Hierarchical platoon merging control method and device for networked autonomous vehicles

Abstract

The application provides a hierarchical platoon merging control method and device for networked autonomous vehicles, and relates to the technical fields of intelligent traffic and vehicle control. The method comprises the following steps: grouping target vehicles according to vehicle state information of the target vehicles entering a platoon formation area to obtain a plurality of target platoons; adjusting the driving trajectories of the target vehicles in each target platoon when the target platoon enters a platoon adjustment area to generate a platoon grouping with a fixed platoon shape direction; in a platoon cooperative control area, constructing an optimization model of a merging sequence according to platoon state information of each target platoon in the platoon grouping to predict the merging sequence and target arrival time of the target platoon entering a platoon merging area; and controlling each target platoon in the platoon grouping to sequentially complete merging in the platoon merging area according to the merging sequence and target arrival time. The application is used to solve the problems of poor safety of platoon merging in the prior art and difficulty in realizing high-speed, continuous and efficient operation of traffic flow.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation and vehicle control technology, specifically to a hierarchical fleet merging control method and device for networked autonomous vehicles. Background Technology

[0002] With rapid economic and social development, urban traffic pressure is increasing, and merging ramps on highways and urban expressways are gradually becoming traffic bottlenecks. Existing research shows that in cities with more than one million vehicles, over 80% of highways experience varying degrees of congestion during morning and evening rush hours, with merging ramps being particularly prominent. These areas not only limit road efficiency but also increase energy consumption and accident risks. Traditional ramp merging control relies heavily on traffic signal control, ramp metering, or driver experience, but these methods often have the following shortcomings under high-traffic conditions: First, signal control modes cause frequent vehicle starts and stops, reducing efficiency and increasing fuel consumption and emissions; second, relying solely on driver decision-making can easily lead to abrupt acceleration, deceleration, or lane changes, potentially causing traffic conflicts or even accidents.

[0003] With the development of vehicle-to-everything (V2X) technology and intelligent connected vehicles, real-time information interaction and collaborative perception between vehicles and between vehicles and infrastructure are possible, providing new possibilities for continuous flow control in merging zones. Connected autonomous vehicles can significantly increase road capacity and reduce energy consumption and emissions by shortening vehicle spacing and improving reaction speed. Existing research on merging control largely focuses on two aspects: rule-based methods, such as first-in-first-out and yielding, which are simple but struggle to balance global efficiency and safety; and optimization-based methods, such as using mixed-integer programming, game theory, or deep reinforcement learning to optimize vehicle merging order and trajectory. However, these methods often suffer from high computational complexity, lack of real-time performance, or neglect of platoon formation mechanisms, resulting in poor platoon merging safety and difficulty in achieving high-speed, continuous, and efficient traffic flow. Summary of the Invention

[0004] In view of this, it is necessary to provide a hierarchical fleet merging control method and device for connected autonomous vehicles to solve the problems of poor safety of existing fleet merging technology and difficulty in achieving high-speed, continuous and efficient traffic flow.

[0005] To address the aforementioned problems, this invention provides a hierarchical fleet merging control method for connected autonomous vehicles, comprising: Obtain the vehicle status information of the target vehicles entering the convoy formation area, and group the target vehicles according to the vehicle status information to obtain multiple target convoys; When the target convoy enters the convoy adjustment area, the driving trajectory of the target vehicles in each target convoy is adjusted to generate a convoy group with a fixed formation direction; In the fleet coordination control area, based on the fleet status information of each target fleet in the fleet formation, an optimization model for the merging sequence is constructed. Based on the optimization model, the merging sequence of the target fleets entering the fleet merging area and the target arrival time are predicted. In the convoy merging area, each target convoy in the convoy formation is controlled to merge sequentially according to the merging order and the target arrival time.

[0006] In one possible implementation, the method further includes: The control area for merging the convoy is divided into four consecutive functional areas according to a preset fixed distance. The functional areas include the convoy formation area, the convoy adjustment area, the convoy coordination control area, and the vehicle merging area. Each of the functional areas is equipped with a roadside unit at its entrance, which is used to collect vehicle status information of target vehicles entering the functional area.

[0007] In one possible implementation, the target vehicles are grouped according to the vehicle status information to obtain multiple target convoys, including: For each target vehicle, the relative distance between the target vehicle and the vehicle in front is determined based on the vehicle status information; Under the maximum permissible acceleration, calculate the time and distance required for the target vehicle to accelerate to the maximum speed based on the current speed of the target train; When the required distance is not less than the relative distance, the catch-up time for the target vehicle to merge into the convoy of the vehicle in front is calculated based on the maximum allowable acceleration and the current speed. When the required distance is less than the relative distance, the catch-up time for the target vehicle to merge into the convoy of the vehicle in front is calculated based on the required time and the maximum vehicle speed. The efficiency score for the target vehicle to merge into the platoon of the preceding vehicle is calculated based on the catch-up time and the availability index of the platoon to which the preceding vehicle belongs. When the benefit score is positive, the target vehicle is grouped as a member vehicle into the convoy where the preceding vehicle is located. When the benefit score is negative, the target vehicle is grouped as a lead vehicle into a newly established target convoy.

[0008] In one possible implementation, adjusting the driving trajectory of the target vehicles in each target convoy to generate a convoy formation with a fixed formation direction includes: When each of the target convoys enters the convoy adjustment zone, the initial boundary conditions for each target vehicle in the target convoy are determined, including the entry time, current position, and maximum available catch-up time. The preset trajectory optimization model is invoked to perform trajectory planning on the initial boundary conditions, generating the planned speed, planned acceleration, and planned trajectory for each target vehicle; The trajectory of the target vehicles is controlled according to the planned speed and the planned acceleration, so that the target vehicles travel along the planned trajectory and form a convoy with a fixed formation direction.

[0009] In one possible implementation, the step of invoking a preset trajectory optimization model to perform trajectory planning on the initial boundary conditions, generating the planned speed, planned acceleration, and planned trajectory for each target vehicle, includes: Based on the initial boundary conditions, the weight parameters of each time state equation in the trajectory optimization model are calculated to obtain the state optimization equation. The state optimization equation includes equations for the vehicle position, vehicle speed, vehicle acceleration, and jitter value changing with time. The constraints of the state optimization equation include: the vehicle speed does not exceed the speed limit specified by the road, the vehicle acceleration does not exceed the preset acceleration limit, and the vehicle jitter value is within the preset jitter range. The initial vehicle state information and the final vehicle state information of each target vehicle are input into the state optimization equation. Under the condition of balancing vehicle energy consumption and ride comfort, the state optimization equation is solved by calling the Pontryagin maximum value to obtain the planned speed, planned acceleration and planned trajectory of each target vehicle. Among them, the ride comfort value is negatively correlated with the vehicle vibration value, and the vehicle energy consumption is positively correlated with the planned speed.

[0010] In one possible implementation, constructing an optimization model for the merging order based on the fleet status information of each target fleet in the fleet formation includes: A prediction window is established at the entrance of the convoy collaborative control area to determine the real-time status information of each target convoy entering the prediction window in the convoy formation. The real-time status information includes the vehicle status information of the lead vehicle in the target convoy. Calculate the earliest reachable time of the target convoy to the convoy rendezvous area based on the real-time status information; A weighted optimization function is constructed based on the earliest reachable time and the assigned arrival time of the target convoy at the convoy rendezvous area to obtain an optimization model for the merging order; The optimization objective of the weighted optimization function is to minimize the maximum interval between the assigned arrival times of the target convoys and to minimize the overall delay time of the convoy formation. The constraints of the weighted optimization function include: the time windows of the assigned arrival times of the target convoys do not overlap, do not exceed the road speed limit and road physical boundary, there is no overtaking, the distance between vehicles does not exceed the prescribed safety distance, and the assigned arrival time of the target convoy is not earlier than the earliest reachable time.

