An intelligent networked queue merging control method based on cluster theory

Abstract

The present invention discloses an intelligent connected vehicle queue merging control method based on swarm theory. The method includes: modeling the system dynamic equation, where the variables in the equation include the longitudinal and lateral positions, speed, and control input of the vehicle; at the moment when the intelligent connected vehicle group enters the cross-section of the merging area, establishing the network topology between the leading vehicles of the intelligent connected vehicle queues according to the merging order of the intelligent connected vehicle queues; determining the ideal control spacing according to the network topology of the intelligent connected vehicle queues and the length of the intelligent connected vehicle queues; designing the intelligent connected vehicle group control protocol according to the ideal control spacing; and designing the intelligent connected vehicle queue control protocol. The method provided by the present invention comprehensively considers the applicability of lateral control in the merging scenario, and the proposed artificial function can better adapt to different merging scenarios, thereby realizing queue and group control and providing guarantee for road traffic safety.

Description

Technical Field

[0001] The present invention relates to the field of intelligent traffic control, and particularly to an intelligent connected queue merging control method based on swarm theory. Background Art

[0002] Traffic congestion, safety, and pollution are key quality-of-life issues. Emerging technologies provide innovative opportunities to address these problems. Information and communication technologies, especially vehicle-to-vehicle communication, have attracted wide attention in the transportation field. In this context, vehicles sharing some common features (e.g., destination, partially overlapping paths) can cooperate to form queues by leveraging V2V communication. Queue-based driving mode refers to a series of vehicles traveling together at a coordinated speed and a pre-specified inter-vehicle distance. The benefits of queue-based cooperative driving mode include increased road throughput, alleviated traffic congestion, reduced energy consumption, and exhaust emissions.

[0003] In many cases, multiple vehicle queues traveling in different lanes have some common interests (e.g., destination, partially overlapping paths), and cooperative driving can be applied in these scenarios. The above scenario requirements have promoted the research on multi-queue vehicle formation control. Multiple queues of vehicles form a group, and the required cooperative state should be achieved not only within each sub-queue but also within the entire group.

[0004] In existing research, the cooperation between intelligent connected queues pays more attention to the group consensus at the system level, while less attention is paid to the connection between the leaders of different sub-groups. The lateral control proposed in existing research cannot converge quickly when far from the lane line, and there are safety hazards during the lane-changing process. Generally speaking, existing research lacks the research on the merging of intelligent connected queues and rarely fully considers different scenarios of the merging of intelligent connected queues. Regarding the merging of intelligent connected queues, during the merging process, vehicles are directly controlled not only by the current queue but also indirectly by the entire group. Summary of the Invention

[0005] Object of the Invention: To overcome the deficiencies of the above-mentioned prior art, the object of the present invention is to propose an intelligent connected queue merging control method based on swarm theory. Based on the merging order of intelligent connected queues and the multi-agent control model of leader-follower, the state change of the leader is controlled. Furthermore, based on the vehicle position, speed, and acceleration data under the intelligent connected queue and intelligent connected group network topologies as basic information, the control inputs required for the lateral and longitudinal directions of each following vehicle in the intelligent connected queue are calculated, thereby realizing the control of the entire intelligent connected group and ensuring the safe, efficient, and stable state of road traffic.

[0006] Technical Solution: To solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0007] An intelligent connected vehicle queue merging control method based on cluster theory, comprising the following steps:

[0008] (1) Model the system dynamic equation, where the variables in the system dynamic equation include vehicle longitudinal and lateral positions, speed, and control input;

[0009] (2) At the moment when the intelligent connected vehicle group enters the merging area section, establish the network topology between the leading vehicles of the intelligent connected vehicle queues according to the merging order of the intelligent connected vehicle queues;

[0010] (3) Determine the ideal control spacing according to the network topology between the leading vehicles of the intelligent connected vehicle queues and the length of the intelligent connected vehicle queues;

[0011] (4) Design an intelligent connected vehicle group controller according to the ideal control spacing;

[0012] (5) Design an intelligent connected vehicle queue controller to achieve the merging control of the intelligent connected vehicle queues.

