial coordination, reduce the number of red-light vehicles on the arterial, and improve the global traffic efficiency. Specifically, it is optimized and controlled at three levels: the vehicle level, the signal level, and the arterial level.

[0009] Step 2: At the vehicle level, according to the real-time traffic flow and traffic signals, the CAV selects an appropriate trajectory control to reduce unnecessary stops or decelerations, enabling the vehicle to pass through the intersection without stopping.

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

[0011] Step 4: The arterial level aggregates the signal phase offset information of each intersection, coordinates the offset of the signal phase, and at the same time considers the queuing quantity at each intersection and the delay time passing through each intersection to find the optimal signal timing plan.

[0012] Step 5: The signal offset optimization amount at the arterial level is transmitted to the signal level, and the signal level appropriately adjusts the signal light duration accordingly. The vehicle level further optimizes the driving trajectory of the passing vehicles according to the adjusted signal light duration. Through the collaborative control of the three levels, the aim is to maximize the traffic passing efficiency on the arterial.

[0013] Further, in step 1, when the intersection spacing is greater than 300 meters, the leading vehicle CAV has sufficient time and space for independent trajectory optimization. Through communication with road infrastructure, the leading vehicle can obtain information such as the condition of the road ahead and traffic flow, so as to plan the optimal driving 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 leading vehicle CAV needs to cooperate with traffic lights for control to ensure that vehicles can pass through intersections smoothly, avoid unnecessary stops and accelerations, thereby reducing traffic congestion and emissions;

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

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

[0016] In the vehicle layer of step 2, when both the time distance and space distance of the road section are sufficient, the CAV vehicle selects two-mode switching formation control to optimize the acceleration of the leading vehicle CAV, which is expressed by Equation (1):

[0017]

[0018] In Equation (1), \(t_0\) represents the moment when the leading vehicle CAV enters the intersection area, \(t\) f represents the moment when the leading vehicle reaches the stop line of the intersection, represents the terminal cost function, which considers the speed error, position error, and acceleration derivative of the vehicle. \(F(v(t),a(t))\) represents the process cost function, that is, the total energy consumption of the mixed vehicle fleet, including the electricity consumption of CAVs and the fuel consumption of HDVs;

[0019] It is expressed as Equation (2):

[0020]

[0021] In Equation (2), \(x_0(t\) f ) represents the position of the leading vehicle CAV at time \(t\) f , \(x\) target is the expected final state position of the leading vehicle, \(v\) i (t f ) represents vehicle \(i\) at time \(t\)f Velocity at a moment, v target Indicates the expected speed of the mixed vehicle platoon, Indicates the derivative of acceleration, and ω1, ω2, and ω3 represent the error weight coefficients of position, velocity, and acceleration respectively;

[0022] F(v(t), a(t)) is expressed as Equation (3):

[0023]

[0024] In Equation (3), f e (v, a) represents the total power consumption of new energy intelligent connected vehicles (CAVs) in the mixed vehicle platoon, and f f (v, a) is the total fuel consumption of traditional fuel human-driven vehicles (HDVs) in the mixed vehicle platoon. n represents the number of new energy intelligent connected vehicles (CAVs) in the mixed vehicle platoon, m represents the number of traditional fuel human-driven vehicles (HDVs) in the mixed vehicle platoon, and λ1 and λ2 represent the weight coefficients;

[0025] In the vehicle layer of Step 2, when there are too many disturbances on the road and platoon 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 leading CAV, that is, the speed of the CAV is adjusted by obtaining the signal timing in advance. The selection of the speed range is shown in Equation (8):

[0026]

[0027] In Equation (8), r i and g i are the start times of the next red light and green light respectively, v min and v max are the minimum and maximum values of the CAV's own vehicle speed respectively. Taking the intersection of the two can obtain the expected fastest arrival time;

[0028] In the intersection layer of Step 3, it is mainly responsible for estimating and predicting the traffic state, optimizing traffic signal parameters, and the arrival time of CAVs; the estimation of the maximum number of vehicles that can pass within the current green light time can be expressed by Equation (9) and Equation (10):

[0029]

[0030] In Equation (9) and Equation (10), d s (v) represents the stable headway between adjacent vehicles, and N represents the maximum number of vehicles that can pass within the current green light time.

