Vehicle passing order decision and trajectory optimization method and system for unsignalized intersection

Abstract

The application discloses a kind of signal intersection vehicle traffic sequence decision and trajectory optimization method and system.No signal intersection appears in the zone, the information of all vehicles is collected;All vehicles in the zone of no signal intersection appear are sorted according to the order of arrival, form a virtual queue;Determine the optimal vehicle team composition scheme and the conflict-free traffic sequence, plan the time when each vehicle team enters the no signal intersection convergence zone and the allocated occupancy time of no signal intersection convergence zone;The optimal trajectory is generated for the head vehicle of intelligent connected vehicle through nonlinear programming model, and the speed suggestion model is used to give the speed suggestion of the next period for the connected human-driven vehicle head willing to follow the speed suggestion, so that the head vehicle leads the vehicle team to pass through the no signal intersection control area and convergence zone according to planning.The system includes roadside sensing and communication equipment, roadside computing unit, on-board unit.The vehicle traffic efficiency of no signal intersection can be effectively improved by using the method and system.

Description

Technical Field

[0001] This invention relates to the field of traffic engineering, and in particular to a method and system for vehicle traffic sequence decision-making and trajectory optimization at unsignalized intersections. Background Technology

[0002] Intersections are typically considered bottlenecks and accident-prone areas in traffic networks, where converging traffic from different directions creates trajectory conflicts. Efficient traffic signal control strategies, such as adaptive signal control, have demonstrated superior performance in increasing the throughput of traditional human-driven vehicle (HV) traffic at intersections. With advancements in communication technology, connected human-driven vehicles (CVs) can be guided to react appropriately and improve traffic safety and efficiency even without traffic signals, allowing vehicles from non-conflicting directions to occupy intersections simultaneously. Combined with vehicle automation, intelligent connected vehicles (CAVs) can further operate collaboratively at unsignalized intersections, leveraging greater flexibility to alleviate congestion, improve safety, and reduce fuel consumption and emissions.

[0003] Unsignalized intersections in pure CAV environments are considered an effective way to alleviate traffic bottlenecks, and scholars have conducted extensive research in this field. Generally speaking, autonomous intersection management (AIM) includes two main tasks: (1) planning trajectories to separate moving conflicts; and (2) controlling vehicles with certain objectives based on the planned trajectories. There are two common methods for resolving conflicts: rule-based and optimization-based models. Many studies emphasize the micro-control level and determine the passage order through some simplified heuristic rules. Commonly used rules include First-Come, First-Served (FCFS) and Longest Queue First (LQF). Compared with rule-based methods, optimization-based methods perform better in improving system efficiency and reducing individual travel costs. The commonly used method is the Mixed Integer Linear Programming (MILP) model, but it usually aims at system optimization and pays less attention to vehicle fairness.

[0004] In recent years, V2X technology has facilitated vehicle-to-vehicle (CAV) platooning to improve intersection throughput and reduce computational burden. Studies show that CAV platooning can effectively reduce travel time and fuel consumption. Furthermore, platooning vehicles through intersections can reduce the complexity of optimization models. However, the number of vehicles in a platoon should be determined based on the real-time status of vehicles within the intersection planning area, serving as a decision variable for the optimization model.

[0005] Given that the CAV market may not reach full saturation in the short term, exploring hybrid environments for CAVs and HVs will be a more complex but practically significant challenge in the future. In a hybrid environment, the behavior of CAVs can be described using a series of control methods, while the behavior of HVs can be characterized using corresponding vehicle following models. However, optimizing platoon trajectories is difficult to achieve if CAVs are restricted to leading vehicles due to their low penetration rate. In a more easily implemented connected environment, human-driven vehicles (HVs) can achieve conflict-free passage, allowing drivers to enter intersections at the expected time with the help of speed-advising devices.

[0006] Therefore, it is essential to propose a method and system for vehicle traffic order decision-making and trajectory optimization at unsignalized intersections based on vehicle platooning in a hybrid environment of intelligent connected vehicles (CAVs) and connected human-driven vehicles (CVs). Summary of the Invention

[0007] The primary objective of this invention is to provide a method for vehicle traffic order decision-making and trajectory optimization at unsignalized intersections, thereby improving vehicle traffic efficiency at unsignalized intersections and providing a reference for alleviating traffic congestion.

[0008] The technical solution to achieve the first objective of this invention is as follows:

[0009] A method for vehicle traffic order decision-making and trajectory optimization at unsignalized intersections, considering the mixed environment of intelligent connected vehicles and connected human-driven vehicles in a fully connected environment, utilizes vehicle-to-everything (V2X) technology and autonomous driving technology to determine the vehicle platoon composition and traffic order at each approach lane of the unsignalized intersection, including the following steps:

[0010] S1. Collect the position, speed, acceleration, vehicle type, and intent information of all vehicles within the unsignalized intersection's emergence zone, as well as the intent information of connected human-driven vehicles. The unsignalized intersection is divided into an emergence zone, a control zone, and a merging zone. First, vehicle platooning and traffic order optimization are performed within the emergence zone. Then, the trajectory optimization or speed suggestion for the lead vehicle in the platoon is implemented within the control zone. Finally, conflict-free passage of the platoon is achieved in the merging zone. The intent information of the connected human-driven vehicles includes willingness to comply with speed suggestions and unwillingness to comply with speed suggestions.

[0011] S2. Sort all vehicles in the unsignalized intersection area according to their arrival order to form a virtual queue;

[0012] S3. Based on the vehicle platooning optimization model that takes into account fairness and the intention of connected human driving vehicles, or combined with the first-come-first-served model, determine the optimal platoon composition scheme and conflict-free passage order, thereby planning the time when each platoon enters the unsignalized intersection merging area and the allocated occupancy time of the unsignalized intersection merging area.

[0013] S4. Based on the current vehicle status, an optimal trajectory is generated for the intelligent connected vehicle leader using a nonlinear programming model. The intelligent connected vehicle leader is controlled to lead the convoy through the unsignalized intersection control area at the planned times when each convoy enters the unsignalized intersection merging area, and through the unsignalized intersection merging area according to the allocated occupancy time. A speed suggestion model is used to provide speed suggestions for the next time period to the connected human-driven vehicle leader that is willing to follow the speed suggestions. The connected human-driven vehicle leader that is willing to follow the speed suggestions leads the convoy through the unsignalized intersection control area at the planned times when each convoy enters the unsignalized intersection merging area, and through the unsignalized intersection merging area according to the allocated occupancy time. The intelligent connected vehicle leader refers to the first vehicle in the convoy being an intelligent connected vehicle, and the connected human-driven vehicle leader refers to the first vehicle in the convoy being a connected human-driven vehicle.

[0014] Furthermore, the decision variable of the unsignaled intersection traffic order optimization model based on vehicle platooning and considering fairness and the intentions of connected human drivers is the number of vehicles in the N initial sets into which the virtual queue is divided. Based on the decision variable and combined with the intention information of connected human drivers, a platoon composition scheme is determined (this platoon composition scheme is not necessarily the optimal platoon composition scheme, but a scheme determined by the decision variable and the intention information of connected human drivers, and is related to the solution space and vehicle conditions), specifically including:

[0015] Step a: Divide the virtual queue into N sets, and call these N sets the initial set;

[0016] Step b: Vehicles located in the same initial assembly and in the same lane form a quasi-platform;

[0017] Step c: Determine the type of the lead vehicle in each quasi-team; if the lead vehicle in a quasi-team is an intelligent connected vehicle, then the quasi-team forms a platoon; if the lead vehicle in a quasi-team is a connected human-driven vehicle and is willing to comply with the speed recommendation, then the quasi-team forms a platoon; if the lead vehicle in a quasi-team is a connected human-driven vehicle and is unwilling to comply with the speed recommendation, then the lead vehicle in the quasi-team is treated as a separate vehicle and forms a platoon, while the remaining vehicles in the quasi-team form another quasi-team. Repeat step c until the quasi-teams form a platoon.

