A partitioned cooperative merging control method and system based on vehicle dynamic platoon

Abstract

The application provides a kind of partition cooperative confluence control method and system based on vehicle dynamic platoon, belongs to intelligent network connection and vehicle-road coordination technical field, and the collaborative operation of vehicle self-organizing area, lane changing and variable speed limit area and confluence adjustment area is realized by overall optimization.Vehicle platoon formation strategy and vehicle platoon driving strategy are formulated in vehicle self-organizing area, and vehicle platoon information is provided to subsequent area, whether lane changing is judged by lane changing probability threshold in lane changing and variable speed limit area, and variable speed limit control is carried out on vehicle platoon based on confluence gap, in confluence adjustment area, confluence sequence research and trajectory planning are carried out on main line and ramp vehicle based on dynamic programming.The effectiveness of the method is verified by simulation.

Description

Technical Field

[0001] This invention belongs to the field of intelligent connected vehicle and vehicle-road coordination technology, specifically relating to a zoned collaborative merging control method and system based on vehicle dynamic formation. Background Technology

[0002] Merging sections on highways are typical bottlenecks affecting traffic efficiency and safety. Existing highway vehicle platooning methods include adaptive platooning strategies, time-delay-based spacing strategies, hierarchical control based on gap acceptance, and distributed model predictive control. Most of these methods rely on fixed spacing or vehicle size for platooning. When ramp traffic surges, fixed platooning strategies cannot cope with sudden situations, potentially leading to traffic congestion. Current platooning-based methods such as lane-changing control, variable speed limit control, and merging control mostly focus on local optimization within the merging zone, neglecting coordination with other areas and ignoring the overall coordination between different zones.

[0003] Existing technologies mostly employ reinforcement learning-driven merging sequence optimization, which improves ramp merging efficiency, reduces traffic congestion, and enhances road capacity to some extent. However, they focus on trajectory planning during merging and do not fully consider the merging order. Different merging orders significantly impact the waiting and travel times of each vehicle, thus affecting key indicators such as the total travel time and fuel consumption of the entire platoon, leading to information gaps. When facing complex traffic flows, these technologies fail to fully consider the coordination and coupling of the structured platoons formed in the merging zone during merging, neglecting the platoon structure and traffic flow characteristics of lane changes and variable speed limit zone adjustments, making it difficult to ensure the safety of the merging process. Summary of the Invention

[0004] To address the shortcomings of existing technologies in coordinating vehicle formations in different areas, this invention provides a zoned collaborative merging control method and system based on dynamic vehicle formations.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A zone-based cooperative merging control method based on vehicle dynamic formation includes the following steps: The merging ramp sections of the expressway are divided into a self-organizing zone, a lane-changing and variable speed limit zone, and a merging adjustment zone; the vehicle status in the self-organizing zone includes main lane platoons and ramp platoons. In a connected environment, real-time fleet status information and traffic flow data are acquired. A formation optimization objective function is constructed based on time cost, fuel cost, and merging efficiency cost. The fleet status and traffic flow data are input into the formation optimization objective function to solve for the optimal values ​​of fleet size and intra-fleet spacing under the current traffic flow for vehicle formation. The fleet length is calculated based on the vehicle formation results. A lane-changing decision cost function is constructed based on lane-changing benefits. In the lane-changing and variable speed limit zones, the fleet status and fleet length are input into the lane-changing decision cost function for solution to obtain a lane-changing probability threshold. Based on the lane-changing probability threshold, a lane-changing triggering rule is set to determine whether the fleet should change lanes. If a lane change is made, the ramp fleet is virtually mapped to the main lane. With the goal of minimizing merging gap deviation and ensuring mainline traffic efficiency, a variable speed limit cost function is constructed. Model predictive control (MPC) is used to solve the variable speed limit cost function to obtain the optimal merging gap. The fleet speed is then adjusted based on the optimal merging gap. With the goal of maximizing the allocated time for the convoy to pass through the merging adjustment zone, a mixed-integer linear programming model is constructed. Based on the adjusted convoy speed, a dynamic programming optimization algorithm is used to solve the mixed-integer linear programming model to obtain the optimal control sequence of vehicles within the merging adjustment zone.