[0011] In one possible implementation, controlling the merging of each target convoy in the convoy formation according to the merging order and the target arrival time includes: Based on the merging order and the target arrival time, determine the earliest reachable time and entrance position of each target convoy in the convoy formation to enter the convoy rendezvous area; The initial boundary conditions for the lead vehicle in the target vehicle are constructed based on the earliest reachable time and the entrance position, and the terminal boundary conditions are set based on the exit position of the target vehicle convoy leaving the convoy rendezvous area and the expected departure speed. A merging optimization function is constructed, which includes state optimization equations for vehicle position, vehicle speed, and vehicle acceleration. The optimization objective of the merging optimization function is to minimize vehicle energy consumption and maximize ride comfort. The constraints of the merging optimization function include: vehicle speed not exceeding the speed limit specified by the road, vehicle acceleration not exceeding a preset upper limit, and vehicle spacing not exceeding the specified safety distance. Among these constraints, ride comfort is negatively correlated with vehicle vibration, and vehicle energy consumption is negatively correlated with the rate of change of vehicle acceleration. The initial boundary conditions and terminal boundary conditions are input into the platoon merging optimization function, and under the optimization objective, the Pontryagin maximum is called to solve the platoon merging optimization function to obtain the merging trajectory of each target vehicle. Based on the merging trajectory and the merging sequence, the target convoys in the convoy formation are controlled to merge sequentially.

[0012] The present invention also provides a hierarchical fleet merging control device for connected autonomous vehicles, comprising: The platoon formation module is used to acquire the vehicle status information of target vehicles entering the platoon formation area, and to group the target vehicles according to the vehicle status information to obtain multiple target platoons. The convoy adjustment module is used to adjust the driving trajectory of each target vehicle in the target convoy when the target convoy enters the convoy adjustment area, and generate a convoy formation with a fixed formation direction. The fleet coordination control module is used to construct an optimization model of the merging sequence in the fleet coordination control area based on the fleet status information of each target fleet in the fleet group, and predict the merging sequence of the target fleet entering the fleet merging area and the target arrival time based on the optimization model. The convoy merging module is used to control each target convoy in the convoy formation to merge sequentially according to the merging order and the target arrival time in the convoy merging area.

[0013] This invention provides a hierarchical merging control method and apparatus for connected autonomous vehicles. The merging process is completed sequentially in the merging zone, merging adjustment zone, merging collaborative control zone, and merging convergence zone. Vehicle grouping is completed in the merging zone using the target vehicle's state information. In the adjustment zone, vehicle trajectories are coordinated to ensure orderly merging. Finally, in the collaborative control zone, an optimization model predicts the merging sequence and arrival time, enabling continuous alternating merging of merging groups in the merging convergence zone. By setting up multiple levels of grouping, adjustment, and collaborative control in different functional zones, and controlling vehicle grouping to complete merging in real time, energy consumption can be effectively reduced and road traffic efficiency improved while ensuring vehicle safety. This overcomes the limitations of traditional merging methods that rely on yielding and queuing. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart illustrating the hierarchical fleet merging control method for networked autonomous vehicles provided by the present invention. Figure 2 This is a schematic diagram illustrating the division of the vehicle merging control area provided by the present invention. Figure 3 A schematic diagram of vehicle control in the platoon formation area provided by the present invention; Figure 4 This is a schematic diagram of vehicle formation in the convoy formation area provided by the present invention; Figure 5 A schematic diagram illustrating the trajectory planning of the vehicle adjustment area provided by this invention; Figure 6 A schematic diagram of the vehicle cooperative control area provided by the present invention; Figure 7 A schematic diagram of vehicle merging in the fleet merging area provided by the present invention; Figure 8 A schematic diagram of the hierarchical fleet merging control device for networked autonomous vehicles provided by the present invention. Figure 9 A schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0017] In the description of the embodiments of this application, unless otherwise stated, "a plurality of" means two or more.

[0018] In this embodiment of the invention, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, apparatus, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product or device.

[0019] The naming or numbering of steps in the embodiments of the present invention does not mean that the steps in the method flow must be executed in the time / logical order indicated by the naming or numbering. The execution order of the named or numbered process steps can be changed according to the technical purpose to be achieved, as long as the same or similar technical effect can be achieved.

[0020] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0021] Before describing the embodiments, the relevant terms and symbols used in this application will be explained in order to facilitate understanding of the steps and processes of the methods in the embodiments of the present invention.

[0022] The merging control area refers to the control section where vehicles merge from the main road and ramps (or side roads) of the highway. The control section can be divided into four continuous areas according to different functions: the merging formation area (length L1, unit: m), the merging adjustment area (hereinafter referred to as the adjustment area, length L2, unit: m), the merging coordination control area (hereinafter referred to as the coordination control area, length L3, unit: m), and the merging area (hereinafter referred to as the merging area, length L4, unit: m).

[0023] Vehicle status information: mainly includes the vehicle's current position, speed, and acceleration. The longitudinal position of the i-th target vehicle at time t is denoted as x. i (t), in meters, the velocity of the i-th target vehicle at time t is denoted as v. i (t), the unit is m / s, and the maximum vehicle speed is denoted as v. max The minimum vehicle speed is denoted as v. min The acceleration of the i-th target vehicle at time t is denoted as: a i (t) is in m / s 2 The maximum permissible acceleration is denoted as a. max The minimum acceleration is denoted as a. min .

[0024] Vehicle vibration value: Used to quantify the degree of vibration during vehicle operation. The vibration value of the i-th target vehicle at time t is denoted as: u i (t), unit is m / s 3 The maximum jitter is u max The minimum value of jitter is denoted as u. min .

[0025] t 0 k,i : Indicates the moment when the i-th target vehicle in the k-th target convoy enters the convoy formation area (i.e., the convoy adjustment area).

[0026] t f k,i : Indicates the moment when the i-th target vehicle in the k-th target convoy leaves the convoy formation area.

[0027] t assign k,i : Indicates the assigned arrival time of the i-th target vehicle in the k-th target convoy.

[0028] n k : Indicates the number of vehicles in the k-th target convoy.

[0029] R: Represents the maximum capacity of the target fleet.

[0030] This invention provides a hierarchical fleet merging control method and apparatus for connected autonomous vehicles, which will be described below.

[0031] The hierarchical fleet merging control method for connected autonomous vehicles provided by this invention can be applied to vehicle merging scenarios where multiple vehicles merge from ramps or side roads into the main road. In specific implementation, the execution entity of the method can be a traffic control unit in a traffic management device or a cloud server. It can monitor the vehicle merging scenario through roadside units deployed in the actual road environment, collect vehicle status information and traffic data using roadside units for analysis, and then use the hierarchical fleet merging control method for connected autonomous vehicles provided by this invention to generate a merging control strategy in real time and distribute it to each vehicle to complete the merging from the ramp or side road into the main road.

[0032] Figure 1 A flowchart illustrating a hierarchical fleet merging control method for connected autonomous vehicles, as shown below. Figure 1 As shown, the hierarchical fleet merging control method for connected autonomous vehicles includes steps 101 to 104, which will be described in detail below.

[0033] Step 101: Obtain the vehicle status information of the target vehicles entering the convoy formation area, and group the target vehicles according to the vehicle status information to obtain multiple target convoys.

[0034] Here, before starting the merging control from the merging formation area, this embodiment of the invention further divides the merging control area into four consecutive functional areas according to a preset fixed distance. The functional areas specifically include the merging formation area, the merging adjustment area, the merging coordination control area, and the vehicle merging area.