[0018] Step d: Determine the fleet composition scheme based on the formed fleet, and in the determined fleet composition scheme, the kth fleet on each lane belongs to a set, called the kth final set;

[0019] (The initial set is directly determined by the decision variables, while the final set is jointly determined by the decision variables and the intention information of connected human drivers.)

[0020] The objective function of the unsignalized intersection traffic order optimization model based on vehicle platooning and considering fairness and the intentions of connected human drivers is:

[0021]

[0022] Among them, t a (i,j,s) represents the time when the j-th vehicle in the s-th final set of the i-th lane enters the unsignalized intersection area; t e (i,j,s) represents the planned entry time of the j-th vehicle in the s-th final set on the i-th lane into the unsignalized intersection merging area; I represents the number of vehicles in the s-th final set on the i-th lane; 总 S represents the total number of lanes; 总 Indicates the total number of the final set;

[0023] The constraints of the unsignalized intersection traffic order optimization model based on vehicle platooning and considering fairness and the intentions of connected human drivers include:

[0024] 1) Fundamental constraints of vehicle kinematics, namely, the vehicle's position, velocity, and acceleration satisfy second-order dynamic constraints:

[0025]

[0026]

[0027] Where p(t) represents the position of the vehicle at time t, v(t) represents the velocity of the vehicle at time t, and u(t) represents the acceleration of the vehicle at time t;

[0028] 2) Earliest Arrival Time Constraint: The planned entry time for a vehicle into the merging area of ​​an unsignalized intersection is restricted by the vehicle's current state and cannot be earlier than the earliest arrival time, i.e.:

[0029]

[0030] Where t represents the current timestamp, t e Ψ represents the planned entry time of a vehicle into the merging zone of an unsignalized intersection, c represents the vehicle appearing in the unsignalized intersection zone, and Ψ represents the vehicle. sThis represents the final set in the determined convoy composition scheme; the earliest arrival time T. m satisfy:

[0031]

[0032] Among them, v max Indicates the maximum speed limit of the vehicle, u max The maximum acceleration limit of the vehicle is represented by d(t), and the distance from the vehicle to the unsignalized intersection merging zone at time t is represented by d(t).

[0033] 3) Fleet Occupancy Time Constraint: The time a fleet occupies the merging area of ​​an unsignalized intersection is related to the number of vehicles in the fleet. Taking into account vehicle start-up reaction time, the maximum fleet occupancy time is calculated, and the fleet occupancy time is then calculated using the maximum fleet occupancy time, i.e.:

[0034] T o =T s +(n-1)T r (4.1)

[0035] Where n represents the number of vehicles in the convoy, T r Indicates vehicle start-up reaction time; T s This indicates the time when the last vehicle in the convoy starts entering the merging zone of an unsignalized intersection, satisfying the following:

[0036]

[0037] Among them, l s This indicates the distance from the parking position of the last car in the convoy to the merging area of ​​the unsignalized intersection. s =L+H+(n-1)(H+s0), where L represents the length of the vehicle's lane within the merging zone of the unsignalized intersection, H represents the vehicle length, and s0 represents the minimum vehicle spacing.

[0038] 4) No-conflict constraints for platoons: Considering the conflict relationships between different lane trajectories, non-conflict trajectories are assigned to platoons. Compatible platoons can simultaneously occupy the merging area of ​​an unsignalized intersection. Therefore, the rule for determining the platoon passage order is that each platoon enters the merging area of ​​the unsignalized intersection later than a higher-priority platoon that is incompatible with it, i.e.:

[0039]

[0040] in, This represents the planned entry time of the lead vehicle in the s-th terminal set of the convoy on the i-th lane into the unsignalized intersection merging area. This represents the planned entry time of the lead vehicle in the s′-th terminal set of the platoon on the i′-th lane into the unsignalized intersection merging area. Let ω represent the time the lead vehicle in the s′-th terminal set of lane i′ occupies the merging zone of an unsignalized intersection. Let I represent the lane number set and S represent the terminal set number set. If s > s′ or s = s′, and lane i′ has priority within the same terminal set, then the binary variable ω... ii′ If it is 1, then the binary variable ω is 1; otherwise, it is 1. ii′ The value is 0; the binary variable λ ii′ This is used to identify whether two vehicle convoys are compatible (i.e., whether their trajectories conflict within the merging zone of an unsignalized intersection). If the two vehicle convoys are compatible, then λ... ii′ =0, otherwise λ ii′ =1.

[0041] Furthermore, for convoys of connected human-driven vehicles that are unwilling to follow speed recommendations, the order of passage of vehicles in each lane of the same final set is determined according to the first-come-first-served model and the rule of the first vehicle in the convoy.

[0042] Furthermore, the nonlinear programming model generates the optimal trajectory for the intelligent connected vehicle's helm; the nonlinear programming model is as follows:

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] v1(t s )=v1, (6.6)

[0049] p1(t s ) = 0, (6.7)

[0050]

[0051] p1(t e )=d1,(6.9)

[0052] Where t represents the current timestamp, t e t represents the planned entry time of a vehicle into the merging zone of an unsignalized intersection. s This indicates the time of vehicle trajectory planning, where d1 represents the distance from the current position of the intelligent connected vehicle's lead vehicle to the unsignalized intersection merging area. Let v1 represent the expected speed of the intelligent connected vehicle's leading vehicle, p1(t) represent the speed of the intelligent connected vehicle's leading vehicle, v1(t) represent the position of the intelligent connected vehicle's leading vehicle at time t, and u1(t) represent the acceleration of the intelligent connected vehicle's leading vehicle at time t. This indicates the minimum speed limit for the lead vehicle in a smart connected vehicle. This indicates the maximum speed limit of the lead vehicle in a smart connected vehicle. This indicates the minimum acceleration limit for the front-end vehicle in a smart connected vehicle. Let represent the maximum acceleration limit of the intelligent connected vehicle's lead car; Equation (6.1) represents minimizing t. s Time to t e The fuel consumption of the intelligent connected vehicle's lead vehicle within a given time period; Equation (6.2) represents the speed limit of the intelligent connected vehicle's lead vehicle; Equation (6.3) represents the acceleration limit of the intelligent connected vehicle's lead vehicle; Equation (6.4) represents the position update rule of the intelligent connected vehicle's lead vehicle; Equation (6.5) represents the speed update rule of the intelligent connected vehicle's lead vehicle; Equation (6.6) represents t s The speed constraint of the intelligent connected vehicle's lead vehicle at any given time is the initial constraint; Equation (6.7) represents t s The position constraint of the intelligent connected vehicle's lead vehicle at any given time is the initial constraint; Equation (6.8) represents t e The speed constraint of the intelligent connected vehicle head unit at any given time is a terminal constraint; Equation (6.9) represents t e The position constraints of the intelligent connected vehicle's lead vehicle at all times are terminal constraints.