[0006] Preferably, the formation optimization objective function is: ; in, This is the adjustment coefficient; The optimal fleet size threshold; This is the intra-team spacing threshold; This represents an increase in time cost; For fuel consumption costs; For merging efficiency and cost; The time cost increment is: ; in, Let m be the total time taken for the m-th convoy to pass through the controlled area. Let M be the longitudinal position difference between the lead and last cars in the convoy, and M be the total number of cars in the convoy. Let m be the speed of the m-th convoy; The fuel consumption cost is: ; in, The start and end times for the formation of a vehicle convoy. , and This is the adjustment coefficient; The merging efficiency cost is: ; Among them, Q( h) represents the merging success rate.

[0007] Preferably, when forming a platoon, a platooning distance is set. If the distance between two vehicles in this lane or an adjacent lane is less than or equal to the platooning distance. And the number of vehicles in the current fleet is less than or equal to the optimal fleet size threshold. If it is found, it will be included in the formation and determined based on the intra-formation spacing threshold. Adjust the spacing within the convoy.

[0008] Preferably, the platoon length is calculated based on the vehicle platooning results, specifically as follows: ; Where L is the length of the vehicle.

[0009] Preferably, the lane-changing decision cost function is as follows: ; Where Z is the decision vector; and These are cost-benefit, incentive-benefit, and safety-benefit, respectively.

[0010] Preferably, the variable speed limit cost function is specifically: ; in, The total number of teams. The length difference between the convoy in main lane 2 and the convoy on the ramp; The longitudinal position difference between the two convoys in front and behind in main lane 2. The average speed of all participating merging teams. The time interval for main lane 2, The speed of main lane 2; For the time interval of the ramp, For the speed of the ramp, This is the optimal merging gap.

[0011] Preferably, the lane-change triggering rule specifically stipulates that a lane change is only performed when the probability of a successful lane change is greater than or equal to the lane-change probability threshold of the corresponding team; the probability of a successful lane change is specifically: ; in, This represents the probability of success in a single trial. This refers to the number of lane change tests.

[0012] The present invention also provides a zoned cooperative merging control system based on vehicle dynamic formation, specifically including: An initialization module is used to divide the merging ramp sections of the highway into a self-organizing zone, a lane-changing and variable speed limit zone, and a merging adjustment zone; the self-organizing zone includes main lane convoys and ramp convoys.

[0013] The platooning module is used to acquire real-time fleet status information and traffic flow data in a connected environment. It constructs a platooning optimization objective function based on time cost, fuel cost, and merging efficiency cost. The fleet status and traffic flow data are input into the platooning optimization objective function to solve for the optimal values ​​of fleet size and intra-fleet spacing under the current traffic flow for vehicle platooning. The fleet length is calculated based on the vehicle platooning results.

[0014] The judgment module is used to construct a lane-changing decision cost function based on lane-changing benefits. In the lane-changing and variable speed limit zones, the convoy status and convoy length are input into the lane-changing decision cost function for solution to obtain a lane-changing probability threshold. Based on the lane-changing probability threshold, a lane-changing triggering rule is set to determine whether the convoy should change lanes. If a lane change is made, the ramp convoy is virtually mapped to the main lane. With the goal of minimizing merging gap deviation and ensuring mainline traffic efficiency, a variable speed limit cost function is constructed. Model predictive control (MPC) is used to solve the variable speed limit cost function to obtain the optimal merging gap. The convoy speed is then adjusted based on the optimal merging gap.

[0015] The dynamic control module is used to construct a mixed-integer linear programming model with the goal of maximizing the allocation time for the convoy to pass through the merging adjustment zone. Based on the adjusted convoy speed, the dynamic programming optimization algorithm is used to solve the mixed-integer linear programming model to obtain the optimal control sequence of vehicles within the merging adjustment zone.

[0016] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps described in the partitioned cooperative merging control method based on vehicle dynamic formation.

[0017] The present invention also provides a computer-readable storage medium storing a computer program that, when loaded by a processor, can execute the steps described in the partitioned cooperative merging control method based on vehicle dynamic formation.