[0035] The following section will illustrate this using the scenario of vehicles merging at the main road and the ramp, such as... Figure 2As shown, both ramps and main roads have entrances. The ramp entrances and main road entrances form a traffic convergence area near the merging point. Ramp entrances are typically single-lane, while main road entrances may include multiple parallel lanes. Vehicles entering from the main road have priority within the merging area, while ramp vehicles can only merge into the main road traffic flow after meeting the merging conditions. The platoon formation zone is located upstream of the ramp and main road entrances, and its function is to group incoming target vehicles. The adjustment zone, adjacent to the platoon formation zone, coordinates vehicle speed and distance to ensure vehicles reach the set desired speed and safe distance. The collaborative control zone, located downstream of the adjustment zone, is responsible for trajectory coordination and conflict avoidance between different target platoons. The merging zone is the final convergence area, where target vehicles complete a natural, alternating merging process according to a preset merging sequence. After functional zoning, the merging control area consists of four zones: the merging formation zone (L1) at the very front of the ramp and main road entrances; the adjustment zone (L2) immediately following the formation zone; the coordination control zone (L3) downstream of the adjustment zone; and the merging zone (L4) at the final junction. These four zones are longitudinally connected end-to-end, forming the total merging control area, with a total length L equal to the sum of the lengths of the four zones.

[0036] At the entrance of each functional area, roadside units (RSUs) are installed. These RSUs are equipped with vehicle detection and information interaction functions to collect vehicle status information of target vehicles entering the functional area, including longitudinal position, driving speed and acceleration, and upload the collected vehicle status information.

[0037] In this embodiment of the invention, the control area for merging of vehicles is divided into four consecutive functional areas according to a preset fixed distance. Different control objectives and operation methods can be set in different functional areas to achieve hierarchical control. This allows vehicles to pass continuously at the merging point of the ramp and the main road without stopping and waiting, breaking through the traditional method of yielding at ramps and merging in sequence.

[0038] like Figure 3 As shown, when a target vehicle enters the platoon formation zone, roadside units deployed on the actual road surface continuously collect data, including the target vehicle's longitudinal position, speed, and acceleration, as well as the timestamp of the target vehicle's arrival at the platoon formation zone boundary. This data is used to calculate the relative distance between the target vehicle and the vehicle in front, and then to calculate the target vehicle's catch-up time, determining whether the target vehicle can merge into the platoon of the vehicle in front. This process is then used to group each target vehicle into individual target platoons.

[0039] In some embodiments, the target vehicles are grouped according to vehicle status information to obtain multiple target convoys, which can be achieved in the following ways, as described in detail below.

[0040] For each target vehicle, the relative distance between the target vehicle and the vehicle in front is determined based on the vehicle status information, and the time and distance required for the target vehicle to accelerate to the maximum speed are calculated based on the current speed of the target train, under the condition of maximum allowable acceleration.

[0041] like Figure 4 As shown, for each target vehicle, after collecting vehicle status information, it is first determined whether the target vehicle is the lead vehicle. If so, the corresponding target vehicle is directly designated as the lead vehicle of the target convoy, thus completing vehicle formation. If not, the relative distance to the vehicle in front is calculated based on the collected vehicle status information. This is calculated by subtracting the current target vehicle's position from the position of the vehicle in front and the ideal distance. Here, the ideal distance is the expected following distance within the target convoy, and the relative distance is denoted as d. target This can be expressed as the following formula: (1) Where, x k,i-1 (t 0 k,i ) indicates the position of the vehicle in front, specifically at t 0 k,i The position of the (i-1)th target vehicle in the k-th target convoy entering the convoy formation area at time x k,i (t 0 k,i ) represents the current position of the target vehicle, i.e., at t 0 k,i The time is the position of the i-th target vehicle in the k-th target convoy entering the convoy formation area, d f The ideal distance is the expected following distance within the target convoy.

[0042] After obtaining the relative distance, the next step is to determine whether the target vehicle can catch up during the acceleration period. This requires calculating the catch-up time for the target vehicle to merge into the convoy of the vehicle in front. First, based on the target vehicle's current speed, the time t required to accelerate to the maximum speed under the maximum permissible acceleration is calculated. acc Distance d acc , respectively represented as: (2) (3) Among them, v k,i Let v represent the current speed of the i-th target vehicle in the k-th target convoy. max Indicates the maximum speed reached, a max This indicates the maximum permissible acceleration.

[0043] When the required distance is not less than the relative distance, the catch-up time for the target vehicle to merge into the convoy is calculated based on the maximum allowable acceleration and the current speed. When the required distance is less than the relative distance, the catch-up time for the target vehicle to merge into the convoy is calculated based on the required time and the maximum speed.

[0044] When the required distance is not less than the relative distance, i.e., d acc ≥d target At that time, the maximum permissible acceleration and current speed of the target vehicle can be used to calculate the catch-up time t for the target vehicle to merge into the convoy. catch , is represented as: (4) However, if the required distance is less than the relative distance, i.e., d acc <d target The time required for the target vehicle to merge into the convoy is calculated using the required time and the maximum speed, and is expressed as: (5) This section determines whether the target vehicle meets the conditions for merging into the convoy based on the required distance and relative distance, i.e., whether the target vehicle can catch up during the acceleration phase. If so, it means the target vehicle can catch up during the acceleration phase, and the acceleration time is used as the standard, i.e., the catching-up time equals the acceleration time. If not, it means the target vehicle cannot catch up during the acceleration phase, and the travel time during the constant speed phase needs to be calculated after reaching maximum speed. Finally, the acceleration time and the travel time during the constant speed phase are added together to obtain the catching-up time, i.e., the catching-up time equals the acceleration time plus the constant speed travel time.

[0045] Furthermore, the calculated catch-up time is used to determine the conditions for vehicle merging. This step requires calculating the catch-up cost, that is, calculating the benefit score of the target vehicle merging into the platoon based on the catch-up time and the availability index of the preceding vehicle's platoon. The result of this benefit score reflects the cost incurred by the target vehicle in the process of catching up with the preceding vehicle, that is, the trade-off between the potential efficiency gains and energy costs of the target vehicle joining the preceding vehicle's platoon. The benefit score is denoted as S. i , is represented as: (6) Where a1 and a2 represent the preset efficiency weight and energy consumption weight, respectively, gain i The availability index of the convoy to which the preceding vehicle belongs is specifically expressed as: (7) Where, n k R represents the number of vehicles in the platoon that the preceding vehicle belongs to, and R represents the maximum number of vehicles in the platoon that the preceding vehicle belongs to. The availability metric is gain. iA value of 1 indicates that the convoy of the vehicle in front is not full, while a value of 0 indicates that the convoy of the vehicle in front is full.

[0046] When the benefit score is positive, the target vehicle is grouped as a member vehicle and enters the target convoy where the preceding vehicle is located. When the benefit score is negative, the target vehicle is grouped as a lead vehicle and formed into a newly established target convoy.

[0047] See also Figure 4 After calculating the cost of catching up and obtaining the benefit score, it is then determined whether the benefit score is greater than 0 and the convoy of the preceding vehicle is not full. If so, the target vehicle is grouped into the convoy of the preceding vehicle. If not, a new target convoy is created based on the target vehicle, and the target vehicle is used as the lead vehicle of the target convoy.

[0048] Here, when the benefit score is positive, it means that the benefits of platooning outweigh the costs of catching up. If the platoon of the preceding vehicle is not full, the target vehicle can be grouped into the platoon of the preceding vehicle as a member vehicle. However, if the benefit score is negative, it means that the benefits of platooning outweigh the costs of catching up. In this case, the target vehicle will not be merged into the platoon of the preceding vehicle, but a new target platoon will be created directly, and the target vehicle will serve as the lead vehicle of the target platoon.

[0049] Based on the above formation process, each target vehicle on the ramp and main road is grouped into a convoy, forming multiple target convoys. Then, the formation information of the target convoys is sent to the on-board unit of each target vehicle for control execution.

[0050] In this embodiment of the invention, convoy formation is achieved by calculating the catch-up time for each target convoy in the convoy formation zone. This avoids conflicts and delays caused by disorderly insertion of individual vehicles, while also maximizing the utilization of road resources. When a target vehicle merges into a target convoy, it is easier to adjust its speed and distance, maintaining a consistent rhythm and safe spacing with other vehicles in the target convoy. This significantly reduces the complexity of the merging process and facilitates subsequent convoy adjustments and coordinated control.