[0053] Furthermore, the speed suggestion model provides speed suggestions for the next time period to the lead vehicle of the connected human-driven vehicle that is willing to follow the speed suggestion, including acceleration, constant speed, and deceleration suggestions; the speed suggestion model is as follows:

[0054] when At that time, the speed suggestion model provides acceleration suggestions to the lead vehicle of the connected human-driven vehicle that is willing to follow the speed suggestion;

[0055] when At that time, the speed recommendation model provides a constant speed recommendation to the lead vehicle of the connected human-driven vehicle that is willing to follow the speed recommendation;

[0056] when At that time, the speed suggestion model provides deceleration suggestions to the lead vehicle of the connected human-driven vehicle that is willing to follow the speed suggestion;

[0057] Where t represents the current timestamp, t ev2(t) represents the time when a vehicle is planned to enter the unsignalized intersection merging zone, v2(t) represents the speed of the lead vehicle of the connected human-driven vehicle that is willing to follow the speed recommendation at time t, d2(t) represents the distance from the current position of the lead vehicle of the connected human-driven vehicle that is willing to follow the speed recommendation to the unsignalized intersection merging zone, and a(-) represents the maximum deceleration of the lead vehicle of the connected human-driven vehicle that is willing to follow the speed recommendation; η1 and η2 are parameters that adjust the degree of conservatism of the controllable speed recommendation.

[0058] Furthermore, the rules for maintaining the distance between the non-leader intelligent connected vehicles in the convoy and the vehicle in front are as follows:

[0059] e = g(t) - H0 - g * (7.1)

[0060]

[0061] Where t represents the current timestamp, Δt a Let g(t) represent the time interval, g(t) represent the gap between the non-leading connected vehicle in the platoon and the vehicle in front of it at time t, and H0 represent the vehicle length of the non-leading connected vehicle in the platoon. * Let 'e' represent the desired gap and 'e' represent the tracking error. θ represents the first derivative of the tracking error with respect to time. p =0.45 and θ d =0.125 is the parameter for adjusting the gap, v3(t) represents the speed of the intelligent connected vehicle in the convoy that is not the lead vehicle at time t, v3(t+Δt) a ) represents t+Δt a The speed of the intelligent connected vehicles that are not the lead vehicle in the fleet.

[0062] Furthermore, the behavior of non-lead connected human-driven vehicles in the convoy and the lead connected human-driven vehicle willing to follow speed recommendations is modeled using a Gipps car-following model that considers randomness, which is represented as:

[0063] u0(t)=a(t)+bq(t)r(t), (8.1)

[0064] Where a(t) represents the acceleration generated at time t according to the Gipps car-following model; b represents the standard deviation under different accelerations; q(t) is a uniformly distributed random number between 0 and 1; r(t) is 1 or -1 with a probability of 50% respectively; and u0(t) represents the actual acceleration.

[0065] The second objective of this invention is to provide a vehicle traffic sequence decision and trajectory optimization system for unsignalized intersections, thereby improving vehicle traffic efficiency at unsignalized intersections.

[0066] The technical solution to achieve the second objective of this invention is as follows:

[0067] A vehicle traffic order decision and trajectory optimization system for unsignalized intersections, wherein the system operates using any of the aforementioned methods for vehicle traffic order decision and trajectory optimization at unsignalized intersections; the system includes roadside sensing and communication equipment, a roadside computing unit, and an on-board unit.

[0068] The roadside sensing and communication equipment collects the position, speed, acceleration, vehicle type, and networked human driving intention information of all vehicles in the unsignalized intersection area, and transmits the information of all vehicles in the unsignalized intersection area to the roadside computing unit.

[0069] The roadside computing unit, acting as the management center of the unsignalized intersection, sorts all vehicles within the unsignalized intersection area according to their arrival order based on the received vehicle information, forming a virtual queue. It then determines the optimal platoon composition and conflict-free passage sequence, thereby planning the entry time of each platoon into the unsignalized intersection merging area and the allocated occupancy time of the merging area, and transmits this information to the onboard unit. The roadside computing unit includes an unsignalized intersection passage sequence optimization model based on vehicle platooning and considering fairness and the intentions of connected human drivers, as well as a first-come, first-served model.

[0070] The on-board unit includes a nonlinear programming model and a speed suggestion model. The nonlinear programming model generates the optimal trajectory for the intelligent connected vehicle leader and controls the intelligent connected vehicle leader so that it leads the convoy through the unsignalized intersection control area at the planned times when each convoy enters the unsignalized intersection merging area, and through the unsignalized intersection merging area according to the allocated occupancy time. The speed suggestion model provides speed suggestions for the next time period to the connected human-driven vehicles leader that are willing to comply with the speed suggestions, so that the connected human-driven vehicles leader that are willing to comply with the speed suggestions lead the convoy through the unsignalized intersection control area at the planned times when each convoy enters the unsignalized intersection merging area, and through the unsignalized intersection merging area according to the allocated occupancy time.

[0071] Compared with the prior art, the present invention has the following advantages:

[0072] I. This invention organizes vehicles to pass through unsignalized intersections in platoon form. Combining simple rules and optimization models, it makes decisions on the order of vehicle passage based on the principle of fairness in vehicle passage. It proposes a more adaptable and computationally efficient autonomous management method and system for unsignalized intersections, which has practical significance.

[0073] Second, this invention considers that the mixed environment of intelligent connected vehicles and connected human-driven vehicles is easier to implement and more practical than the environment of purely intelligent connected vehicles; this invention considers the intentions of the drivers of connected human-driven vehicles, allows connected human-driven vehicles to act as the lead vehicle in a convoy and provide speed suggestions, while designing the optimal trajectory for the intelligent connected vehicle lead vehicle, which can effectively simulate the characteristics of the mixed environment.

[0074] Third, this invention optimizes vehicle platooning schemes and platooning order at unsignalized intersections simultaneously, taking into account both fairness of vehicle passage and the possibility that connected human-driven vehicles may not comply with speed recommendations. Based on the optimization results, it provides the optimal trajectory for the intelligent connected vehicle leader and speed recommendations for connected human-driven vehicles that are willing to comply with speed recommendations. This controls or guides vehicles to pass through unsignalized intersections with higher traffic efficiency, thereby reducing average vehicle delay, controlling maximum delay, and reducing fuel consumption.

[0075] The present invention will be further described in detail below with reference to specific embodiments and accompanying drawings, but this does not imply any limitation on the scope of protection of the present invention. Attached Figure Description

[0076] Figure 1 This is a schematic diagram of the framework of the method and system for vehicle traffic order decision and trajectory optimization at an unsignalized intersection according to an embodiment of the present invention.

[0077] Figure 2 This is a schematic diagram of a signalless intersection according to an embodiment of the present invention.

[0078] Figure 3 This is a flowchart illustrating the process of determining the fleet composition scheme in an embodiment of the present invention.

[0079] Figure 4 This chart compares the average vehicle delays under different traffic demands with the method provided in this embodiment of the invention and other methods, assuming a 100% CAV penetration rate.

[0080] Figure 5 This is a comparison chart showing the maximum vehicle delay results under different traffic demands, with a CAV penetration rate of 100%, using the method provided in the embodiments of the present invention and using other methods.

[0081] Figure 6 This is a comparison chart showing the average vehicle delay results under different CAV penetration rates and a traffic demand of 1200veh / h, using the method provided in the embodiments of the present invention and other methods.

[0082] Figure 7 This is a comparison chart showing the maximum vehicle delay results under different CAV penetration rates and a traffic demand of 1200veh / h, using the method provided in the embodiments of the present invention and other methods.