[0031] Furthermore, for the time t when the CAV arrives at the intersection f, determined according to the distance x between the leading CAV and the stop line k and the target speed v obj and can be expressed by Equation (11):

[0032]

[0033] For the calculation of the number of vehicles that can go straight through intersection i during the green light time on a road section, it is composed of the number of vehicles going straight from the previous intersection i - 1 into intersection i and the number of vehicles turning right at intersection i - 1, minus the number of vehicles changing lanes to the left and right at intersection i, and is specifically expressed by Equation (12):

[0034] N i,s = N i-1,s + N i-1,l -(N i,cr + N i,cl ) (12)

[0035] In Equation (12), N i,s represents the number of vehicles going straight through intersection i, N i-1,s and N i-1,l respectively represent the number of vehicles going straight and turning right at intersection i - 1, N i,cr and N i,cl respectively represent the number of vehicles changing lanes to the left and right;

[0036] When the number of vehicles entering intersection i is too large and the vehicles on the road section cannot all pass within the fixed green light time T gi , adjust the green light duration T gi and appropriately extend the green light time.

[0037] Let the maximum adjustable green light time be T gimax . When T gi < T gimax , extend the green light duration to T gi , and the vehicle fleet can all pass within the time T gi ; otherwise, extend the green light duration to T gimax . At this time, the vehicle fleet n cannot all pass within the time T gimax , then perform row-by-row processing. The front row optimizes the trajectory of the mixed vehicle fleet to pass through the intersection according to T gimax , and the rear row of vehicles optimizes the arrival speed according to the next green light arrival time and stops waiting at necessary moments;

[0038] The constraint of the green light time is expressed as the following Equation (13):

[0039] T gimin ≤ T gi ≤ T gimax (13)

[0040] In order to enable vehicles to continuously pass through intersections without stopping, a progressive coordination control method is adopted to adjust the signal phase difference. The appropriate phase is determined based on the desired vehicle speed and the distance to the intersection, and when the green light comes on again, and so on, enabling vehicles to smoothly pass through the intersections on the entire arterial road. The calculation of the phase difference Q is expressed by Equation (14):

[0041]

[0042] In Equation (14), Q represents the phase difference between two adjacent signals, and d i-1,i represents the distance between intersection i - 1 and intersection i;

[0043] The arterial layer in step 4 is mainly responsible for coordinating the offset of the arterial phases to promote two-way coordination. While optimizing the overall offset of the arterial layer, the queue length at each intersection and the delay time passing through each intersection are considered to find the optimal signal timing plan;

[0044] The objective function of the arterial layer is expressed by Equation (15):

[0045]

[0046] In Equation (11), t i represents the signal offset of the i-th intersection, N stop represents the queue length at each intersection on the arterial road, t delay represents the delay time for a vehicle fleet to pass through a single intersection, α1, α2, and α3 are weight coefficients, and k represents the number of intersections;

[0047] The constraint of the signal cycle is expressed as Equation (16):

[0048] C i = g i + r i (16)

[0049] In Equation (16), C i represents the cycle of the i-th intersection, g i and r i represent the green and red light durations of the i-th intersection, respectively;

[0050] The delay time t delay is expressed by Equation (17):

[0051]

[0052] In Equation (17), λ represents the green ratio, that is, the ratio of the effective green light time to the cycle; x represents the saturation; N stop represents the number of queuing vehicles; η mix represents the correction coefficient for mixed traffic;

[0053] Converting the arterial signal coordination problem into an optimization problem can simultaneously optimize the overall offset, the number of stops, and the delay time, achieve two-way coordination, effectively improve the traffic efficiency of the arterial, and reduce congestion and delays.

[0054] Compared with related technologies, the CAV hierarchical multi-modal arterial coordination control optimization method proposed by the present invention has the following beneficial effects:

[0055] The CAV hierarchical multi-modal arterial coordination control optimization method constructed by the present invention aims to maximize traffic efficiency through hierarchical progressive optimization of the vehicle layer, intersection layer, and arterial layer. At the vehicle layer, a combination of dual-mode formation control and single-vehicle trajectory planning is used to enable CAV vehicles to pass through intersections without stopping; at the intersection layer, signal timing is dynamically adjusted based on real-time traffic flow to eliminate local congestion points; at the arterial layer, green wave coordination is achieved through phase offset optimization, reducing the global number of stops and delay time. The three-layer architecture forms a closed-loop optimization through information intercommunication, ensuring both the smooth driving of vehicles and global coordination at the arterial level, minimizing vehicle stagnation caused by red light signals, and thus improving regional traffic efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a schematic diagram of the simulation scenario of the present invention;

[0057] Figure 2 is a diagram of the CAV hierarchical multi-modal arterial coordination control optimization method in a mixed traffic environment;

[0058] Figure 3 is a schematic diagram of the vehicle trajectory of the leading CAV;

[0059] Figure 4 is a flowchart of the green light time adjustment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] The present invention will be further described below in conjunction with the drawings and embodiments.