[0018] The partitioned cooperative merging control method based on vehicle dynamic formation provided by this invention has the following beneficial effects: This invention constructs a formation optimization objective function through vehicle self-organizing zone formation, dynamically solving for the optimal fleet size threshold and fleet spacing threshold. Dynamic spacing adjustment reduces vehicle acceleration and deceleration frequency, resulting in reduced overall fleet fuel consumption and improved mainline traffic efficiency in actual tests. The formation optimization objective function is constructed based on fleet state information, fully considering traffic flow characteristics. In lane-changing and variable speed-limited zones, a lane-changing decision cost function based on a lane-changing probability threshold enables dynamic lane-changing decisions, allowing for early intervention in mainline lane-changing and speed-limit control. This avoids concentrated congestion in merging zones, ensuring smooth entry of mainline traffic into merging zones and reducing the risk of merging collisions. Through merging sequence planning and trajectory optimization, it solves the problems of high delays and trajectory conflicts caused by ignoring merging sequence in traditional methods, improving traffic efficiency and demonstrating significant advantages in reducing average vehicle delay and increasing average vehicle speed. Attached Figure Description

[0019] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The 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.

[0020] Figure 1 This is a framework design diagram of a zoned cooperative merging control method based on vehicle dynamic formation according to an embodiment of the present invention.

[0021] Figure 2 This is a comparison chart of fuel costs for different methods under different traffic flows in the embodiments of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0023] Example The zone-based cooperative merging strategy based on dynamic formation in a connected vehicle environment comprises three parts: vehicle self-organizing zone formation strategy, mainline early lane changing and variable speed limit strategy, and fleet-based dynamic merging strategy. The control algorithm framework of this method is as follows: Figure 1 As shown.

[0024] This invention provides a zoned cooperative merging control method based on vehicle dynamic formation, specifically including the following steps: Step 1: Establish a platooning model. By establishing a comprehensive economic benefit model, the optimal platoon size and headway within the platoon are determined at the current moment for vehicle platooning. Adjusting the platooning based on changes in traffic flow on the main road and ramps helps improve traffic efficiency, and a platooning driving strategy is formulated to ensure safe platooning operation.

[0025] Vehicles on the main lane and vehicles on the ramp each form a platoon, transitioning from a single-vehicle state to a platoon state. A comprehensive platooning benefit optimization model, i.e., a cost function, is established. Time, fuel, and merging efficiency costs are used as benefit metrics, and then the model constraints are determined by combining three types of eigenvalues.

[0026] Define formation distance If the distance between two vehicles in this lane or an adjacent lane is less than or equal to At that time, that is: And the number of vehicles in the current fleet is less than or equal to the fleet size threshold. If it is found, it will be included in the formation and determined based on the intra-formation spacing threshold. Adjust the spacing within the convoy.

[0027] During platooning, the acceleration or deceleration of vehicles will affect time, with the increment in time cost being: ; in, Let m be the total time taken for the m-th convoy to pass through the controlled area. Let M be the longitudinal position difference between the lead and last cars in the convoy, and M be the total number of cars in the convoy. Let m be the speed of the m-th convoy.

[0028] During platooning, improper spacing between vehicles or unstable platooning speeds will lead to additional fuel consumption. Fuel costs are:

[0029] ; in, The start and end times for the formation of a vehicle convoy. , and This is the adjustment coefficient.

[0030] Improper platooning can lead to uneven merging of vehicles on ramps and mainline vehicles, causing traffic congestion and reducing merging efficiency. The cost of merging efficiency is:

[0031] ; Define Q( h) represents the merging success rate: ; in, Where L is the vehicle arrival rate and L is the vehicle length. The maximum speed of vehicles in the lane. To increase the vehicle's travel distance.

[0032] Taking into account time cost, fuel cost, and merging efficiency cost, the following cost function can be constructed: ; in, This is the adjustment coefficient. The optimal fleet size threshold for the current time can be obtained by solving the cost function. and intra-team spacing threshold .

[0033] Step Two: Fleet Driving Strategy. After the convoy is formed, the convoy following strategy and lane-changing strategy need to be revised. The lead car following model in the convoy is as follows:

[0034] ; ; in, To track the position of the following vehicles. To keep up with the speed of the vehicle, and For adjustment coefficients, This represents the error between the actual distance and the expected distance. To determine the minimum safe headway, n-1 represents the vehicle in front. The speed of the vehicle at the moment before.

[0035] The other vehicles in the convoy follow the vehicle in front, and their acceleration is: ; in, Representing the m-th team, For the distance between vehicles in front and behind, , Let n be the speed of the nth car in the convoy. , The speed and acceleration of the vehicle in front, This is the adjustment coefficient.