[0083] Figure 8 The diagram illustrates vehicle fuel consumption indicators using the method provided in the embodiments of the present invention and other methods. Detailed Implementation

[0084] Example

[0085] This example presents a method for vehicle traffic order decision-making and trajectory optimization at unsignalized intersections. The method considers a hybrid environment of intelligent connected vehicles and connected human-driven vehicles in a fully connected environment. Utilizing vehicle-to-everything (V2X) technology and autonomous driving technology, it determines the vehicle platoon composition and traffic order at each approach lane of the unsignalized intersection, including the following steps:

[0086] S1. Collect the position, speed, acceleration, vehicle type, and intent information of all vehicles within the unsignalized intersection's emergence zone, as well as the intent information of connected human-driven vehicles. The unsignalized intersection is divided into an emergence zone, a control zone, and a merging zone. First, vehicle platooning and traffic order optimization are performed within the emergence zone. Then, the trajectory optimization or speed suggestion for the lead vehicle in the platoon is implemented within the control zone. Finally, conflict-free passage of the platoon is achieved in the merging zone. The intent information of the connected human-driven vehicles includes willingness to comply with speed suggestions and unwillingness to comply with speed suggestions.

[0087] S2. Sort all vehicles in the unsignalized intersection area according to their arrival order to form a virtual queue;

[0088] S3. Based on the vehicle platooning optimization model that takes into account fairness and the intention of connected human driving vehicles, or combined with the first-come-first-served model, determine the optimal platoon composition scheme and conflict-free passage order, thereby planning the time when each platoon enters the unsignalized intersection merging area and the allocated occupancy time of the unsignalized intersection merging area.

[0089] S4. Based on the current vehicle status, an optimal trajectory is generated for the intelligent connected vehicle leader using a nonlinear programming model. The intelligent connected vehicle leader is controlled to lead the convoy through the unsignalized intersection control area at the planned times when each convoy enters the unsignalized intersection merging area, and through the unsignalized intersection merging area according to the allocated occupancy time. A speed suggestion model is used to provide speed suggestions for the next time period to the connected human-driven vehicle leader that is willing to follow the speed suggestions. The connected human-driven vehicle leader that is willing to follow the speed suggestions leads the convoy through the unsignalized intersection control area at the planned times when each convoy enters the unsignalized intersection merging area, and through the unsignalized intersection merging area according to the allocated occupancy time. The intelligent connected vehicle leader refers to the first vehicle in the convoy being an intelligent connected vehicle, and the connected human-driven vehicle leader refers to the first vehicle in the convoy being a connected human-driven vehicle.

[0090] In this example, the decision variable for the unsignaled intersection traffic order optimization model based on vehicle platooning and considering fairness and the intentions of connected human drivers is the number of vehicles in the N initial sets into which the virtual queue is divided. Based on these decision variables and combined with information on the intentions of connected human drivers, the platoon composition scheme is determined, specifically including:

[0091] Step a: Divide the virtual queue into N sets, and call these N sets the initial set;

[0092] Step b: Vehicles located in the same initial assembly and in the same lane form a quasi-platform;

[0093] Step c: Determine the type of the lead vehicle in each quasi-team; if the lead vehicle in a quasi-team is an intelligent connected vehicle, then the quasi-team forms a platoon; if the lead vehicle in a quasi-team is a connected human-driven vehicle and is willing to comply with the speed recommendation, then the quasi-team forms a platoon; if the lead vehicle in a quasi-team is a connected human-driven vehicle and is unwilling to comply with the speed recommendation, then the lead vehicle in the quasi-team is treated as a separate vehicle and forms a platoon, while the remaining vehicles in the quasi-team form another quasi-team. Repeat step c until the quasi-teams form a platoon.

[0094] Step d: Determine the fleet composition scheme based on the formed fleet, and in the determined fleet composition scheme, the kth fleet on each lane belongs to a set, called the kth final set;

[0095] The objective function of the unsignalized intersection traffic order optimization model based on vehicle platooning and considering fairness and the intentions of connected human drivers is:

[0096]

[0097] Among them, t a(i,j,s) represents the time when the j-th vehicle in the s-th final set of the i-th lane enters the unsignalized intersection area; t e (i,j,s) represents the planned entry time of the j-th vehicle in the s-th final set on the i-th lane into the unsignalized intersection merging area; I represents the number of vehicles in the s-th final set on the i-th lane; 总 S represents the total number of lanes; 总 Indicates the total number of the final set;

[0098] The constraints of the unsignalized intersection traffic order optimization model based on vehicle platooning and considering fairness and the intentions of connected human drivers include:

[0099] 1) Fundamental constraints of vehicle kinematics, namely, the vehicle's position, velocity, and acceleration satisfy second-order dynamic constraints:

[0100]

[0101]

[0102] Where p(t) represents the position of the vehicle at time t, v(t) represents the velocity of the vehicle at time t, and u(t) represents the acceleration of the vehicle at time t;

[0103] 2) Earliest Arrival Time Constraint: The planned entry time for a vehicle into the merging area of ​​an unsignalized intersection is restricted by the vehicle's current state and cannot be earlier than the earliest arrival time, i.e.:

[0104]

[0105] Where t represents the current timestamp, t e Ψ represents the planned entry time of a vehicle into the merging zone of an unsignalized intersection, c represents the vehicle appearing in the unsignalized intersection zone, and Ψ represents the vehicle. s Represents the final set in the determined convoy composition scheme; earliest arrival time T m satisfy:

[0106]

[0107] Among them, v max Indicates the maximum speed limit of the vehicle, u max The maximum acceleration limit of the vehicle is represented by d(t), and the distance from the vehicle to the merging zone of the unsignalized intersection at time t is represented by d(t).

[0108] 3) Fleet Occupancy Time Constraint: The time a fleet occupies the merging area of ​​an unsignalized intersection is related to the number of vehicles in the fleet. Taking into account vehicle start-up reaction time, the maximum fleet occupancy time is calculated, and the fleet occupancy time is then calculated using the maximum fleet occupancy time, i.e.:

[0109] T o =T s +(n-1)T r (4.1)

[0110] Where n represents the number of vehicles in the convoy, T r Indicates vehicle start-up reaction time; T s This indicates the time when the last vehicle in the convoy starts entering the merging zone of an unsignalized intersection, satisfying the following:

[0111]

[0112] Among them, l s This indicates the distance from the parking position of the last car in the convoy to the merging area of ​​the unsignalized intersection. s =L+H+(n-1)(H+s0), where L represents the length of the vehicle's lane within the merging zone of the unsignalized intersection, H represents the vehicle length, and s0 represents the minimum vehicle spacing.

[0113] 4) No-conflict constraints for platoons: Considering the conflict relationships between different lane trajectories, non-conflict trajectories are assigned to platoons. Compatible platoons can simultaneously occupy the merging area of ​​an unsignalized intersection. Therefore, the rule for determining the platoon passage order is that each platoon enters the merging area of ​​the unsignalized intersection later than a higher-priority platoon that is incompatible with it, i.e.:

[0114]

[0115] in, This represents the planned entry time of the lead vehicle in the s-th terminal set of the convoy on the i-th lane into the unsignalized intersection merging area. This represents the planned entry time of the lead vehicle in the s′-th terminal set of the platoon on the i′-th lane into the unsignalized intersection merging area. Let ω represent the time the lead vehicle in the s′-th terminal set of lane i′ occupies the merging zone of an unsignalized intersection. Let I represent the lane number set and S represent the terminal set number set. If s > s′ or s = s′, and lane i′ has priority within the same terminal set, then the binary variable ω... ii′ It is 1, otherwise the binary variable ω ii′ The value is 0; the binary variable λ ii′ This is used to identify whether two teams are compatible. If the two teams are compatible, then λ ii′ =0, otherwise λ ii′ =1.