[0013] Preferably, in step (1), the system dynamic equation is:

[0014] Dynamic equation of the following vehicle i in the intelligent connected vehicle queue k:

[0015]

[0016] Among them, k is the number of the intelligent connected vehicle queue in the intelligent connected vehicle group, k = 1,..., M, where M is the number of intelligent connected vehicle queues in the intelligent connected vehicle group; i is the number of the following vehicle in the intelligent connected vehicle queue, i = 1,..., N - 1, where N is the number of vehicles in the intelligent connected vehicle queue; is the longitudinal position of the following vehicle i in the intelligent connected vehicle queue k at time t; is the lateral position of the following vehicle i in the intelligent connected vehicle queue k at time t; is the longitudinal speed of the following vehicle i in the intelligent connected vehicle queue k at time t; is the lateral speed of the following vehicle i in the intelligent connected vehicle queue k at time t; is the longitudinal control input of the following vehicle i in the intelligent connected vehicle queue k at time t; is the lateral control input of the following vehicle i in the intelligent connected vehicle queue k at time t;

[0017] Dynamic equation of the leading vehicle of the intelligent connected vehicle queue k:

[0018]

[0019] Among them, and are defined in the same way as and is similar to

[0020]

[0021] is the longitudinal position of the leading vehicle in the intelligent connected vehicle queue k at time t; is the lateral position of the leading vehicle in the intelligent connected vehicle queue k at time t; is the longitudinal speed of the leading vehicle in the intelligent connected vehicle queue k at time t; is the lateral speed of the leading vehicle in the intelligent connected vehicle queue k at time t; is the longitudinal control input of the leading vehicle in the intelligent connected vehicle queue k at time t; is the lateral control input of the leading vehicle in the intelligent connected vehicle queue k at time t.

[0022] Preferably, in step (2), the method for establishing the network topology between the leading vehicles of the intelligent connected vehicle queues is as follows:

[0023] Multiple intelligent connected vehicle queues driving on different lanes and with longitudinal positions satisfying the set conditions form an intelligent connected vehicle group when entering the merging area. After the merging order of the intelligent connected vehicle queues in the intelligent connected vehicle group is determined, a network topology is established in which the leading vehicle of the intelligent connected vehicle queue in the first order sends its own status information to the leading vehicles of other queues. The set condition is: the maximum longitudinal distance between any two intelligent connected vehicle queues is less than the steady-state queue length of any other intelligent connected vehicle queue under the set intelligent connected vehicle penetration rate.

[0024] Preferably, in step (3), the method for determining the desired distance is as follows:

[0025] The ideal distance is the desired distance inside the intelligent connected vehicle group and the desired distance inside the intelligent connected vehicle queue;

[0026] Method for calculating the desired distance inside the intelligent connected vehicle group:

[0027] Calculate L k = N k × H k where L k are the lengths of the intelligent connected vehicle queue k respectively; N k is the number of vehicles in the intelligent connected vehicle queue k; H k is the ideal headway of the vehicles in the intelligent connected vehicle queue k under queue control, which can be the headway of the queue k in the steady state

[0028] The intelligent connected vehicle queue k in the first order 1st The desired distance r 1st = 0; The desired distances of the remaining intelligent connected vehicle queues Increase its own queue length in the confluence order in turn, that is is the length of the intelligent connected queue of the nth order;

[0029] The expected spacing inside the intelligent connected queue is the headway of the intelligent connected queue k in the steady state

[0030] are the expected spacings of the following vehicle i, the following vehicle j and the leading vehicle in the intelligent connected queue k respectively, is the longitudinal position of the leading vehicle in the intelligent connected queue k in the steady state, is the longitudinal position of the rear vehicle in the intelligent connected queue k in the steady state.

[0031] Preferably, in step (4), the intelligent connected group controller includes a longitudinal controller and a lateral controller,

[0032] Longitudinal controller design:

[0033]

[0034] Among them, d1 and d2 are positive parameters related to the communication between queues, is the intelligent connected queue k located in the first order 1s The longitudinal position of the leading vehicle at time t, is the intelligent connected queue k located in the first order 1st The longitudinal speed of the leading vehicle at time t, is The derivative with respect to time;

[0035] Lateral controller design:

[0036] 1) When the number of lanes in the driving direction is odd and the lane where the confluence point is located is in the middle, the lateral control input is:

[0037]

[0038] f′(q) = (e q -1)(1 / (M′l) 2 -1 / q 2 ), 0 < q < 2M′l

[0039]

[0040] Among them, is the projection of the lateral position of the leading vehicle of the intelligent connected queue k at time t on the lane lines on the two outermost sides of the driving road, For the lateral control correction of the leading vehicle at time t in the intelligent connected vehicle queue k, l is the half-lane width, m (m = 1, 2) are two projected target lane lines, and M' is the number of lanes;

[0041] 2) The lateral control input in other cases is:

[0042]

[0043] Among them, is the projection of the lateral position of the leading vehicle at time t in the intelligent connected vehicle queue k on the two lane lines of the lane where the merging point is located. β2 and β3 are positive constants, and q b (t) is the projection of the lateral position of the leading vehicle at time t in the intelligent connected vehicle queue k on the lane line farthest from the vehicle in the traveling direction of the road.

[0044] Preferably, in step (5), the intelligent connected vehicle queue controller includes a longitudinal controller and a lateral controller.