[0036] When changing lanes, the convoy must maintain a safe following distance from vehicles in the adjacent lane and a safe following distance from the vehicle in front. The required safe following distance for the m-th convoy is as follows. and safe following distance for:

[0037] ; in, This is the speed of the lead vehicle in the convoy. When changing lanes, the minimum safe following distance from the vehicle in the adjacent lane is... .

[0038] convoys must maintain a safe distance to avoid collisions: ; in, Let m be the position of convoy m on lane i at time k.

[0039] The length of the formed convoy is defined as: .

[0040] Step 3: Mainline Early Lane Change and Variable Speed ​​Limit Strategy: A dynamic lane change control strategy based on a lane change probability threshold is proposed. The lane change probability threshold obtained by solving the lane change decision model determines whether a convoy should change lanes, i.e., whether a convoy in main lane 2 should change lanes to main lane 1. Based on the lane change implementation, a variable speed limit strategy is adopted to solve the merging gap using MPC (Multi-Purpose Calculation). The ramp convoy is virtually mapped to the main lane, the merging gap is solved, and variable speed limit control is applied to mainline convoys with conflict risk.

[0041] (1) Lane-changing decision optimization model. The cost function of the lane-changing decision optimization model measures and quantifies the benefits of lane changing from three aspects: cost-benefit, incentive benefit, and safety benefit, and then determines whether to change lanes. The cost function is defined as:

[0042] ; in, Z is the lane-changing benefit function; Z is the decision vector. A value of 0 indicates the vehicle will not change lanes, while a value of 1 indicates it will be selected as a vehicle to change lanes. Solve. The minimum value is obtained from the optimal value in the formula. . and These are cost-benefit, incentive-benefit, and safety-benefit, respectively.

[0043] Cost-benefit analysis: Standard deviation reflects the dispersion of a dataset. The standard deviations of the output acceleration and velocity datasets are calculated as shown in the following equations: ; ; in, and Let T represent the acceleration and speed fluctuation of the lead car in the m-th convoy before and after changing lanes, and T be the prediction time. To accelerate the convoy.

[0044] The difference between the overall acceleration and total speed fluctuation of a vehicle before and after lane changing is used as the cost-benefit ratio of the vehicle: ; in, and This represents the fluctuation value resulting from the decision of a smart connected vehicle fleet to change lanes.

[0045] Incentive benefits, to represent the improvement in local traffic flow speed caused by the lane-changing behavior of intelligent connected vehicles, are specifically defined as follows: ; in, and Let be the speed value of the m-th convoy at time k, indicating whether it changes lanes.

[0046] Safety benefits, specifically Time to Collision (TTC), refers to the time required for two consecutive vehicles occupying the same lane to collide if the following vehicle is traveling faster than the preceding vehicle and continuing at their current speeds. This study primarily focuses on controlling the leading vehicle; therefore, TTC was chosen as the safety benefit evaluation indicator.

[0047] ; in, For the length of the convoy, (k)= , and Let be the longitudinal position and speed of the preceding convoy at time k.

[0048] ; ; ; in, and Collision time before and after lane change For safety and benefit.

[0049] The lane-changing process of a vehicle is abstracted into the probability of success in a single trial. The binomial distribution problem. Assume there exists The number of lane change attempts, and the success rate of a single attempt are: The probability of a vehicle successfully changing lanes is... In the formula This represents the number of tests generated during the lane change process. Converted to the probability of success in a single trial Number of trials Solve for it.

[0050] Lane change sign definition: ; Because in the quadratic programming problem, For values ​​that are continuous between 0 and 1, integer programming is used, and the definition is... A lane-changing probability threshold is set for each team. The lane-changing probability of all teams is then assessed. When the lane-changing probability is greater than or equal to... Lane changes are only performed when the lane change probability threshold of the corresponding team is met.

[0051] (2) Variable speed limit control. Most current research on variable speed limits involves uniformly adjusting the speed of all vehicles, which may reduce road capacity. Therefore, a variable speed limit control strategy based on merging gap is proposed. The merging gap is adjusted based on the information of the mainline and ramp vehicle convoys to dynamically adjust the speed of the mainline vehicle convoy, thereby ensuring the safe merging of the ramp vehicle convoy.