[0116] In this example, the convoy formed by connected human-driven vehicles that are unwilling to follow speed recommendations, and the convoy located in different lanes of the same final set, determines the vehicle passage order according to the first-come-first-served model and the rule of the convoy leader arriving first.

[0117] The nonlinear programming model described in this example is used to generate the optimal trajectory for the intelligent connected vehicle's front-end vehicle; the nonlinear programming model is as follows:

[0118]

[0119]

[0120]

[0121]

[0122]

[0123] v1(t s )=v1, (6.6)

[0124] p1(t s ) = 0, (6.7)

[0125]

[0126] p1(t e )=d1,(6.9)

[0127] Where t represents the current timestamp, t e t represents the planned entry time of a vehicle into the merging zone of an unsignalized intersection. s This indicates the time of vehicle trajectory planning, where d1 represents the distance from the current position of the intelligent connected vehicle's lead vehicle to the unsignalized intersection merging area. Let v1 represent the expected speed of the intelligent connected vehicle's leading vehicle, p1(t) represent the speed of the intelligent connected vehicle's leading vehicle, v1(t) represent the position of the intelligent connected vehicle's leading vehicle at time t, and u1(t) represent the acceleration of the intelligent connected vehicle's leading vehicle at time t. This indicates the minimum speed limit for the lead vehicle in a smart connected vehicle. This indicates the maximum speed limit of the lead vehicle in a smart connected vehicle. This indicates the minimum acceleration limit for the front-end vehicle in a smart connected vehicle. Let represent the maximum acceleration limit of the intelligent connected vehicle's lead car; Equation (6.1) represents minimizing t. s Time to t e The fuel consumption of the intelligent connected vehicle's lead vehicle within a given time period; Equation (6.2) represents the speed limit of the intelligent connected vehicle's lead vehicle; Equation (6.3) represents the acceleration limit of the intelligent connected vehicle's lead vehicle; Equation (6.4) represents the position update rule of the intelligent connected vehicle's lead vehicle; Equation (6.5) represents the speed update rule of the intelligent connected vehicle's lead vehicle; Equation (6.6) represents t sThe speed constraint of the intelligent connected vehicle's lead vehicle at any given time is the initial constraint; Equation (6.7) represents t s The position constraint of the intelligent connected vehicle's lead vehicle at any given time is the initial constraint; Equation (6.8) represents t e The speed constraint of the intelligent connected vehicle head unit at any given time is a terminal constraint; Equation (6.9) represents t e The position constraints of the intelligent connected vehicle's lead vehicle at all times are terminal constraints.

[0128] The speed suggestion model described in this example provides speed suggestions for the next time period to the lead vehicle of a connected human-driven vehicle that is willing to follow speed recommendations, including acceleration, constant speed, and deceleration suggestions; the speed suggestion model is as follows:

[0129] when At that time, the speed suggestion model provides acceleration suggestions to the lead vehicle of the connected human-driven vehicle that is willing to follow the speed suggestion;

[0130] when At that time, the speed recommendation model provides a constant speed recommendation to the lead vehicle of the connected human-driven vehicle that is willing to follow the speed recommendation;

[0131] when At that time, the speed suggestion model provides deceleration suggestions to the lead vehicle of the connected human-driven vehicle that is willing to follow the speed suggestion;

[0132] Where t represents the current timestamp, t e v2(t) represents the time when a vehicle is planned to enter the unsignalized intersection merging zone, v2(t) represents the speed of the lead vehicle of the connected human-driven vehicle that is willing to follow the speed recommendation at time t, d2(t) represents the distance from the current position of the lead vehicle of the connected human-driven vehicle that is willing to follow the speed recommendation to the unsignalized intersection merging zone, and a(-) represents the maximum deceleration of the lead vehicle of the connected human-driven vehicle that is willing to follow the speed recommendation; η1 and η2 are parameters that adjust the degree of conservatism of the controllable speed recommendation.

[0133] In this example, the rules for maintaining the distance between the non-lead connected vehicle and the vehicle in front of it in the convoy are as follows:

[0134] e = g(t) - H0 - g * (7.1)

[0135]

[0136] Where t represents the current timestamp, Δt a Let g(t) represent the time interval, g(t) represent the gap between the non-leading connected vehicle in the platoon and the vehicle in front of it at time t, and H0 represent the vehicle length of the non-leading connected vehicle in the platoon. * Let 'e' represent the desired gap and 'e' represent the tracking error. θ represents the first derivative of the tracking error with respect to time.p =0.45 and θ d =0.125 is the parameter for adjusting the gap, v3(t) represents the speed of the intelligent connected vehicle in the convoy that is not the lead vehicle at time t, v3(t+Δt) a ) represents t+Δt a The speed of the intelligent connected vehicles that are not the lead vehicle in the fleet.

[0137] In this example, the behavior of the non-lead connected human-driven vehicles and the lead connected human-driven vehicle willing to follow the speed recommendation in the convoy is modeled using the Gipps car-following model, which considers randomness. This randomness is represented as:

[0138] u o (t)=a(t)+bq(t)r(t), (8.1)

[0139] Where a(t) represents the acceleration generated at time t according to the Gipps car-following model; b represents the standard deviation under different accelerations; q(t) is a uniformly distributed random number between 0 and 1; r(t) is 1 or -1 with a probability of 50% respectively; and u0(t) represents the actual acceleration.

[0140] This example also presents a vehicle traffic order decision and trajectory optimization system for unsignalized intersections. The system operates using the vehicle traffic order decision and trajectory optimization method for unsignalized intersections presented in this example. The system includes roadside sensing and communication equipment, a roadside computing unit, and an on-board unit.

[0141] The roadside sensing and communication equipment collects the position, speed, acceleration, vehicle type, and networked human driving intention information of all vehicles in the unsignalized intersection area, and transmits the information of all vehicles in the unsignalized intersection area to the roadside computing unit.

[0142] The roadside computing unit, acting as the management center of the unsignalized intersection, sorts all vehicles within the unsignalized intersection area according to their arrival order based on the received vehicle information, forming a virtual queue. It then determines the optimal platoon composition and conflict-free passage sequence, thereby planning the entry time of each platoon into the unsignalized intersection merging area and the allocated occupancy time of the merging area, and transmits this information to the onboard unit. The roadside computing unit includes an unsignalized intersection passage sequence optimization model based on vehicle platooning and considering fairness and the intentions of connected human drivers, as well as a first-come, first-served model.

[0143] The on-board unit includes a nonlinear programming model and a speed suggestion model. The nonlinear programming model generates the optimal trajectory for the intelligent connected vehicle leader and controls the intelligent connected vehicle leader so that it leads the convoy through the unsignalized intersection control area at the planned times when each convoy enters the unsignalized intersection merging area, and through the unsignalized intersection merging area according to the allocated occupancy time. The speed suggestion model provides speed suggestions for the next time period to the connected human-driven vehicles leader that are willing to comply with the speed suggestions, so that the connected human-driven vehicles leader that are willing to comply with the speed suggestions lead the convoy through the unsignalized intersection control area at the planned times when each convoy enters the unsignalized intersection merging area, and through the unsignalized intersection merging area according to the allocated occupancy time.

[0144] Numerical simulation experiments based on Matlab are now conducted to verify the effectiveness of the proposed method (denoted as AIM_O) in this example, specifically including:

[0145] Figure 1 This is a schematic diagram illustrating the framework of the vehicle traffic sequence decision-making and trajectory optimization method and system for this example of an unsignalized intersection.