[0061] Figure 1 is a schematic diagram of the simulation scenario of the present invention, in which several intersections with different spacings are distributed on the main road. Given the differences in the distances between these intersections, vehicles will adopt corresponding differential control strategies under different spacing conditions. Figure 2 is a diagram of the CAV hierarchical multi-modal arterial coordination control optimization method in a mixed traffic environment of the present invention, and the specific steps are as follows:

[0062] Step 1: The leading CAV communicates with the roadbed facilities, determines the distance to 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 vehicles stopped at red lights on the main line, and improve the overall traffic efficiency. Specifically, it is optimized and controlled at three levels: the vehicle level, the signal level, and the main-line level;

[0063] Step 2: Based on the real-time traffic flow and traffic signals at the vehicle level, the CAV selects an appropriate trajectory control to reduce unnecessary stops or decelerations, enabling the vehicle to pass through the intersection without stopping;

[0064] Step 3: The intersection level adjusts the signal light duration in real time according to the number of vehicles, traffic flow, and estimated arrival time, etc., to ensure smooth traffic and avoid congestion;

[0065] Step 4: The main-line level aggregates the signal phase offset information of each intersection, coordinates the offset of the signal phase, and at the same time considers the queue length at each intersection and the delay time passing through each intersection to find the optimal signal timing plan;

[0066] Step 5: Transmit the signal offset optimization amount of the main-line level to the signal level. The signal level adjusts the signal light duration accordingly, and the vehicle level further optimizes the driving trajectory of the vehicle according to the adjusted signal light duration. Through the coordinated control of the three levels, the aim is to maximize the traffic efficiency of the vehicles on the main line.

[0067] Furthermore, in Step 1, when the distance between intersections is greater than 300 meters, the leading CAV has sufficient time and space for independent trajectory optimization. By communicating with the road infrastructure, the leading vehicle can obtain information such as the condition of the road ahead and traffic flow, so as to plan the optimal speed trajectory to improve driving efficiency and fuel economy. When the distance between intersections is less than 300 meters, the mutual influence between vehicles and the control of signal lights become more important. At this time, the leading CAV needs to cooperate with the signal lights for coordinated control, appropriately adjust the signal light duration to ensure that the vehicle can pass through the intersection smoothly, avoid unnecessary stops, accelerations and decelerations, and reduce traffic congestion and emissions.

[0068] Furthermore, in Step 1, while performing vehicle trajectory control or vehicle trajectory and signal light coordinated control, the system also needs to consider the phase coordination of the main line. In addition to optimizing the overall offset, the phase coordination optimization also needs to consider parameters such as the queue length at each intersection and the delay time passing through each intersection to ensure that the vehicles on the main line can drive smoothly and reduce the delay caused by stopping at red lights;

[0069] To achieve the above goals, the road sections are optimized and scheduled at three levels: the vehicle level, the signal level, and the arterial level. The vehicle level is mainly responsible for the real-time control and adjustment of vehicle trajectories; the signal level is responsible for the optimization and adjustment of signal phases; the arterial level is responsible for the optimization and coordination of the traffic flow of the entire arterial;

[0070] In the vehicle level of step 2, the leading vehicle CAV selects an appropriate trajectory control according to the road conditions, Figure 3 As shown in the schematic diagram of the vehicle trajectory of the leading vehicle CAV, the purpose of trajectory control is that the vehicle fleet led by the leading vehicle CAV just passes through the intersection when the green light comes on, realizing passing through the intersection without stopping. It is specifically divided into the following two situations. When both the time distance and the space distance of the road section are sufficient, the CAV vehicle selects two-mode switching formation control to optimize the acceleration of the leading vehicle CAV, which is expressed by Equation (1):

[0071]

[0072] Furthermore, It is expressed as Equation (2):

[0073]

[0074] In Equation (2), x0(t f ) represents the position of the leading vehicle CAV at time t f , x target is the expected final state position of the leading vehicle, v i (t f ) represents the speed of vehicle i at time t f , v target represents the expected speed of the mixed vehicle fleet, represents the derivative of acceleration, and ω1, ω2, and ω3 respectively represent the error weight coefficients of position, speed, and acceleration;