[0045] The longitudinal controller of the following vehicle i in the intelligent connected vehicle queue k:

[0046]

[0047] Among them, the expected spacing between following vehicles γ1 and γ2 are positive constants; and The meanings of are:

[0048] Define the communication topology within the intelligent connected vehicle queue based on algebraic graph theory. Each vehicle in the queue is a communication node of a weighted graph. is the communication parameter between the i-th following vehicle and the j-th following vehicle in the intelligent connected vehicle queue k. There is a communication link when. is the communication parameter between the leading vehicle in the intelligent connected vehicle queue k and the i-th following vehicle. There is a communication link when;

[0049] The lateral controller of the following vehicle i in the intelligent connected vehicle queue k:

[0050]

[0051] Among them, and are respectively the projections of the lateral position and lateral speed of the leading vehicle at time t in the intelligent connected vehicle queue k on the lane line of the current driving lane. is the lane lateral control correction of the i-th following vehicle in the intelligent connected vehicle queue k. α 1 and α 2 are positive constants.

[0052] Beneficial effects: An intelligent connected vehicle queue merging control method based on the clustering theory proposed by the present invention determines the network topology between different merging queues based on the vehicle states and merging orders of the intelligent connected vehicle queues, and further determines the expected spacing between the leading vehicles of different queues and the expected spacing between the vehicles within the queue. Based on the expected spacing and vehicle states, an intelligent connected vehicle group control protocol and an intelligent connected vehicle queue control protocol are designed. The method provided by the present invention comprehensively considers the applicability of lateral control in the merging scenario, and the proposed artificial function f(q) can better adapt to different merging scenarios, thereby realizing queue and group control and providing guarantee for road traffic safety. Description of the Drawings

[0053] Figure 1 is the flowchart of the method according to the embodiment of the present invention;

[0054] Figure 2 is the schematic diagram of the initial traffic condition according to the embodiment of the present invention;

[0055] Figure 3 is the schematic diagram of the coordinate system establishment according to the embodiment of the present invention;

[0056] Figure 4 is the schematic diagram of the traffic condition after merging according to the embodiment of the present invention. Detailed Embodiment

[0057] In order to make the content of the present invention easier to be clearly understood, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0058] As Figure 1 shown, a method for determining the driving safety level based on the field theory disclosed in the embodiment of the present invention includes the following steps:

[0059] (1) Establish a system dynamic equation according to vehicle kinematics.

[0060] Specifically, only considering the kinematic characteristics of the vehicle, the vehicle is regarded as a rigid body to simplify the calculation; the state variables are vehicle position, vehicle speed and vehicle control input, and the state variables are decoupled horizontally and vertically; system dynamic equations are established for the leading vehicle of the queue and the following vehicle respectively:

[0061]

[0062] (2) At the moment when the intelligent connected vehicle group enters the cross-section of the merging area, establish the network topology between the leading vehicles of the intelligent connected vehicle queues according to the merging order of the intelligent connected vehicle queues.

[0063] The merging area is the area for judging the merging order of vehicles, and its size can be determined by the traffic density and the length of the intelligent connected vehicle queue. Generally, it can be taken as 450 m upstream of the merging point. Let the longitudinal positions of the leading vehicle, the vehicle in front of the leading vehicle, the trailing vehicle, and the vehicle following the trailing vehicle of the intelligent connected vehicle queue i be where n ∈ {leader, leader-f, last, last-r}, leader represents the leading vehicle, leader-f represents the vehicle in front of the leading vehicle, last represents the trailing vehicle, and last-r represents the vehicle following the trailing vehicle; i ∈ {1, 2, 3} represents the intelligent connected vehicle queue and the number of its lane, 1 represents the innermost lane, 2 represents the middle lane, and 3 represents the outermost lane.

[0064] In this embodiment, as Figure 2 shown in the mixed traffic merging scenario under a three-lane road with the two outer lanes closed. The intelligent connected vehicle group is a group formed by the intelligent connected vehicle queues 1, 2, and 3 that are driving parallel and have similar longitudinal positions at the lane-changing decision moment. In the merging area, the intelligent connected vehicles in the intelligent connected vehicle group form a communication topology and perform cooperative control to complete the merging process.

[0065] The formation condition of the intelligent connected vehicle group is that at a certain intelligent connected vehicle penetration rate, the maximum longitudinal distance between any two intelligent connected vehicle queues at the decision moment is less than the steady-state queue length of another intelligent connected vehicle queue. That is, the measure of the similarity of the longitudinal positions of the queues is related to the intelligent connected vehicle penetration rate. The merging process of the queues in the vehicle group can be adjusted by controlling the acceleration and deceleration of the vehicles. Specifically:

[0066]

[0067] In the formula, N z is the number of vehicles in the intelligent connected vehicle queue z, and α is a coefficient related to the intelligent connected vehicle penetration rate.