[0146] Figure 2 This is a schematic diagram of an unsignalized intersection in this example. The unsignalized intersection is divided into three zones: the Staging Zone (SZ), the Control Zone (CZ), and the Merging Zone (MZ), with corresponding lengths of 160m, 40m, and 35m respectively. Vehicles within these three zones belong to sets ψ. s (final set), ψ C ψ M An unsignaled intersection comprises 8 lanes, numbered i∈I={1,2,…,8}, including straight-ahead and left-turn lanes. Each lane has a defined trajectory length L in the merging area of ​​the unsignaled intersection. i The mixed environment considered for unsignalized intersections includes connected vehicles (CAVs) and connected human-vehicle (CV) systems. The arrival areas of vehicles in each lane at the unsignalized intersection follow a Poisson distribution, and lane-changing behavior is not considered.

[0147] Within the unsignalized intersection zone (SZ): Roadside sensing and communication equipment collects the position, speed, acceleration, vehicle type (CV represented by α=0, CAV represented by α=1), and connected human driving intention information (β=0 represents CVs unwilling to comply with speed recommendations, β=1 represents CVs willing to comply with speed recommendations) of all vehicles within the SZ, and transmits this vehicle information to the roadside computing unit. Upon receiving the vehicle information, the roadside computing unit sorts all vehicles within the SZ according to their arrival order, forming a virtual queue. Based on the decision variables of an unsignalized intersection traffic order optimization model (hereinafter referred to as the optimization model) that considers vehicle platooning, fairness, and connected human driving intentions, and in conjunction with the connected human driving intention information, it determines the platoon composition scheme. The optimal platoon composition scheme and conflict-free traffic order based on the optimization model are solved, thereby planning the entry time of each platoon into the unsignalized intersection merging zone and the allocated occupancy time of the unsignalized intersection merging zone, and transmitting this information to the onboard unit.

[0148] Within the unsignalized intersection control zone (CZ): The onboard unit receives information transmitted from the roadside computing unit, generates the optimal trajectory for the intelligent connected vehicle leader using a nonlinear programming model, and controls the intelligent connected vehicle leader to lead the convoy through the unsignalized intersection control zone at the planned times for each convoy to enter the unsignalized intersection merging zone; and provides speed recommendations for the next time period to connected human-driven vehicles that are willing to comply with speed recommendations using a speed recommendation model, so that connected human-driven vehicles that are willing to comply with speed recommendations lead the convoy through the unsignalized intersection control zone at the planned times for each convoy to enter the unsignalized intersection merging zone.

[0149] Within the unsignalized intersection merging zone (MZ): Each convoy passes through the unsignalized intersection merging zone according to its assigned occupancy time.

[0150] Figure 3 This is a flowchart illustrating the process of determining the fleet composition scheme in this example. It assumes only... Figure 2 There are vehicles in lanes 1 and 3 as shown. Figure 3 The box in the middle represents and Figure 2 The vehicles corresponding to lane 1, Figure 3 The circle in the middle represents the circle with Figure 2For vehicles in lane 3, the numbers within the boxes and circles indicate the order in which they arrived at the unsignalized intersection area (e.g., 1 indicates the first vehicle to arrive). Assume vehicle 1 is the lead vehicle of the intelligent connected vehicle, vehicle 3 is the lead vehicle of the connected human driver willing to follow the speed recommendation, vehicle 7 is the connected human driver unwilling to follow the speed recommendation, and vehicle 8 is the connected human driver willing to follow the speed recommendation. First, consider dividing the vehicles within the unsignalized intersection area into two initial sets. Indicates that the decision variables are directly determined by the decision variables. The initial vehicle set is determined, and then dynamically adjusted according to the vehicle's intent to ultimately form a stable convoy. The final vehicle set is determined as follows. Specifically, the virtual queue is divided into two initial sets, which are all vehicles within the unsignalized intersection area. The decision variables are 5 and 4, meaning the number of vehicles in the two initial sets is 5 and 4 respectively. Vehicles 1, 2, 4, and 6 are in the same lane, while vehicles 3, 5, 7, 8, and 9 are in the same lane. Therefore, according to the rule that "vehicles in the same initial set and in the same lane form a quasi-platform," there are four quasi-platforms (vehicles 1, 2, and 4 form one quasi-platform, vehicle 6 forms another, vehicles 3 and 5 form another, and vehicles 7, 8, and 9 form another). Since vehicle number 7, the lead vehicle in the preliminary convoy, is a connected human-driven vehicle unwilling to comply with the speed recommendation, it is treated as a separate vehicle, forming a separate convoy. The remaining vehicles in this preliminary convoy, vehicles 8 and 9, form another preliminary convoy. Since vehicle 8 is a connected human-driven vehicle willing to comply with the speed recommendation, vehicles 8 and 9 form a separate convoy (if vehicle 8 were unwilling to comply with the speed recommendation, it would similarly be treated as a separate vehicle, forming a separate convoy; and so on). The convoy formation scheme is determined based on the formed convoys (vehicles 1, 2, and 4 form one convoy; vehicles 3 and 5 form another convoy; vehicle 6 forms another convoy; vehicle 7 forms another convoy; and vehicles 8 and 9 form another convoy), with the vehicles in the first final set being... The lead vehicle 1 in the convoy (the intelligent connected vehicle leader) follows the optimal trajectory given by the nonlinear programming model, while the lead vehicle 3 in the convoy (the connected human-driven vehicle leader) follows the speed suggestion given by the speed suggestion model for the next time period, which can optimize the convoy's passage order; it is located in the second final set. Since vehicle number 7 in the convoy refused to comply with the speed recommendation, the passage order of the convoy within this final set is determined according to the first-come, first-served rule provided by the first-come, first-served model; combined with the third final set... The time it takes for the inner convoy to enter the unsignalized intersection is used to determine the optimal passage plan under this decision variable, and the total vehicle delay is calculated.

[0151] The optimization model in this example is solved using the optimization toolbox in Matlab software, and the nonlinear programming model is solved using the Gurobi optimization solver software.

[0152] Numerical simulation experiments were conducted using Matlab to verify the effectiveness of the proposed method (denoted as AIM_O). Ten randomized trials were performed, comparing it with the First-Come, First-Served (FCFS) rule, the Longest Queue First (LQF) rule, and the hierarchical optimization method (AIM_H). The results are as follows: Figure 4-8 As shown in Table 1. All three comparison methods are based on platooning, that is, firstly, clustering methods are used to divide the platoons, then the FCFS method determines the platoon passage order according to the first arriving platoon through rules, in LQF, the more vehicles in the lane, the higher the priority, and the AIM_H method establishes an optimization model to determine the optimal order. Figure 4 , 5 In a pure CAV environment (CAV penetration rate of 100%), the average delay and maximum delay of vehicles under different traffic demands are compared. When the traffic demand is 1600veh / h, the method proposed in this example reduces the average delay of vehicles by 50.1%, 44.6% and 21.9% compared with the other three methods. Figure 6 , 7 A comparison chart of average and maximum vehicle delays for different CAV penetration rates (assuming all CVs comply, i.e., are willing to accept speed recommendations) and a traffic demand of 1200veh / h. Figure 8 Table 1 shows the vehicle fuel consumption indicators using the method provided in this example and three other methods. Table 1 presents the average and maximum delays of vehicles under different CV compliance rates when the CAV penetration rate is 30% and the traffic demand is 1200 veh / h. As shown in Table 1, under the LQF and FCFS rules, CVs are considered to always follow the traffic rules, while the AIM_H and AIM_O methods, which optimize traffic order, are affected by the CV compliance rate.