[0051] Step 102: When the target convoy enters the convoy adjustment area, the driving trajectory of the target vehicles in each target convoy is adjusted to generate a convoy group with a fixed formation direction.

[0052] like Figure 5As shown, after the target vehicles are grouped into multiple target convoys in the convoy formation area, they drive normally into the adjustment area. At this time, the driving status of each target convoy continues to be monitored by roadside units deployed at the entrance of the adjustment area. In the adjustment area, this embodiment of the invention uses the vehicle status information of the target convoys entering the adjustment area to plan the driving trajectory of the target vehicles. The target vehicles only need to adjust their speed and spacing according to the plan to maintain a stable longitudinal formation. Meanwhile, the lead vehicles between adjacent target convoys exchange trajectory information through information interaction between vehicles and between vehicles and roadside units, and perform lateral and longitudinal coordinated adjustments to avoid potential conflicts.

[0053] In some embodiments, adjusting the driving trajectories of target vehicles in each target convoy to generate a convoy formation with a fixed formation direction can be achieved in the following ways.

[0054] First, when each target convoy enters the convoy adjustment zone, the initial boundary conditions for each target vehicle in the convoy are determined. These initial boundary conditions include the entry time, current position, and maximum available catch-up time. Next, a pre-defined trajectory optimization model is invoked to plan the trajectory based on the initial boundary conditions, generating the planned speed, planned acceleration, and planned trajectory for each target vehicle. Finally, the driving trajectories of the target vehicles are controlled according to the planned speed and planned acceleration to ensure that the target vehicles travel along the planned trajectories, forming a convoy with a fixed formation direction.

[0055] Specifically, after the target convoy is formed, trajectory planning is performed on the target vehicles within the same convoy. The operational status of the target vehicles is constrained by the convoy boundary conditions, meaning that the departure time and position of the target vehicles when entering the adjustment zone from the formation zone exit must meet certain rules. For example... Figure 5As shown, for a specific target convoy, after the lead vehicle enters the adjustment zone from the formation zone exit, the boundary conditions are immediately determined by the road testing unit, including the entry time, current position, and maximum available catch-up time. This is because the departure time of each target vehicle depends not only on the departure time of the vehicle ahead but also on its own entry time into the adjustment zone and its catch-up time. For ordinary member vehicles within the same target convoy, their departure time is adjusted based on the departure time of the vehicle ahead, with the addition of corrections for headway and desired speed, to ensure a stable spacing within the convoy. For the lead vehicle of a newly established target convoy, its departure time is determined based on the departure time of the last vehicle in the previous target convoy, combined with the minimum safe distance between the two target convoys, thus avoiding interference between convoys. Furthermore, dynamic constraints on the target vehicle itself are introduced. If a target vehicle requires a certain catch-up process to approach the vehicle ahead, its departure time is adjusted based on the catch-up time and minimum safe distance to ensure that the target vehicle does not leave the vehicle formation zone prematurely. The final departure time is determined by the maximum value of the conditions of the vehicle ahead and the vehicle's own conditions, thus achieving a balance between safety and efficiency.

[0056] Furthermore, this embodiment of the invention pre-constructs a trajectory optimization model to achieve distributed control of the target convoy in the convoy adjustment zone. The trajectory optimization model can be deployed in the onboard unit of each target vehicle. When the target convoy enters the vehicle adjustment zone, the model can be invoked for trajectory planning. This model is constructed using the principle of Pontryagin's maximum. By setting speed range, acceleration range, and jitter range as constraints, and introducing energy consumption parameters and comfort weight parameters, it can balance energy efficiency and ride smoothness during trajectory planning. By inputting initial boundary conditions such as the departure time, position, and maximum available catch-up time of the target vehicle when entering the adjustment zone from the formation zone exit into the model, the model calculates and solves the speed, acceleration, and position trajectory of the target vehicle using the principle of Pontryagin's maximum under the condition that the target vehicle's fixed departure time and position boundary conditions are met.

[0057] Once the target vehicle planning is complete, the planning information is directly returned to the onboard unit. The onboard unit then optimizes the target vehicle's trajectory based on the planned speed and acceleration, adjusting the vehicle's speed and spacing to ensure the target vehicles follow the planned trajectory. This ensures all target vehicles form a fixed convoy formation with a stable longitudinal configuration. Subsequently, the target vehicles prioritize leaving the adjustment area, and the status information of the vehicles leaving the adjustment area is recorded by the road testing unit.

[0058] In this embodiment of the invention, a trajectory optimization model is used to achieve distributed control of the target convoy in the convoy adjustment zone. This enables trajectory coordination of target vehicles within and between target convoys at a macro level. The formation of convoy groups with fixed formation directions also avoids the disorder of random insertion of single vehicles into the convoy. The entire process of vehicles in the adjustment zone relies on trajectory planning, and only corresponding speed and spacing adjustments are made, ensuring safety and comfort during vehicle merging.

[0059] In one possible implementation, a preset trajectory optimization model is invoked to perform trajectory planning on the initial boundary conditions, generating the planned speed, planned acceleration, and planned trajectory for each target vehicle. This can be achieved in the following ways, which are explained in detail below.

[0060] The weight parameters of each time state equation in the trajectory optimization model are calculated based on the initial boundary conditions to obtain the state optimization equation. The initial vehicle state information and the final vehicle state information of each target vehicle are input into the state optimization equation. Under the condition of balancing vehicle energy consumption and ride comfort, the state optimization equation is solved by calling the Pontryagin maximum value to obtain the planned speed, planned acceleration and planned trajectory of each target vehicle.

[0061] Here, the trajectory optimization model is used to simultaneously plan the target vehicle's position, velocity, acceleration, and jitter value. The model consists of four time-state equations, specifically the equations for how the vehicle's position, velocity, acceleration, and jitter value change over time, expressed as follows: (8) (9) (10) (11) Where, x k,i (t), v k,i (t), a k,i (t), u k,i (t) represents the vehicle position, vehicle speed, vehicle acceleration, and vibration value to be planned, respectively. w1 and w2 are the preset weight parameters for vehicle energy consumption and ride comfort, respectively. If vehicle planning focuses more on vehicle energy consumption, then w1 is larger, and if it focuses more on ride comfort, then w2 is larger. Under equilibrium, both are taken as 0.5, c1 (i) c2 (i) c3 (i) c4 (i) c5 (i) c6 (i) These are parameters that can be calculated by substituting the initial boundary conditions of the target vehicle.

[0062] The constraints of the state optimization equation include: the vehicle speed does not exceed the speed limit specified by the road, the vehicle acceleration does not exceed the preset upper limit, and the vehicle vibration value is within the preset vibration range.

[0063] The constraints here are to ensure that the trajectory planning process remains within the specified speed, acceleration, and jitter ranges. The speed range constrains vehicle speed; its upper limit is generally the maximum speed limit stipulated by the road, such as the speed a vehicle cannot exceed within a ramp, and its lower limit is the minimum speed the vehicle must travel at, ensuring the target vehicle travels normally and avoids unnecessary stops. The acceleration range is similar to the speed range, constraining changes in vehicle speed. For example, a vehicle cannot accelerate within a ramp. The upper limit of the acceleration range is generally 0, while the lower limit ensures that the target vehicle's speed does not drop to 0 when decelerating, thus preventing unnecessary stops. The jitter range constrains the jitter generated during the target vehicle's movement within a reasonable range, ensuring that the vehicle jitter value is within the preset jitter range [u]. min u max This ensures a comfortable ride.

[0064] When performing planning calculations using the state optimization equations, the initial and final vehicle state information of each target vehicle are directly input into the equations. Specifically, the initial vehicle state information includes the target vehicle's speed, time, and position upon entering the adjustment zone, which can be directly measured by the road testing unit at the adjustment zone entrance. This includes the vehicle's initial speed, initial entry time, initial position, and initial acceleration. The final vehicle state information refers to the target vehicle's state information upon reaching the adjustment zone exit. This information is pre-configured and includes the arrival time at the exit, final position, and final acceleration.