[0052] Vehicle longitudinal dynamics model. Establish the vehicle's longitudinal kinematic model:

[0053] ; ; in, , representing the vehicle's position and velocity at time k, are the vehicle's state parameters; This represents the vehicle's acceleration at time k, and is a control input parameter for the vehicle. , where is the position and velocity output by the vehicle at time k; k is the discrete time step; Sampling time.

[0054] Let a replace , replace Then the discrete state-space equations are transformed into: ; Define the output equation to output the state variable deviation at time k. for: ; The state variable deviation and control variable deviation are integrated and constructed. New state quantity at every moment for: ; Therefore, the new state quantity at time k+1 is derived. for: ; in, For discretization The increment of the vehicle control deviation at any given time; due to the generation of a new state space, we can obtain New output equation at every moment for: ; in, To predict the time domain, To control the time domain, the prediction process for the state and output variables in the prediction time domain can be derived as described above. To achieve the prediction function in the prediction model, the state variables and control increments at the current time step need to be iterated continuously. Furthermore, the objective function is used to solve for the system's control increments.

[0055] The optimal merging gap is solved based on MPC. Since the vehicle selection space is very small in continuous traffic flow, the insertion position selected by the vehicle may be inaccessible due to the restrictions of the vehicles in front and behind. Therefore, before the variable speed limit, the current position of the ramp convoy is projected onto the position of the main lane as the insertion position, and the nearest convoy in front and behind on the main lane is determined based on the projected position. Defined as the optimal merging gap. If the variable speed limit is described as a combinatorial optimization problem, then the controller... Variable speed limit cost function:

[0056] ; in, The total number of teams. The length difference between the convoy in main lane 2 and the convoy on the ramp; The longitudinal position difference between the two convoys in front and behind in main lane 2. The average speed of all participating merging teams. , and The longitudinal positions of the front and rear convoys in main lane 2. , , , , and Main lane 2 and the first on the ramp convoy length, time interval, and speed.

[0057] The MPC controller is described as an optimal control problem, and the optimal merging gap for the mainline convoy is obtained by solving the problem. To regulate the speed of the convoy Implement variable speed control.

[0058] (3) Dynamic merging strategy based on fleet: A dynamic merging strategy based on fleet is proposed. A mixed integer linear programming model is constructed, and a dynamic programming optimization algorithm is used to solve the merging order of the fleet. Finally, the optimal control theory is used to optimize the merging trajectory of the fleet.

[0059] To model the merging order problem, in order to determine the merging order, we first construct the objective function: Introducing a series of binary variables , The problem of the order of co-current flow can be expressed as a mixed-integer linear programming problem, as shown in the following equation: ; : ; ; ; ; ; in, It is a sufficiently large positive number. The merging order matrix, For the team At the current speed The time taken to pass through the merging zone, i.e. the merging moment, should be greater than the minimum arrival time and less than the maximum arrival time to prevent the allocation of unreasonable arrival times to vehicles. At the same time, to avoid collisions between vehicles, the time taken for two consecutive vehicles to pass through the merging point must be greater than a certain interval. Indicates vehicle Later than the vehicle Through the merging zone, Indicates vehicle Prior to vehicles Through the merging zone.

[0060] Merging sequence planning based on dynamic programming, defined Indicates starting from the initial state to state The maximum merging time allocated to merging teams in the middle. For lane 2 convoy and ramp convoy in Merging sequence at time, defining state The criterion function is: ; Terminal state set The optimal solution is the one that minimizes the criterion function among all the included states. Starting from the optimal terminal state, the optimal path sequence is used... Backtrack to the initial state This allows us to obtain the decision variable values ​​for each step, thereby obtaining the optimal merging order of the convoy within the cooperative area.

[0061] Vehicle trajectory optimization control based on optimal control, specifically merging optimization control, aims to coordinate the safe and efficient passage of convoys from both the main lane 2 and the ramp through the merging zone. The objective function is established based on the convoy kinematic model:

[0062] ; in, For the team The initial running time. The goal of this optimal control problem is to find the optimal control input. This refers to the acceleration of the convoy, thereby reducing the convoy's energy consumption and, to some extent, ensuring the comfort of the convoy's driving. The Hamiltonian function is constructed as follows:

[0063] ; in, , It's a team Costate variables; It's a team The position and velocity at time k. According to the Pontryagin's minimum principle, if... This is the optimal solution that minimizes the above expression. , For the corresponding optimal trajectory line, then The function relative to time is:

[0064] ; The formula Substituting into the vehicle kinematics model, the position can be obtained. , The optimal trajectory: ; ; in, The integral constant can be obtained from the initial state of the vehicle. , and target state , calculate; , It is the vehicle at the current moment. Position and velocity; It is the location of the merging point; , is the specified merging speed; This is the allocated time to reach the merging point. Combining the above three formulas, we get the following formula:

[0065] ; Solving the equations yields the results for each vehicle. The numerical solution yields the optimal control sequence for vehicles within the merging adjustment zone. .