[0075] Furthermore, F(v(t), a(t)) is expressed as Equation (3):

[0076]

[0077] In Equation (3), f e (v, a) represents the total power consumption of new energy intelligent connected vehicles (CAV) in the mixed vehicle fleet, f f (v, a) represents the total fuel consumption of traditional fuel human-driven vehicles (HDV) in the mixed vehicle fleet, n represents the number of new energy intelligent connected vehicles (CAV) in the mixed vehicle fleet, m represents the number of traditional fuel human-driven vehicles (HDV) in the mixed vehicle fleet, and λ1 and λ2 represent the weight coefficients;

[0078] In Equation (3), the relationship between n and m satisfies the following Equation (4):

[0079] n + m = N (4)

[0080] In Equation (4), N represents the number of mixed vehicle fleets;

[0081] Furthermore, the total power consumption f of new energy intelligent connected vehicles (CAV) within the mixed vehicle fleet in Equation (3) e (v, a) is expressed as the following Equation (5):

[0082]

[0083] In Equation (5), M represents the vehicle mass, g represents the acceleration due to gravity, μ represents the vehicle rolling resistance coefficient, i represents the road gradient, C represents the air resistance coefficient, A represents the vehicle frontal area, σ represents the vehicle mass conversion coefficient, and η represents the vehicle driveline efficiency;

[0084] Furthermore, the total fuel consumption f of traditional fuel - consuming human - driven vehicles (HDV) within the mixed vehicle fleet in Equation (3) f (v, a) is expressed as the following Equations (6) and (7):

[0085]

[0086] In Equations (6) and (7), α represents the constant idling fuel rate, β1 represents the efficiency parameter that relates fuel consumption to the energy provided by the engine, β2 represents the efficiency parameter that relates the fuel consumed during positive acceleration to the product of inertia performance and acceleration, R T represents the total traction force required to drive the vehicle, which is the sum of the resistance force, inertial force, and gradient force, b1 represents the rolling coefficient, b2 represents the engine parameter, b3 represents the aerodynamic resistance parameter, and G represents the percentage gradient of the downhill slope with a negative value;

[0087] 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 leading CAV, that is, the CAV vehicle speed is adjusted by obtaining the signal timing in advance, and the selection of the speed range is shown in Equation (8):

[0088]

[0089] In Equation (8), r i and g i are the start times of the next red light and green light respectively, v min and v max are the minimum and maximum values of the CAV's own vehicle speed respectively. Taking the intersection of the two can obtain the desired fastest arrival time.

[0090] After obtaining the desired vehicle speed in Equation (8), perform second-order vehicle kinematic modeling and then use MPC to track the desired speed, and use the Gauss pseudospectral method for solution.

[0091] The intersection layer in Step 3 is mainly responsible for estimating and predicting traffic states, optimizing traffic signal parameters, and the arrival time of CAVs. The estimation of the maximum number of vehicles that can pass through during the current green light time can be expressed by Equations (9) and (10):

[0092]

[0093] In Equations (9) and (10), d s (v) represents the stable headway between adjacent vehicles, and N represents the maximum number of vehicles that can pass through during the current green light time;

[0094] Furthermore, for the arrival time t f of the CAV at the intersection, it is determined according to the distance x k between the leading CAV and the stop line and the target speed v obj , and can be expressed by Equation (11):

[0095]

[0096] For the calculation of the number of vehicles that can go straight through the intersection i on the road section during the green light time, it is composed of the number of vehicles going straight from the previous intersection i - 1 into intersection i and the number of vehicles turning right at intersection i - 1, minus the number of vehicles changing lanes to the left and right at intersection i, and is specifically expressed by Equation (12):

[0097] N i,s =N i-1,s +N i-1,l -(N i,cr +N i,cl ) (12)

[0098] In Equation (12), N i,s represents the number of vehicles going straight through intersection i, N i-1,s and N i-1,l respectively represent the number of vehicles going straight and turning right at intersection i - 1, and N i,cr and N i,cl respectively represent the number of vehicles changing lanes to the left and right;

[0099] When the number of vehicles entering intersection i is too large and the vehicles on the road section cannot all pass through within the fixed green light time T gi , at this time, the green light duration T gi should be adjusted so that the number of vehicles passing through reaches the maximum within the adjustable time, Figure 3 is the green light time adjustment flow chart;