[0065] The trajectory optimization model, during planning and calculation, also needs to incorporate a condition balancing vehicle energy consumption and ride comfort. In other words, trajectory planning must be completed while maintaining a balance between these two factors. Here, vehicle energy consumption is positively correlated with the planned speed; that is, the higher the planned speed, the higher the vehicle energy consumption. During the planning process, vehicle energy consumption can be calculated in real-time based on the planned speed. Furthermore, ride comfort is negatively correlated with vehicle vibration; the higher the vehicle vibration, the lower the ride comfort. A negative weight can be set to weight the real-time determined vehicle vibration values, thereby determining the real-time ride comfort value of the target vehicle.

[0066] In each trajectory planning process, the trajectory optimization model adjusts the planning strategy based on vehicle energy consumption and ride comfort values. Under the aforementioned constraints, when planning the speed, acceleration, and trajectory of the target vehicle, the Pontryagin maximum solution is used to solve the state optimization equations, yielding the planned speed, planned acceleration, and planned trajectory for each target vehicle, thus generating speed curves, trajectory curves, and acceleration curves.

[0067] At the execution level, the output of the trajectory optimization model is used to generate corresponding control commands. Upon receiving these commands, the onboard unit of each target vehicle sequentially adjusts its speed and acceleration, gradually approximating the planned trajectory. Once a target vehicle leaves the adjustment zone, its operating state is considered a new boundary condition, providing input for trajectory planning in the next time period. This cyclical update method enables dynamic trajectory optimization within the convoy, ensuring the convoy can continuously enter subsequent areas in a stable, smooth, and energy-efficient manner.

[0068] In this embodiment of the invention, a trajectory optimization model is used to achieve distributed control of the target fleet in the fleet adjustment area. The trajectory planning of the vehicles is carried out while taking into account both energy efficiency optimization and passenger comfort, so that the speed and acceleration curves of the vehicles are as smooth as possible, reducing unnecessary acceleration and deceleration and energy consumption fluctuations, while improving passenger comfort, thereby ensuring energy efficiency and continuous flow stability and avoiding common merging conflicts.

[0069] Step 103: In the fleet coordination control area, based on the fleet status information of each target fleet in the fleet formation, construct an optimization model for the merging sequence, and predict the merging sequence of the target fleets entering the fleet merging area and the target arrival time based on the optimization model.

[0070] After the convoy formation completes its adjustment in the adjustment zone, it will successively enter the convoy coordination control zone. The convoy coordination control zone will determine the merging order based on the speed, position, and size of each target convoy. Traditional merging often relies on a simple "first-come, first-served" rule, which can easily lead to excessive waiting for vehicles on ramps or excessive interference for vehicles on the main road. This embodiment of the invention constructs a convoy merging optimization function based on the convoy state information of each target convoy and uses a rolling time domain approach to calculate the merging sequence of the target convoys and the target arrival time to enter the convoy merging zone. Here, the control of each target convoy is equivalent to the control of a single vehicle, that is, the calculation of the merging order at the target convoy level is equivalent to the calculation of the merging order of the lead vehicle of the target convoy. The optimization objective of the convoy merging optimization function is to suppress maximum waiting and reduce overall delay within a finite prediction time domain, so as to achieve throughput fairness between target convoys on the main road and ramps.

[0071] like Figure 6As shown, after the convoy leaves the adjustment area and enters the cooperative control area, each convoy is treated as an equivalent single vehicle to calculate the merging order, which is done for the lead vehicle. First, the convoy merging status is collected using road test units deployed at the entrance of the cooperative control area, and the earliest arrival time of each convoy is calculated. Then, a convoy merging optimization function is constructed, with constraints including safe headway, no overtaking constraints, physical boundaries and road speed limits, and accessibility constraints. Under these constraints, the assigned arrival time is calculated using the convoy merging optimization function, thus implicitly determining the merging order. The assigned arrival time is then sent to the lead vehicle, generating and broadcasting the target speed trajectory. Finally, it is determined whether the re-optimization conditions are met. Triggering conditions can be fixed-interval triggers or event triggers. If not, the target convoy is controlled to move towards the merging area according to the trajectory; if so, the iteration continues, and based on time-domain optimization, the convoy merging status is collected again in the next prediction window, and the convoy merging optimization function is used for calculation.

[0072] In one possible implementation, an optimization model for the merging sequence is constructed based on the fleet status information of each target fleet in the fleet formation. This can be achieved in the following way, which will be explained in detail below.

[0073] A prediction window is established at the entrance of the convoy collaborative control area to determine the real-time status information of each target convoy entering the prediction window in the convoy formation. The real-time status information includes the vehicle status information of the lead vehicle in the target convoy.

[0074] Here, the merging order is calculated by setting a corresponding prediction time domain. The prediction time domain can be achieved by setting a prediction window at a preset time point. When the vehicle platoon enters the cooperative control area, the real-time status information of each target platoon entering the prediction window is obtained through the road test unit. This real-time status information includes the vehicle status information of the lead vehicle in the target platoon. In other words, in the prediction window, each target platoon only focuses on the operating status of the lead vehicle in the platoon, including the position and speed of the lead vehicle.

[0075] Furthermore, the earliest reachable time for the target convoy to reach the convoy rendezvous area is calculated based on real-time status information.

[0076] Here, based on position and speed, the earliest reachable time for the lead vehicle to arrive at the convoy rendezvous area, which is also the earliest time to leave the cooperative control area, can be calculated, denoted as t. min k,0 Here, it is assumed that the lead vehicle can accelerate at the maximum permissible acceleration and then travel at a constant speed at the maximum speed to leave the cooperative control area. Then, the shortest time to leave the cooperative control area is calculated based on the current position and speed.

[0077] Finally, a weighted optimization function is constructed based on the earliest reachable time and the assigned arrival time of the target convoy at the convoy rendezvous area, resulting in an optimization model for the merging sequence.

[0078] Here, the optimization model for the merging order is derived using an optimization subproblem, specifically a mixed-integer linear programming model. The optimization subproblem uses the assigned arrival time of the target convoy to the convoy rendezvous area as the optimization decision to construct a weighted optimization function. Here, the assigned arrival time, calculated by the weighted optimization function, is the designated time when the target convoy leaves the coordinated control area and enters the rendezvous area, denoted as {t}. assign k,i} k∈N , where N represents the target fleet set corresponding to the fleet group, and k represents the kth fleet.

[0079] The optimization objective of the weighted optimization function is to minimize the maximum interval between the assigned arrival times of target convoys and to minimize the overall delay time of convoy formation. Here, the maximum interval between the assigned arrival times of target convoys is the maximum time interval between target convoys leaving the coordinated control area. Minimizing this interval reduces the waiting time of target convoys and prevents individual target convoys from waiting excessively long. The overall delay time of convoy formation is the difference between the time when the first target convoy leaves the coordinated control area and the time when the last target convoy leaves the coordinated control area. Minimizing this difference reduces the time consumption of coordinated control and lowers the overall convoy formation delay.

[0080] The constraints of the weighted optimization function include: the time windows for the arrival times of the target convoy do not overlap, the speed limit and physical boundary of the road are not exceeded, there is no overtaking, the distance between vehicles does not exceed the prescribed safety distance, and the arrival time of the target convoy is not earlier than the earliest reachable time.

[0081] Here, the time windows for the assigned arrival times of the target convoys must not overlap. This means that two target convoys cannot have the same assigned arrival time; overlapping time windows would lead to unclear merging sequences and merging conflicts. The speed limits and physical boundaries of the roads must not be exceeded. This means that the target vehicles' speeds cannot exceed the speed limits on roads such as ramps and main roads. Physical boundaries of the roads mean that target vehicles cannot cross road boundaries, such as solid lines on the side of the road or crossing into other lanes. The distance between vehicles must not exceed the prescribed safe distance. This means that sufficient safe distance must be maintained between vehicles during travel. The assigned arrival time of the target convoy cannot be earlier than the earliest reachable time, which is an accessibility constraint, i.e., t. min k,0 >t assign k,iThis constraint is to ensure that the calculated arrival time is reasonable. The earliest arrival time is the earliest time when the target convoy leaves the cooperative control area. Exceeding this value is unreasonable.