[0066] The above-mentioned coordinated control method was simulated using MATLAB software. To verify the model's effectiveness during ramp merging, the impact of four different control schemes on ramp merging was considered under traffic flow conditions of 800, 1000, and 1200 veh / h on ramps and 2400, 2800, 3200, and 3600 veh / h on dual main lanes. Each scheme had a running time of 450 s. The four schemes were: 1) No control (NC); 2) Lane change decision and first-in-first-out (LC-FIFO) merging; 3) Fixed-threshold formation-based zone cooperative control (FT-PZCC); and 4) Dynamic formation-based zone cooperative control (DP-ZCC).

[0067] The effectiveness of the strategy is demonstrated by average delay time and average speed. Simulation results under different traffic flow conditions are shown in Table 1. The values ​​in parentheses in Table 1 represent the rate of decrease in average delay time and the rate of increase in average speed of the current strategy compared to the NC strategy under the same traffic flow conditions. Delay time is calculated as the difference between the actual travel time of each vehicle and the free-flow travel time. For each scenario, the delay value is measured from the merging adjustment zone. The average speed is the average speed of the simulated vehicles within the total control zone. The results show that under different traffic demand levels, the performance of DP-ZCC is consistently better than NC, LC-FIFO, and FT-PZCC, indicating that this strategy can effectively improve the capacity of the merging zone.

[0068] Table 1. Comparison of average vehicle delay time and average speed under different control schemes and traffic flows. The values ​​in parentheses represent the rate of decrease in average delay time and the rate of increase in average speed of the current strategy relative to case 1 under the same traffic conditions.

[0069] As the mainline traffic flow increases, when the ramp flow is between 800veh / h and 1200veh / h, compared with NC, the LC-FIFO scheme can reduce the average vehicle delay by 16.08%, 5.03%, and 8.2%, and increase the average speed by 4.7%, 4.85%, and 8.0%; the FT-PZCC scheme can reduce the average vehicle delay by 13.98%, 13.8%, and 16.48%, and increase the average speed by 8.85%, 13.38%, and 14.28%; and the DP-ZCC scheme can reduce the average vehicle delay by 22.48%, 24.8%, and 27.7%, and increase the average speed by 20.95%, 24.15%, and 24.45%.

[0070] Data shows that DP-ZCC has significant advantages in reducing average vehicle delay and increasing average vehicle speed. Secondly, as shown in Table 1, with the gradual increase in traffic flow in both main lanes, the increase in average delay time for DP-ZCC is slower compared to NC, LC-FIFO, and FT-PZCC, indicating that the DP-ZCC control scheme is more stable.

[0071] The fuel cost calculation formula for connected vehicles is the same as that for regular gasoline vehicles, primarily based on fuel consumption rate and fuel prices. For example... Figure 2 The figure shows a comparison of fuel costs for NC, LC-FIFO, FT-PZCC, and DP-ZCC schemes when traffic flow is 2400, 2800, and 3200 veh / h. It can be observed that the fuel cost of the DP-ZCC scheme is significantly lower than that of the other three schemes. This is because, under the condition of traveling the same distance, the method proposed in this paper can make the convoy passage smoother and the time for vehicles to stop and wait for merging on the ramp is shorter, thereby reducing fuel consumption loss.

[0072] Therefore, compared with NC, LC-FIFO and FT-PZCC methods, the control strategy proposed in this invention has improved vehicle average speed and average delay time to varying degrees, reduced merging delay time and fuel costs, and increased the average speed of the fleet and lane flow.

[0073] The present invention also provides a zoned cooperative merging control system based on vehicle dynamic formation, comprising: The initialization module is used to divide the merging ramp sections of the highway into a self-organizing zone, a lane-changing and variable speed limit zone, and a merging adjustment zone; the self-organizing zone includes main lane convoys and ramp convoys.