[0100] Let the maximum adjustable green light time be T gimax When T gi <T gimax the green light duration is extended to T gi and the vehicle platoon can all pass through within time T gi Conversely, the green light duration is extended to T gimax At this time, if vehicle platoon n cannot all pass through within time T gimax row division processing is performed. The front row optimizes the trajectory of the mixed vehicle platoon to pass through the intersection according to T gimax and the rear vehicles optimize their arrival speeds according to the next green light arrival time and stop waiting when necessary;

[0101] The constraint of the green light time is expressed by the following formula (13):

[0102] T gimin ≤T gi ≤T gimax (13)

[0103] In order to enable vehicles to continuously pass through the intersection without stopping, a progressive coordination control method is adopted to adjust the signal phase difference. The appropriate phase is determined according to the expected vehicle speed and the distance from the intersection, and when the green light lights up again, and so on, so that vehicles can smoothly pass through the intersections on the entire main line. The calculation of the phase difference Q is expressed by formula (14):

[0104]

[0105] In formula (14), Q represents the phase difference between two adjacent signals, and d i-1,i represents the distance between intersection i - 1 and intersection i;

[0106] The main line layer in step 4 is mainly responsible for coordinating the offset of the main line phase to promote two-way coordination. While considering the overall offset of the main line layer, the queue length at each intersection and the delay time for passing through each intersection are considered to find the optimal signal timing plan;

[0107] The objective function of the main line layer is expressed by formula (15):

[0108]

[0109] In formula (15), t i represents the signal offset of the i-th intersection, N stop represents the queue length at each intersection on the main line, t delay represents the delay time for a vehicle platoon to pass through a single intersection, α1, α2, and α3 are weight coefficients, and k represents the number of intersections;

[0110] The constraint of the signal cycle is expressed as formula (16):

[0111] C i = g i + r i (16)

[0112] In Equation (16), C i represents the cycle of the i-th intersection, g i and r i respectively represent the green and red light durations of the i-th intersection;

[0113] The delay time t delay is expressed by Equation (17):

[0114]

[0115] In Equation (17), λ represents the green ratio, that is, the ratio of the effective green light time to the cycle; x represents the saturation; N stop represents the number of queuing vehicles; η mix represents the correction coefficient of mixed traffic;

[0116] Transforming the arterial signal coordination problem into an optimization problem can simultaneously optimize the overall offset, the number of stops, and the delay time, achieve two-way coordination, effectively improve the traffic efficiency of the arterial, and reduce congestion and delays;

[0117] Transfer the signal offset optimization amount of the arterial layer to the signal layer. The signal layer appropriately adjusts the signal light duration accordingly, and the vehicle layer further optimizes the driving trajectory of the passing vehicles according to the adjusted signal light duration. Through the collaborative control of the three levels, the efficient passage of vehicles is achieved, the stop frequency caused by red lights is greatly reduced, the traffic jams caused by signal incoordination are eliminated, and the traffic operation efficiency is comprehensively improved.

[0118] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied to other related technical fields, shall be included in the patent protection scope of the present invention by the same token.

Claims

1. A CAV hierarchical multi-modal trunk line coordinated control optimization method in a mixed traffic environment, characterized in that: The following steps are involved: Step 1: The leading CAV communicates with the roadbed facilities, determines 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. The optimization control is specifically divided into three levels: vehicle layer, signal layer and trunk line layer; Step 2: At the vehicle level, according to real-time traffic flow and traffic signals, the CAV selects appropriate trajectory control to reduce unnecessary stops or decelerations, so that the vehicle can pass through the intersection without stopping; Step 3: The intersection layer adjusts the duration of the signal lights in real time according to the number of vehicles, flow, and expected arrival time to ensure smooth traffic and avoid congestion; Step 4: The trunk layer aggregates the signal phase offset information of each intersection, coordinates the signal phase offset, and considers the number of queues at each intersection and the delay time of passing each intersection to find the best signal timing solution; Step 5: The signal offset optimization value of the trunk layer is transmitted to the signal layer, and the signal layer appropriately adjusts the duration of the signal light accordingly. The vehicle layer further optimizes the driving trajectory of the vehicle according to the adjusted duration of the signal light.