[0082] Therefore, an optimization model for the merging sequence can be constructed based on the optimization objective and constraints of the weighted optimization function and the mixed-integer linear programming model. During model calculation, the real-time status information of the target convoy within each prediction window is input into the weighted optimization function. With the optimization objective as the direction of calculation, the assigned arrival time of each target convoy is calculated under the constraints. Based on the temporal order of these assigned arrival times, the merging sequence of each target vehicle entering the convoy rendezvous area can be determined. The assigned arrival time calculated here is the target arrival time of the target convoy entering the convoy rendezvous area, used for subsequent merging within the convoy rendezvous area. The merging sequence and target arrival time calculated by the optimization model are generally broadcast to other members of the target convoy by the onboard unit of the lead vehicle.

[0083] In addition, for target fleets that have been coordinated and controlled according to the assigned arrival time, the corresponding vehicle adjustments are made in the coordinated control area. For target fleets that have not yet reached the assigned arrival time, they continue to enter the next sliding window and are further optimized according to the real-time status information collected in the next sliding window to achieve closed loop.

[0084] This invention, through the construction of an optimized merging sequence model, rationally arranges the arrival times of each target vehicle convoy entering the merging area, thereby determining the merging sequence of each target vehicle. Under safe driving conditions, this ensures the merging process is both efficient and fair. Target vehicles do not need to frequently start and stop during this process; instead, they maintain a stable acceleration or deceleration trend and control their movement in an orderly manner, avoiding situations where vehicles compete for the right of way.

[0085] Step 104: In the convoy rendezvous area, control each target convoy in the convoy formation to complete the rendezvous in turn according to the merging order and the target arrival time.

[0086] Once each target convoy enters the merging area at its assigned arrival time, the final merging process is complete. Since the target convoys have already determined their merging order according to the optimization results and completed the necessary adjustments, target vehicles within the merging area no longer need to brake suddenly or force their way in. Instead, they merge naturally, like gears meshing, avoiding the disorderly competition of traditional merging. Vehicles from both the main road and the ramps can merge smoothly at a near-constant speed while maintaining a safe distance. The entire process achieves high-speed, low-energy, and highly comfortable continuous flow operation.

[0087] Even in the merging zone, it's still necessary to plan the trajectories of each target convoy. After the vehicles enter the merging zone, a convoy-level trajectory planning method is used. The target convoy is still considered as the planning object, but for that convoy, only the trajectory of the lead vehicle is optimized. The other convoy members simply maintain the same speed and acceleration as the lead vehicle through the convoy's internal following mechanism. This method significantly reduces computational and storage requirements, avoids the high cost of solving for each vehicle individually, and maintains stability and consistency within the convoy.

[0088] like Figure 7 As shown, when the lead vehicle of the target convoy enters the merging zone, each convoy is still treated as an equivalent single vehicle. The vehicle status information of the lead vehicle is obtained through the corresponding roadside unit, specifically including the entry time, current position, maximum available acceleration, and merging zone exit position, to determine the boundary conditions of the lead vehicle. A convoy merging optimization model is then constructed. This convoy merging optimization model is similar to the trajectory optimization model in the previous embodiment, also including multiple state optimization equations. The optimization objective of the model is to optimize the smoothness of speed and acceleration, with constraints including upper and lower speed limits, speed range, acceleration range, and minimum safe following distance. Finally, the convoy merging optimization model calculates and outputs the corresponding merging trajectory. The calculation result is sent to the lead vehicle, which then broadcasts it to each member vehicle until the convoy exits the merging zone, recording the state as a new initial condition. After outputting the corresponding merging trajectory, if external traffic changes, a rolling time-domain approach is used to re-optimize the convoy merging optimization model.

[0089] In one possible implementation, the merging of each target convoy in the convoy formation is controlled according to the merging order and the target arrival time. This can be achieved in the following way, which is explained in detail below.

[0090] Based on the merging order and the target arrival time, determine the earliest reachable time and entrance position of each target convoy in the convoy formation to enter the convoy rendezvous area.

[0091] Here, based on the merging order of each target convoy and the target arrival time, the earliest reachable time and entrance position of each target convoy entering the convoy rendezvous area can be calculated one by one.

[0092] The initial boundary conditions for the lead vehicle in the target vehicle are constructed based on the earliest reachable time and the entrance position, and the terminal boundary conditions are set based on the exit position of the target vehicle convoy leaving the convoy rendezvous area and the expected departure speed.

[0093] Here, the convoy merging optimization model still needs to determine the initial and terminal boundary conditions for planning. The speed entering the convoy merging area can be obtained through road test units and used together with the earliest reachable time and entrance position as the initial boundary conditions. The terminal boundary conditions are also preset, and here they are set based on the exit position of the target convoy leaving the convoy merging area and the expected exit speed, which can be used as the terminal boundary conditions.

[0094] Furthermore, a merging optimization function is constructed, which includes state optimization equations for vehicle position, vehicle speed, and vehicle acceleration. The state optimization equations are specifically expressed as follows: (12) (13) (14) Where, x k,0 (t), v k,0 (t), a k,0 (t) represents the merging position, merging speed, and merging acceleration that need to be planned, respectively. w3 and w4 are the preset weight parameters for vehicle energy consumption and ride comfort, respectively. If vehicle planning focuses more on vehicle energy consumption, then w3 is larger, and if it focuses more on ride comfort, then w4 is larger. Under equilibrium, both are taken as 0.5, b1 (i) b2 (i) b3 (i) b4 (i) These are parameters, which can be calculated by substituting the initial boundary conditions of the lead vehicle in the target vehicle.

[0095] Similar to the trajectory optimization model in the above embodiments, the optimization objective of the platoon merging optimization function is to minimize vehicle energy consumption and maximize ride comfort. The principle will not be elaborated here. Furthermore, similar to the trajectory optimization model in the above embodiments, ride comfort and vehicle vibration are negatively correlated. The calculation method for ride comfort can also be referred to, but here vehicle energy consumption is negatively correlated with the rate of change of vehicle acceleration. A smoothing coefficient is introduced to penalize drastic acceleration changes. Specifically, the product of the smoothing coefficient and the rate of change of vehicle acceleration is used as the vehicle energy consumption.

[0096] The constraints of the merging optimization function include: vehicle speed not exceeding the speed limit specified on the road, vehicle acceleration not exceeding the preset upper limit, and vehicle spacing not exceeding the specified safety distance.

[0097] Here, the speed limit stipulated by the road is the speed limit within the merging zone. The maximum acceleration of the vehicle is limited to prevent excessive acceleration, and the distance between vehicles does not exceed the prescribed safety distance, meaning that there must be sufficient safety distance between vehicles.

[0098] Next, the initial boundary conditions and terminal boundary conditions are input into the merging optimization function of the vehicle fleet. Under the optimization objective, the Pontryagin maximum algorithm is called to solve the merging optimization function of the vehicle fleet, and the merging position, merging speed and merging acceleration of each target vehicle when merging are obtained. The merging trajectory of the target vehicle in the merging area can be further determined.

[0099] Finally, based on the merging trajectory and merging sequence, the target convoys in the convoy formation are controlled to merge sequentially.

[0100] Here, the calculation method of the merging optimization function is similar to that of the trajectory optimization model in the above embodiment. Both are based on the Pontryagin maximum principle to solve the various state optimization equations. The calculation results are the planned merging velocity, merging acceleration, and merging trajectory, and the corresponding merging trajectory is generated. This calculation result is sent to the onboard unit of the lead vehicle and then broadcast to other member vehicles of the target convoy. When merging in the merging zone, each target vehicle completes the merging according to the determined merging sequence and merging trajectory.