[0153] Table 1. Average and maximum vehicle delays under different CV compliance rates.

[0154]

[0155] This paper uses the proposed method to simultaneously optimize vehicle platooning and platooning sequence at unsignalized intersections, considering both vehicle fairness and vehicle driver (CV) non-compliance rate. Based on the optimization results, an optimal trajectory is provided for the lead vehicle of the vehicle-controlled aerial vehicle (CAV), and speed suggestions are offered to compliant CV lead vehicles. Numerical experiments show that, compared to optimizing only the platooning sequence, the proposed method reduces average vehicle delay by 19.8% at a traffic demand of 2000 veh / h. Furthermore, the average vehicle delay at unsignalized intersections decreases with increasing CAV penetration. Additionally, the proposed method significantly improves traffic efficiency even when the CV non-compliance rate is 25%.

[0156] Through simulation experiments, the proposed method effectively reduces the average and maximum vehicle delays, as well as fuel consumption. Furthermore, the proposed method is also applicable to situations where traffic demand varies across different approach lanes at unsignalized intersections.

Claims

1. A method for vehicle traffic sequence decision-making and trajectory optimization at an unsignalized intersection, characterized in that: The method considers the mixed environment of intelligent connected vehicles and connected human-driven vehicles in a fully connected environment. Utilizing vehicle-to-everything (V2X) technology and autonomous driving technology, it determines the composition of the convoy at each approach lane and the passage sequence at unsignalized intersections, including the following steps: S1. Collect the position, speed, acceleration, vehicle type, and intent information of all vehicles within the unsignalized intersection's emergence zone, as well as the intent information of connected human-driven vehicles. The unsignalized intersection is divided into an emergence zone, a control zone, and a merging zone. First, vehicle platooning and traffic order optimization are performed within the emergence zone. Then, the trajectory optimization or speed suggestion for the lead vehicle in the platoon is implemented within the control zone. Finally, conflict-free passage of the platoon is achieved in the merging zone. The intent information of the connected human-driven vehicles includes willingness to comply with speed suggestions and unwillingness to comply with speed suggestions. S2. Sort all vehicles in the unsignalized intersection area according to their arrival order to form a virtual queue; S3. Based on the vehicle platooning optimization model that takes into account fairness and the intention of connected human driving vehicles, or combined with the first-come-first-served model, determine the optimal platoon composition scheme and conflict-free passage order, thereby planning the time when each platoon enters the unsignalized intersection merging area and the allocated occupancy time of the unsignalized intersection merging area. The decision variable of the unsignaled intersection traffic order optimization model based on vehicle platooning and considering fairness and the driving intentions of connected human vehicles is the number of vehicles in the N initial sets into which the virtual queue is divided. Based on the aforementioned decision variables and combined with the intent information of connected human drivers, a fleet composition scheme is determined, specifically including: Step a: Divide the virtual queue into N sets, and call these N sets the initial set; Step b: Vehicles located in the same initial assembly and in the same lane form a quasi-platform; Step c: Determine the type of the lead vehicle in each quasi-team; if the lead vehicle in a quasi-team is an intelligent connected vehicle, then the quasi-team forms a platoon; if the lead vehicle in a quasi-team is a connected human-driven vehicle and is willing to comply with the speed recommendation, then the quasi-team forms a platoon; if the lead vehicle in a quasi-team is a connected human-driven vehicle and is unwilling to comply with the speed recommendation, then the lead vehicle in the quasi-team is treated as a separate vehicle and forms a platoon, while the remaining vehicles in the quasi-team form another quasi-team. Repeat step c until the quasi-teams form a platoon. Step d: Determine the fleet composition scheme from the formed fleet, and in the determined fleet composition scheme, the first lane in each lane... The 100 convoys belong to a set, called the 100th convoy. A final set; The objective function of the unsignalized intersection traffic order optimization model based on vehicle platooning and considering fairness and the intentions of connected human drivers is: in, Indicates the first The first lane The first set within the final set The moment a vehicle enters the area where an unsignalized intersection appears; Represented as the first The first lane The first set within the final set The planned timing for vehicles to enter the merging area of ​​an unsignalized intersection; Indicates the first The first lane The number of vehicles in the final set; Indicates the total number of lanes; Indicates the total number of the final set; The constraints of the unsignalized intersection traffic order optimization model based on vehicle platooning and considering fairness and the intentions of connected human drivers include: 1) Fundamental constraints of vehicle kinematics, namely, the vehicle's position, velocity, and acceleration satisfy second-order dynamic constraints: in, express The vehicle's location at all times. express The speed of the vehicle at any time express The vehicle's acceleration at any given moment; 2) Earliest Arrival Time Constraint: The planned entry time for a vehicle into the merging area of ​​an unsignalized intersection is restricted by the vehicle's current state and cannot be earlier than the earliest arrival time, i.e.: in, Indicates the current timestamp. This represents the planned entry time of a vehicle into the merging area of ​​an unsignalized intersection. This indicates vehicles appearing in the area of ​​an unsignalized intersection. This represents the final set in the determined convoy composition scheme; earliest arrival time. satisfy: ， (3.2) in, Indicates the maximum speed limit of the vehicle. Indicates the maximum acceleration limit of the vehicle. express The distance of a vehicle from the merging area of ​​an unsignalized intersection at any given time; 3) Fleet Occupancy Time Constraint: The time a fleet occupies the merging area of ​​an unsignalized intersection is related to the number of vehicles in the fleet. Taking into account vehicle start-up reaction time, the maximum fleet occupancy time is calculated. The fleet occupancy time is then calculated using the maximum fleet occupancy time, i.e.: in, Indicates the number of vehicles in the convoy. Indicates the vehicle's start-up reaction time; This indicates the time when the last vehicle in the convoy starts entering the merging zone of an unsignalized intersection, satisfying the following: in, This indicates the distance from the parking position of the last car in the convoy to the merging area of ​​the unsignalized intersection. , This indicates the length of the vehicle's trajectory within the merging zone of an unsignalized intersection. Indicates the length of the vehicle. Indicates the minimum vehicle spacing; 4) No-conflict constraints for platoons: Considering the conflict relationships between different lane trajectories, non-conflict trajectories are assigned to platoons. Compatible platoons can simultaneously occupy the merging area of ​​an unsignalized intersection. Therefore, the rule for determining the platoon passage order is that each platoon enters the merging area of ​​the unsignalized intersection later than a higher-priority platoon that is incompatible with it, i.e.: in, Represented as the first The first lane The planned entry time of the lead vehicle in the convoy within the final set of vehicles into the unsignalized intersection merging area. Represented as the first The first lane The planned entry time of the lead vehicle in the convoy within the final set of vehicles into the unsignalized intersection merging area. Indicates the first The first lane The time that the lead vehicle of a convoy occupies in the unsignalized intersection merging zone within the final settling point. Represents the set of lane numbers. Represents the final set numbered set; if or And the first If lanes have priority in the same final set, then the binary variable... If it is 1, otherwise the binary variable =0; binary variable This is used to identify whether two teams are compatible. If the two teams are compatible, then... ,otherwise ; S4. Based on the current vehicle status, an optimal trajectory is generated for the intelligent connected vehicle leader using a nonlinear programming model. The intelligent connected vehicle leader is controlled to lead the convoy through the unsignalized intersection control area at the planned times when each convoy enters the unsignalized intersection merging area, and through the unsignalized intersection merging area according to the allocated occupancy time. A speed suggestion model is used to provide speed suggestions for the next time period to the connected human-driven vehicle leader that is willing to follow the speed suggestions. The connected human-driven vehicle leader that is willing to follow the speed suggestions leads the convoy through the unsignalized intersection control area at the planned times when each convoy enters the unsignalized intersection merging area, and through the unsignalized intersection merging area according to the allocated occupancy time. The intelligent connected vehicle leader refers to the first vehicle in the convoy being an intelligent connected vehicle, and the connected human-driven vehicle leader refers to the first vehicle in the convoy being a connected human-driven vehicle.