[0074] The platooning module is used to acquire real-time fleet status information and traffic flow data in a connected environment. It constructs a platooning optimization objective function based on time cost, fuel cost, and merging efficiency cost. The fleet status and traffic flow data are input into the platooning optimization objective function to solve for the optimal values ​​of fleet size and intra-fleet spacing under the current traffic flow for vehicle platooning. The fleet length is calculated based on the vehicle platooning results.

[0075] The judgment module is used to construct a lane-changing decision cost function based on lane-changing benefits. In lane-changing and variable speed limit zones, the vehicle fleet status and fleet length are input into the lane-changing decision cost function for solution to obtain a lane-changing probability threshold. Based on the lane-changing probability threshold, lane-changing trigger rules are set to determine whether the vehicle fleet should change lanes. If a lane change is made, the ramp vehicle fleet is virtually mapped to the main lane. With the goal of minimizing merging gap deviation and ensuring mainline traffic efficiency, a variable speed limit cost function is constructed. Model predictive control (MPC) is used to solve the variable speed limit cost function to obtain the optimal merging gap. The vehicle fleet speed is then adjusted based on the optimal merging gap.

[0076] The dynamic control module is used to construct a mixed-integer linear programming model with the goal of maximizing the allocated time for the convoy to pass through the merging adjustment zone. Based on the adjusted convoy speed, the dynamic programming optimization algorithm is used to solve the mixed-integer linear programming model to obtain the optimal control sequence of vehicles within the merging adjustment zone.

[0077] The modules in the aforementioned vehicle dynamic formation-based zoned cooperative merging control system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0078] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps in an embodiment of a vehicle dynamic formation-based zoned cooperative merging control method. Specific implementation methods can be found in the method embodiments, and will not be repeated here.

[0079] Furthermore, the present invention also provides a non-transitory computer-readable storage medium containing instructions on which a computer program is stored. For example, a memory containing instructions that can be executed by a processor of a computer device to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc. When the computer program is executed by the processor, it can implement the steps in an embodiment of a vehicle dynamic formation-based partitioned cooperative merging control method. Specific implementation methods can be found in the method embodiments, which will not be repeated here.

[0080] Those skilled in the art will understand that embodiments of the present invention can provide methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0082] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0083] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0084] It should be noted that the specific embodiments described above enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail in this specification and embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered within the protection scope of the present invention patent. No reference numerals in the claims should be construed as limiting the scope of the claims. Any simple variations or equivalent substitutions of technical solutions that can be readily obtained by those skilled in the art within the scope of the technology disclosed in the present invention are within the protection scope of the present invention.

Claims

1. A zoned cooperative merging control method based on vehicle dynamic formation, characterized in that, Includes the following steps: The merging ramp sections of the expressway are divided into a self-organizing zone, a lane-changing and variable speed limit zone, and a merging adjustment zone; the vehicle status in the self-organizing zone includes main lane platoons and ramp platoons. In a connected environment, real-time fleet status information and traffic flow data are acquired. A formation optimization objective function is constructed based on time cost, fuel cost, and merging efficiency cost. The fleet status and traffic flow data are input into the formation optimization objective function to solve for the optimal values ​​of fleet size and intra-fleet spacing under the current traffic flow for vehicle formation. The fleet length is calculated based on the vehicle formation results. Based on lane-changing benefits, a lane-changing decision cost function is constructed. In the lane-changing and variable speed limit zones, the fleet state and fleet length are input into the lane-changing decision cost function for solution to obtain the lane-changing probability threshold. Based on the lane change probability threshold, a lane change triggering rule is set to determine whether the convoy changes lanes; If changing lanes, the ramp convoy is virtually mapped to the main lane. With the goal of minimizing merging gap deviation and ensuring mainline traffic efficiency, a variable speed limit cost function is constructed. The variable speed limit cost function is solved using model predictive control (MPC) to obtain the optimal merging gap. The convoy speed is then adjusted based on the optimal merging gap. With the goal of maximizing the allocated time for the convoy to pass through the merging adjustment zone, a mixed-integer linear programming model is constructed. Based on the adjusted convoy speed, a dynamic programming optimization algorithm is used to solve the mixed-integer linear programming model to obtain the optimal control sequence of vehicles within the merging adjustment zone.