[0101] During operation within the merging zone, if external traffic conditions change (e.g., fluctuations in main road traffic flow or adjustments to the merging point timing), the platoon merging optimization function is continuously invoked using a rolling time-domain approach to re-optimize the merging trajectory of the lead vehicle. Instructions are updated with minor corrections to avoid significant disturbances to the target platoon. Finally, once the target platoon exits the merging zone, its state is recorded as new initial conditions for use by downstream control areas.

[0102] In this embodiment of the invention, by constructing a merging optimization function in the merging zone, a trajectory planning mode is realized with the merging vehicle as the unit, the navigator vehicle being optimally controlled, and the queue members following and executing. Under the premise of ensuring safety and efficiency, energy consumption and computational burden are effectively reduced, and the operating performance of hybrid and electric vehicles in the merging zone is improved, providing the final execution guarantee for the entire continuous flow merging scheme.

[0103] In this embodiment of the invention, the merging process of a convoy is completed sequentially in the convoy formation zone, convoy adjustment zone, convoy coordination control zone, and convoy merging zone. Vehicle status information of the target vehicles is used to complete vehicle grouping in the convoy formation zone. In the adjustment zone, vehicle trajectories are coordinated to ensure orderly vehicle grouping. Finally, in the coordination control zone, an optimization model of the merging sequence is used to predict the merging order and arrival time, enabling the convoy groups to continuously and alternately merge in the convoy merging zone. Therefore, by setting up multiple levels of grouping, adjustment, and coordination control in different functional zones, the real-time control of vehicle grouping to complete the final merging effectively reduces energy consumption and improves road traffic efficiency while ensuring vehicle safety, overcoming the limitations of traditional vehicle merging methods that rely on yielding and queuing.

[0104] This invention also provides a hierarchical fleet merging control device for connected autonomous vehicles, such as... Figure 8 As shown, the hierarchical fleet merging control device for connected autonomous vehicles includes: a fleet formation module 801, a fleet adjustment module 802, a fleet coordination control module 803, and a fleet merging module 804. Specifically, the fleet formation module 801 is used to acquire vehicle status information of target vehicles entering the fleet formation area, and to group the target vehicles according to the vehicle status information to obtain multiple target fleets; The convoy adjustment module 802 is used to adjust the driving trajectory of the target vehicles in each target convoy when the target convoy enters the convoy adjustment area, and generate a convoy formation with a fixed formation direction. The fleet coordination control module 803 is used to construct an optimization model of the merging sequence in the fleet coordination control area based on the fleet status information of each target fleet in the fleet group, and predict the merging sequence of the target fleet entering the fleet merging area and the target arrival time based on the optimization model. The convoy merging module 804 is used to control each target convoy in the convoy formation to merge sequentially according to the merging order and the target arrival time in the convoy merging area.

[0105] In one possible implementation, the hierarchical fleet merging control device for connected autonomous vehicles may further include a functional area division module 805. The functional area division module 805 is used to divide the fleet merging control area into four consecutive functional areas according to a preset fixed distance. The functional areas include a fleet formation area, a fleet adjustment area, a fleet cooperative control area, and a vehicle merging area. Each functional area has a roadside unit at its entrance, which is used to collect vehicle status information of the target vehicle entering the functional area.

[0106] The hierarchical fleet merging control device for connected autonomous vehicles provided in the above embodiments can realize the technical solutions described in the embodiments of the hierarchical fleet merging control method for connected autonomous vehicles. The specific implementation principles of each module or unit can be found in the corresponding content in the embodiments of the hierarchical fleet merging control method for connected autonomous vehicles, and will not be repeated here.

[0107] like Figure 9 As shown, the present invention also provides an electronic device 900. The electronic device 900 includes a processor 901, a memory 902, and a display 903. Figure 9 Only some components of the electronic device 900 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0108] In some embodiments, memory 902 may be an internal storage unit of electronic device 900, such as a hard disk or memory of electronic device 900. In other embodiments, memory 902 may also be an external storage device of electronic device 900, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 900.

[0109] Furthermore, the memory 902 may include both internal storage units of the electronic device 900 and external storage devices. The memory 902 is used to store application software and various types of data installed on the electronic device 900.

[0110] In some embodiments, processor 901 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 902 or process data, such as the hierarchical fleet merging control method for connected autonomous vehicles in this invention.

[0111] In some embodiments, display 903 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 903 is used to display information from electronic device 900 and to display a visual user interface. Components 901-903 of electronic device 900 communicate with each other via a device bus.

[0112] In some embodiments of the present invention, when the processor 901 executes the merging control program in the memory 902, the following steps can be implemented: acquiring vehicle status information of target vehicles entering the convoy formation area, and grouping the target vehicles according to the vehicle status information to obtain multiple target convoys; when the target convoys enter the convoy adjustment area, adjusting the driving trajectory of the target vehicles in each target convoy to generate a convoy group with a fixed formation direction; in the convoy coordination control area, constructing an optimization model of the merging sequence according to the convoy status information of each target convoy in the convoy group, and predicting the merging sequence of the target convoys entering the convoy merging area and the target arrival time based on the optimization model; in the convoy merging area, controlling each target convoy in the convoy group to complete the merging sequentially according to the merging sequence and the target arrival time.

[0113] It should be understood that when the processor 901 executes the merging control program in the memory 902, in addition to the functions mentioned above, it can also perform other functions, as detailed in the description of the corresponding method embodiments above.

[0114] Furthermore, the embodiments of the present invention do not specifically limit the type of electronic device 900 mentioned. Electronic device 900 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, electronic device 900 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0115] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a hierarchical fleet merging control method for networked autonomous vehicles provided by the methods described above. The method includes: acquiring vehicle status information of target vehicles entering a fleet formation area, and grouping the target vehicles according to the vehicle status information to obtain multiple target fleets; when the target fleets enter a fleet adjustment area, adjusting the driving trajectories of the target vehicles in each target fleet to generate a fleet group with a fixed formation direction; in a fleet cooperative control area, constructing an optimization model for the merging sequence according to the fleet status information of each target fleet in the fleet group, and predicting the merging sequence and target arrival time of the target fleets entering the fleet merging area based on the optimization model; in the fleet merging area, controlling each target fleet in the fleet group to merge sequentially according to the merging sequence and the target arrival time.

[0116] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0117] The hierarchical fleet merging control method and device for networked autonomous vehicles provided by the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A hierarchical fleet merging control method for connected autonomous vehicles, characterized in that, include: Obtain the vehicle status information of the target vehicles entering the convoy formation area, and group the target vehicles according to the vehicle status information to obtain multiple target convoys; When the target convoy enters the convoy adjustment area, the driving trajectory of the target vehicles in each target convoy is adjusted to generate a convoy group with a fixed formation direction; In the fleet coordination control area, based on the fleet status information of each target fleet in the fleet formation, an optimization model for the merging sequence is constructed. Based on the optimization model, the merging sequence of the target fleets entering the fleet merging area and the target arrival time are predicted. In the convoy rendezvous area, each target convoy in the convoy formation is controlled to merge sequentially according to the merging order and the target arrival time. The step of grouping the target vehicles according to the vehicle status information to obtain multiple target convoys includes: For each target vehicle, the relative distance between the target vehicle and the vehicle in front is determined based on the vehicle status information; Under the maximum permissible acceleration, calculate the time and distance required for the target vehicle to accelerate to the maximum speed based on the current speed of the target train; When the required distance is not less than the relative distance, the catch-up time for the target vehicle to merge into the convoy of the vehicle in front is calculated based on the maximum allowable acceleration and the current speed. When the required distance is less than the relative distance, the catch-up time for the target vehicle to merge into the convoy of the vehicle in front is calculated based on the required time and the maximum vehicle speed. The efficiency score for the target vehicle to merge into the platoon of the preceding vehicle is calculated based on the catch-up time and the availability index of the platoon to which the preceding vehicle belongs. When the benefit score is positive, the target vehicle is grouped as a member vehicle and enters the convoy where the preceding vehicle is located. When the benefit score is negative, the target vehicle is grouped as a lead vehicle and formed into a newly established target convoy. The step of constructing an optimization model for merging order based on the fleet status information of each target fleet in the fleet formation includes: A prediction window is established at the entrance of the convoy collaborative control area to determine the real-time status information of each target convoy entering the prediction window in the convoy formation. The real-time status information includes the vehicle status information of the lead vehicle in the target convoy. Calculate the earliest reachable time of the target convoy to the convoy rendezvous area based on the real-time status information; A weighted optimization function is constructed based on the earliest reachable time and the assigned arrival time of the target convoy at the convoy rendezvous area to obtain an optimization model for the merging order; The optimization objective of the weighted optimization function is to minimize the maximum interval between the assigned arrival times of the target convoys and to minimize the overall delay time of the convoy formation. The constraints of the weighted optimization function include: the time windows of the assigned arrival times of the target convoys do not overlap, do not exceed the road speed limit and road physical boundary, there is no overtaking, the distance between vehicles does not exceed the prescribed safety distance, and the assigned arrival time of the target convoy is not earlier than the earliest reachable time.