2. The CAV hierarchical multi-modal trunk line coordinated control optimization method in a mixed traffic environment as claimed in claim 1, characterized in that: In step 1, the control method is as follows: When the intersection spacing is greater than 300 meters, the lead vehicle CAV independently optimizes the trajectory; when it is less than 300 meters, it is coordinated with the traffic lights for control; at the same time, the main line phase coordination is taken into account, including the green light time and signal phase delay, to ensure smooth vehicle travel; the system is divided into vehicle layer, signal layer and main line layer, which are responsible for trajectory control, traffic light optimization and main line signal phase coordination respectively.

3. The CAV hierarchical multi-modal trunk line coordinated control optimization method in a mixed traffic environment as claimed in claim 1, characterized in that: The vehicle layer control method in step 2 is as follows: When both the time distance and the spatial distance of the road section are sufficient, the CAV selects two-mode switching formation control to optimize the acceleration of the lead CAV: Where t0 represents the time when the leading CAV enters the intersection area, t f Indicates the time when the leading vehicle reaches the stop line of the intersection. represents the terminal cost function, which takes into account the speed error, position error, and acceleration derivative of the vehicle. F(v(t), a(t)) represents the process cost function, which is the total energy consumption of the mixed fleet vehicles, including the CAV electricity consumption and the HDV fuel consumption. When there are too many disturbances on the road and the intersection spacing is short, platoon control cannot be effectively implemented. Single-vehicle control is used to optimize the trajectory of each CAV on the road, and the predictive cruise control algorithm (PCC) is selected to optimize the speed of the lead CAV: In the formula, r i and g i are the start time of the next red light and green light respectively, v min and v max are the minimum and maximum values ​​of the CAV vehicle speed respectively. The intersection of the two can get the expected optimal speed.

4. The CAV hierarchical multi-modal trunk line coordinated control optimization method in a mixed traffic environment as claimed in claim 1, characterized in that: The intersection layer control method in step 3 is as follows: The intersection layer is mainly responsible for estimating and predicting traffic conditions, calculating CAV arrival times, and optimizing traffic signal parameters; estimating the maximum number of vehicles that can pass within the current green light time: Where, d s (v) represents the stable headway between two adjacent vehicles, and N represents the maximum number of vehicles that can pass within the current green light time; The time t when the leading CAV arrives at the intersection f : When there are too many vehicles entering intersection i, the vehicles in the road section will be gi If all the vehicles cannot pass through within the time limit, adjust the green light duration T gi , appropriately extend the green light time; Assume the maximum adjustable green light time is T gimax , when T gi <T gimax When the green light is extended to T gi , the team can gi Otherwise, the green light time is extended to T gimax , at this time, team n cannot gimax If all the candidates pass the test within the specified time, they will be sorted and processed. The front row will be sorted according to T gimax Optimize the trajectory of the mixed fleet through the intersection. The rear vehicles optimize their arrival speed based on the arrival time of the next green light and stop and wait when necessary.

5. The CAV hierarchical multi-modal trunk line coordinated control optimization method in a mixed traffic environment as claimed in claim 1, characterized in that: The trunk layer control method in step 4 is as follows: The trunk layer is responsible for coordinating the offset of the trunk phase and promoting two-way coordination. While optimizing the overall offset of the trunk layer, the number of queues at each intersection and the delay time at each intersection are considered to find the best signal timing solution. Its objective function is as follows: t i represents the signal offset of the i-th intersection, N stop represents the number of queues at each intersection on the trunk line, t delay represents the delay time of the convoy passing through a single intersection, α1, α2 and α3 are weight coefficients, and k represents the number of intersections; Among them, the delay time t delay The calculation of is as follows: λ represents the green-to-signal ratio, i.e. the ratio of the effective green light time to the cycle; x represents the saturation; N stop represents the number of vehicles in the queue; η mix Represents the correction coefficient for mixed traffic; transforms the arterial signal coordination problem into an optimization problem to simultaneously optimize the overall offset, number of stops, and delay time to achieve two-way coordination.

6. The CAV hierarchical multi-modal trunk line coordinated control optimization method in a mixed traffic environment as claimed in claim 1, characterized in that: The three-level coordinated control optimization in step 5 is as follows: The vehicle layer is responsible for regulating the driving trajectory of CAVs, the intersection layer flexibly adjusts the timing of traffic lights according to real-time traffic conditions, and the trunk layer is responsible for coordinating the phase relationship of signals at each intersection to ensure the smooth operation of the entire traffic system; Three levels of coordinated control are used to achieve efficient vehicle traffic, reduce the frequency of stops due to red lights, and eliminate traffic jams caused by inconsistent signals.

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