2. The method for zoned cooperative merging control based on vehicle dynamic formation according to claim 1, characterized in that, The specific objective function for formation optimization is: ； in, This is the adjustment coefficient; The optimal fleet size threshold; This is the intra-team spacing threshold; This represents an increase in time cost; For fuel consumption costs; For merging efficiency and cost; The time cost increment is: ； in, Let m be the total time taken for the m-th convoy to pass through the controlled area. Let M be the longitudinal position difference between the lead and last cars in the convoy, and M be the total number of cars in the convoy. Let m be the speed of the m-th convoy; The fuel consumption cost is: ； in, The start and end times for the formation of a vehicle convoy. , and This is the adjustment coefficient; The merging efficiency cost is: ； Among them, Q( h) represents the merging success rate.

3. The method for zoned cooperative merging control based on vehicle dynamic formation according to claim 2, characterized in that, When forming a vehicle platoon, set the platooning distance. If the distance between two vehicles in this lane or an adjacent lane is less than or equal to the platooning distance. And the number of vehicles in the current fleet is less than or equal to the optimal fleet size threshold. If it is found, it will be included in the formation and determined based on the intra-formation spacing threshold. Adjust the spacing within the convoy.

4. The partitioned cooperative merging control method based on vehicle dynamic formation according to claim 3, characterized in that, The platoon length is calculated based on the vehicle formation results, specifically: ； Where L is the length of the vehicle.

5. The method for zoned cooperative merging control based on vehicle dynamic formation according to claim 1, characterized in that, The lane-changing decision cost function is specifically as follows: ； Where Z is the decision vector; and These are cost-benefit, incentive-benefit, and safety-benefit, respectively.

6. The method for zoned cooperative merging control based on vehicle dynamic formation according to claim 1, characterized in that, The variable speed limit cost function is specifically as follows: ； in, The total number of teams. The length difference between the convoy in main lane 2 and the convoy on the ramp; The longitudinal position difference between the two convoys in front and behind in main lane 2. The average speed of all participating merging teams. The time interval for main lane 2, The speed of main lane 2; For the time interval of the ramp, For the speed of the ramp, This is the optimal merging gap.

7. The method for zoned cooperative merging control based on vehicle dynamic formation according to claim 1, characterized in that, The lane change triggering rule specifically states that a lane change is only performed when the probability of a successful lane change is greater than or equal to the corresponding team's lane change probability threshold; the specific probability of a successful lane change is: ； in, This represents the probability of success in a single trial. This refers to the number of lane change tests.

8. A zoned cooperative merging control system based on vehicle dynamic formation, characterized in that, include: An initialization module is used to divide the merging ramp sections of the highway into a self-organizing zone, a lane-changing and variable speed limit zone, and a merging adjustment zone; the self-organizing zone includes main lane convoys and ramp convoys. The platooning module is used to acquire real-time fleet status information and traffic flow data in a connected environment. It constructs a platooning optimization objective function based on time cost, fuel cost, and merging efficiency cost. The fleet status and traffic flow data are input into the platooning optimization objective function to solve for the optimal values ​​of fleet size and intra-fleet spacing under the current traffic flow for vehicle platooning. The platoon length is calculated based on the vehicle platooning results; The judgment module is used to construct a lane-changing decision cost function based on lane-changing benefits. In the lane-changing and variable speed limit zones, the fleet status and fleet length are input into the lane-changing decision cost function for solution to obtain the lane-changing probability threshold. Based on the lane change probability threshold, a lane change triggering rule is set to determine whether the convoy changes lanes; If changing lanes, the ramp convoy is virtually mapped to the main lane. With the goal of minimizing merging gap deviation and ensuring mainline traffic efficiency, a variable speed limit cost function is constructed. The variable speed limit cost function is solved using model predictive control (MPC) to obtain the optimal merging gap. The convoy speed is then adjusted based on the optimal merging gap. The dynamic control module is used to construct a mixed-integer linear programming model with the goal of maximizing the allocation time for the convoy to pass through the merging adjustment zone. Based on the adjusted convoy speed, the dynamic programming optimization algorithm is used to solve the mixed-integer linear programming model to obtain the optimal control sequence of vehicles within the merging adjustment zone.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is loaded by the processor, it is able to perform the steps of the method according to any one of claims 1 to 7.

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