2. The hierarchical fleet merging control method for networked autonomous vehicles according to claim 1, characterized in that, The method further includes: The control area for merging the convoy is divided into four consecutive functional areas according to a preset fixed distance. The functional areas include the convoy formation area, the convoy adjustment area, the convoy coordination control area, and the vehicle merging area. Each of the functional areas is equipped with a roadside unit at its entrance, which is used to collect vehicle status information of target vehicles entering the functional area.

3. The hierarchical fleet merging control method for networked autonomous vehicles according to claim 1, characterized in that, The step of adjusting the driving trajectory of the target vehicles in each of the target convoys to generate a convoy formation with a fixed formation direction includes: When each of the target convoys enters the convoy adjustment zone, the initial boundary conditions for each target vehicle in the target convoy are determined, including the entry time, current position, and maximum available catch-up time. The preset trajectory optimization model is invoked to perform trajectory planning on the initial boundary conditions, generating the planned speed, planned acceleration, and planned trajectory for each target vehicle; The trajectory of the target vehicles is controlled according to the planned speed and the planned acceleration, so that the target vehicles travel along the planned trajectory and form a convoy with a fixed formation direction.

4. The hierarchical fleet merging control method for networked autonomous vehicles according to claim 3, characterized in that, The step of calling a preset trajectory optimization model to perform trajectory planning on the initial boundary conditions, generating the planned speed, planned acceleration, and planned trajectory for each target vehicle, includes: Based on the initial boundary conditions, the weight parameters of each time state equation in the trajectory optimization model are calculated to obtain the state optimization equation. The state optimization equation includes equations for the vehicle position, vehicle speed, vehicle acceleration, and jitter value changing with time. The constraints of the state optimization equation include: the vehicle speed does not exceed the speed limit specified by the road, the vehicle acceleration does not exceed the preset acceleration limit, and the vehicle jitter value is within the preset jitter range. The initial vehicle state information and the final vehicle state information of each target vehicle are input into the state optimization equation. Under the condition of balancing vehicle energy consumption and ride comfort, the state optimization equation is solved by calling the Pontryagin maximum value to obtain the planned speed, planned acceleration and planned trajectory of each target vehicle. Among them, the ride comfort value is negatively correlated with the vehicle vibration value, and the vehicle energy consumption is positively correlated with the planned speed.

5. The hierarchical fleet merging control method for networked autonomous vehicles according to claim 1, characterized in that, The step of controlling each target convoy in the convoy formation to merge sequentially according to the merging order and the target arrival time includes: Based on the merging order and the target arrival time, determine the earliest reachable time and entrance position of each target convoy in the convoy formation to enter the convoy rendezvous area; The initial boundary conditions for the lead vehicle in the target vehicle are constructed based on the earliest reachable time and the entrance position, and the terminal boundary conditions are set based on the exit position of the target vehicle convoy leaving the convoy rendezvous area and the expected departure speed. A merging optimization function is constructed, which includes state optimization equations for vehicle position, vehicle speed, and vehicle acceleration. The optimization objective of the merging optimization function is to minimize vehicle energy consumption and maximize ride comfort. The constraints of the merging optimization function include: vehicle speed not exceeding the speed limit specified by the road, vehicle acceleration not exceeding a preset upper limit, and vehicle spacing not exceeding the specified safety distance. Among these constraints, ride comfort is negatively correlated with vehicle vibration, and vehicle energy consumption is negatively correlated with the rate of change of vehicle acceleration. The initial boundary conditions and terminal boundary conditions are input into the platoon merging optimization function, and under the optimization objective, the Pontryagin maximum is called to solve the platoon merging optimization function to obtain the merging trajectory of each target vehicle. Based on the merging trajectory and the merging sequence, the target convoys in the convoy formation are controlled to merge sequentially.

6. A hierarchical fleet merging control device for connected autonomous vehicles, characterized in that, include: The platoon formation module is used to acquire the vehicle status information of target vehicles entering the platoon formation area, and to group the target vehicles according to the vehicle status information to obtain multiple target platoons. The convoy adjustment module is used to adjust the driving trajectory of each target vehicle in the target convoy when the target convoy enters the convoy adjustment area, and generate a convoy formation with a fixed formation direction. The fleet coordination control module is used to construct an optimization model of the merging sequence in the fleet coordination control area based on the fleet status information of each target fleet in the fleet group, and predict the merging sequence of the target fleet entering the fleet merging area and the target arrival time based on the optimization model. The convoy merging module is used to control each target convoy in the convoy formation to merge sequentially according to the merging order and the target arrival time in the convoy merging area. The step of grouping the target vehicles according to the vehicle status information to obtain multiple target convoys includes: For each target vehicle, the relative distance between the target vehicle and the vehicle in front is determined based on the vehicle status information; Under the maximum permissible acceleration, calculate the time and distance required for the target vehicle to accelerate to the maximum speed based on the current speed of the target train; When the required distance is not less than the relative distance, the catch-up time for the target vehicle to merge into the convoy of the vehicle in front is calculated based on the maximum allowable acceleration and the current speed. When the required distance is less than the relative distance, the catch-up time for the target vehicle to merge into the convoy of the vehicle in front is calculated based on the required time and the maximum vehicle speed. The efficiency score for the target vehicle to merge into the platoon of the preceding vehicle is calculated based on the catch-up time and the availability index of the platoon to which the preceding vehicle belongs. When the benefit score is positive, the target vehicle is grouped as a member vehicle and enters the convoy where the preceding vehicle is located. When the benefit score is negative, the target vehicle is grouped as a lead vehicle and formed into a newly established target convoy. The step of constructing an optimization model for merging order based on the fleet status information of each target fleet in the fleet formation includes: A prediction window is established at the entrance of the convoy collaborative control area to determine the real-time status information of each target convoy entering the prediction window in the convoy formation. The real-time status information includes the vehicle status information of the lead vehicle in the target convoy. Calculate the earliest reachable time of the target convoy to the convoy rendezvous area based on the real-time status information; A weighted optimization function is constructed based on the earliest reachable time and the assigned arrival time of the target convoy at the convoy rendezvous area to obtain an optimization model for the merging order; The optimization objective of the weighted optimization function is to minimize the maximum interval between the assigned arrival times of the target convoys and to minimize the overall delay time of the convoy formation. The constraints of the weighted optimization function include: the time windows of the assigned arrival times of the target convoys do not overlap, do not exceed the road speed limit and road physical boundary, there is no overtaking, the distance between vehicles does not exceed the prescribed safety distance, and the assigned arrival time of the target convoy is not earlier than the earliest reachable time.

7. An electronic device, characterized in that, Including memory and processor, among which, The memory is used to store programs; The processor, coupled to the memory, is configured to execute the program stored in the memory to implement the steps of the hierarchical fleet merging control method for connected autonomous vehicles as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the hierarchical fleet merging control method for networked autonomous vehicles as described in any one of claims 1 to 5.

Images