2. The method for vehicle traffic sequence decision-making and trajectory optimization at an unsignalized intersection according to claim 1, characterized in that: A convoy of connected human-driven vehicles that are unwilling to follow speed recommendations, located in the same final set of lanes, determines the order of vehicle passage according to the first-come-first-served model and the rule of the convoy leader arriving first.

3. The method for vehicle traffic sequence decision-making and trajectory optimization at an unsignalized intersection according to claim 1, characterized in that: The nonlinear programming model generates the optimal trajectory for the intelligent connected vehicle's front-end vehicle; the nonlinear programming model is as follows: in, Indicates the current timestamp. This represents the planned entry time of a vehicle into the merging area of ​​an unsignalized intersection. Indicates the time of vehicle trajectory planning. This indicates the distance from the current position of the intelligent connected vehicle's front end to the merging area of ​​the unsignalized intersection. This indicates the expected speed of the intelligent connected vehicle's lead vehicle. Indicates the speed of the intelligent connected vehicle's lead vehicle. The position of the intelligent connected vehicle's lead vehicle at all times. express The speed of the intelligent connected vehicle's lead vehicle at all times. express The acceleration of the intelligent connected vehicle's front end at all times This indicates the minimum speed limit for the lead vehicle in a smart connected vehicle. This indicates the maximum speed limit of the lead vehicle in a smart connected vehicle. This indicates the minimum acceleration limit for the front-end vehicle in a smart connected vehicle. Let represent the maximum acceleration limit of the intelligent connected vehicle's lead vehicle; Equation (6.1) represents minimizing Time to Fuel consumption of the intelligent connected vehicle's lead vehicle within a given time period; Equation (6.2) represents the speed limit of the intelligent connected vehicle's lead vehicle; Equation (6.3) represents the acceleration limit of the intelligent connected vehicle's lead vehicle; Equation (6.4) represents the position update rule of the intelligent connected vehicle's lead vehicle; Equation (6.5) represents the speed update rule of the intelligent connected vehicle's lead vehicle; Equation (6.6) represents... The speed constraint of the intelligent connected vehicle's lead vehicle at any given time is the initial constraint; Equation (6.7) represents The position constraint of the intelligent connected vehicle's lead vehicle at any given time is the initial constraint; Equation (6.8) represents The speed constraint of the intelligent connected vehicle's lead vehicle at any given time is a terminal constraint; Equation (6.9) represents The position constraints of the intelligent connected vehicle's lead vehicle at all times are terminal constraints.

4. The method for vehicle traffic sequence decision-making and trajectory optimization at an unsignalized intersection according to claim 1, characterized in that: The speed suggestion model provides speed suggestions for the next time period to the lead vehicle of the connected human-driven vehicle that is willing to follow the speed suggestion, including acceleration, constant speed, and deceleration suggestions; the speed suggestion model is as follows: when At that time, the speed suggestion model provides acceleration suggestions to the lead vehicle of the connected human-driven vehicle that is willing to follow the speed suggestion; when At that time, the speed recommendation model provides a constant speed recommendation to the lead vehicle of the connected human-driven vehicle that is willing to follow the speed recommendation; when At that time, the speed suggestion model provides deceleration suggestions to the lead vehicle of the connected human-driven vehicle that is willing to follow the speed suggestion; in, Indicates the current timestamp. This represents the planned entry time of a vehicle into the merging area of ​​an unsignalized intersection. The speed of the lead vehicle in a connected human-driven vehicle that is always willing to follow speed recommendations. The distance from the current position of the lead vehicle in a connected human-driven vehicle to the unsignalized intersection merging zone, indicating a willingness to comply with speed recommendations. The maximum deceleration of the lead vehicle in a connected human-driven vehicle that indicates its willingness to comply with speed recommendations; and The parameter is used to adjust the controllable speed, and a conservative approach is recommended.

5. The method for vehicle traffic sequence decision-making and trajectory optimization at an unsignalized intersection according to claim 1, characterized in that: The rules for maintaining a safe distance between connected vehicles (excluding the lead vehicle) and the vehicle in front of them in a convoy are as follows: in, Indicates the current timestamp. Indicates time interval, express The distance between the intelligent connected vehicle (not the lead vehicle) and the vehicle in front of it in the convoy. This indicates the vehicle length of the connected vehicles that are not the lead vehicle in the convoy. Indicates the expected gap. Indicates tracking error. This represents the first derivative of the tracking error with respect to time. = 0.45 and = 0.125 is the parameter for adjusting the gap. express The speed of the connected vehicles in the convoy that are not the lead vehicle. express The speed of the intelligent connected vehicles that are not the lead vehicle in the fleet.

6. The method for vehicle traffic sequence decision and trajectory optimization at an unsignalized intersection according to claim 1, characterized in that: The behavior of non-lead connected human-driven vehicles in the convoy and the lead connected human-driven vehicle willing to follow speed recommendations is modeled using a Gipps car-following model that considers randomness, which is represented as: in, express Acceleration is generated in real time based on the Gipps car-following model; This represents the standard deviation under different accelerations; It is a uniformly distributed random number between 0 and 1; It is either 1 or -1, with a probability of 50% each; This indicates the actual acceleration during execution.

7. A system for vehicle traffic order decision-making and trajectory optimization at an unsignalized intersection, characterized in that: The system operates using the method described in any one of claims 1-6; the system includes roadside sensing and communication equipment, a roadside computing unit, and an on-board unit. The roadside sensing and communication equipment collects the position, speed, acceleration, vehicle type, and networked human driving intention information of all vehicles in the unsignalized intersection area, and transmits the information of all vehicles in the unsignalized intersection area to the roadside computing unit. The roadside computing unit, acting as the management center of the unsignalized intersection, sorts all vehicles within the unsignalized intersection area according to their arrival order based on the received vehicle information, forming a virtual queue. It then determines the optimal platoon composition and conflict-free passage sequence, thereby planning the entry time of each platoon into the unsignalized intersection merging area and the allocated occupancy time of the merging area, and transmits this information to the onboard unit. The roadside computing unit includes an unsignalized intersection passage sequence optimization model based on vehicle platooning and considering fairness and the intentions of connected human drivers, as well as a first-come, first-served model. The on-board unit includes a nonlinear programming model and a speed suggestion model. The nonlinear programming model generates the optimal trajectory for the intelligent connected vehicle leader and controls the intelligent connected vehicle leader so that it leads the convoy through the unsignalized intersection control area at the planned times when each convoy enters the unsignalized intersection merging area, and through the unsignalized intersection merging area according to the allocated occupancy time. The speed suggestion model provides speed suggestions for the next time period to the connected human-driven vehicles leader that are willing to comply with the speed suggestions, so that the connected human-driven vehicles leader that are willing to comply with the speed suggestions lead the convoy through the unsignalized intersection control area at the planned times when each convoy enters the unsignalized intersection merging area, and through the unsignalized intersection merging area according to the allocated occupancy time.

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