[0068] The calculation method of the longitudinal position of the vehicle in front of the leading vehicle in the intelligent connected vehicle queue 2 after the manually driven vehicle in front of each intelligent connected vehicle queue merges first is as follows:[[]]

[0069] When and at the same time,[[]] When or at the same time,[[]]

[0070] The calculation method of the front and rear surplus distances I f and I r of the intelligent connected vehicle queue 2 is as follows:[[]]

[0071]

[0072] The safe headway s of the Gipps car-following model safe =(v j (t)τ j +v j-1 (t) 2 / B j-1 +2l j-1 -((v j (t+τ j )-B j τ j ) 2 -B j 2 τ j 2 ) / B j ) / 2, where v j (t) is the speed of vehicle j at time t, τ j is the reaction time of the driver of vehicle j, v j-1 (t) is the speed of the leading vehicle j-1 of vehicle j at time t, B j is the maximum deceleration of vehicle j, l j is the body length of vehicle j, where vehicle j is a manually driven vehicle in a balanced state.

[0073] The calculation methods for the lengths L1 and L3 of the intelligent connected queue 1 and the intelligent connected queue 3 are as follows:

[0074] L1 = N1 * H1

[0075] L3 = N3 * H3

[0076] In the formula, N1 and N3 are the number of vehicles in the intelligent connected queue 1 and the intelligent connected queue 3 respectively, and H1 and H3 are the headways of the vehicles in the intelligent connected queue 1 and the intelligent connected queue 3 under queue control respectively.

[0077] The headway H of the vehicles in the intelligent connected queue under queue control is H = N(X leader -X la ) / (N - 1), where N is the number of vehicles in the intelligent connected queue, X leader is the longitudinal position of the leading vehicle of the intelligent connected queue, and X las is the longitudinal position of the trailing vehicle of the intelligent connected queue.

[0078] The method for judging the merging order of the intelligent connected queues in the intelligent connected group at the confluence point is as follows:

[0079] If all of the following conditions are met simultaneously: ① L1 ≥ L3, ② I f ≥ L1 + L3, ③ I r ≥ L1 + L3 or L1 + L3 > Ir ≥ L1 or L1 > I r ≥ L3 or L3 > I r ,④ Then the merging order at the confluence point is Connected and Autonomous Vehicle Queue 3, Connected and Autonomous Vehicle Queue 1, Connected and Autonomous Vehicle Queue 2;

[0080] If all of the following conditions are satisfied simultaneously: ① L1 ≥ L3, ② I f ≥ L1 + L3, ③ I r ≥ L1 + L3 or L1 + L3 > I r ≥ L1 or L1 > I r ≥ L3 or L3 > I r ,④ Then the merging order at the confluence point is Connected and Autonomous Vehicle Queue 1, Connected and Autonomous Vehicle Queue 3, Connected and Autonomous Vehicle Queue 2;

[0081] If all of the following conditions are satisfied simultaneously: ① L1 ≥ L3, ② L1 + L3 > I f ≥ L1, ③ I r ≥ L1 + L3 or L1 + L3 > I r ≥ L1 or L1 > I r ≥ L3, then the merging order at the confluence point is Connected and Autonomous Vehicle Queue 1, Connected and Autonomous Vehicle Queue 2, Connected and Autonomous Vehicle Queue 3;

[0082] If all of the following conditions are satisfied simultaneously: ① L1 ≥ L3, ② L1 + L3 > I f ≥ L1, ③ L3 > I r ,④ Then the merging order at the confluence point is Connected and Autonomous Vehicle Queue 3, Connected and Autonomous Vehicle Queue 1, Connected and Autonomous Vehicle Queue 2;

[0083] If all of the following conditions are satisfied simultaneously: ① L1 ≥ L3, ② L1 + L3 > I f ≥ L1, ③ L3 > I r ,④ Then the merging order at the confluence point is Connected and Autonomous Vehicle Queue 1, Connected and Autonomous Vehicle Queue 3, Connected and Autonomous Vehicle Queue 2;

[0084] If all of the following conditions are satisfied simultaneously: ① L1 ≥ L3, ② L1 > I f ≥ L3, ③ I r ≥ L1 + L3 or L1 + L3 > I r ≥ L1, then the merging order at the confluence point is Connected and Autonomous Vehicle Queue 3, Connected and Autonomous Vehicle Queue 2, Connected and Autonomous Vehicle Queue 1;

[0085] If all of the following conditions are satisfied simultaneously: ① L1 ≥ L3, ② L1 > I f ≥ L3, ③ L1 > I r ≥ L3 or L3 > L3, ④ Then the merging order at the confluence point is the intelligent networked queue 3, the intelligent networked queue 1, and the intelligent networked queue 2;

[0086] If all of the following conditions are met simultaneously: ① L1≥L3, ② L1>I f ≥L3, ③ L1>I r ≥L3 or L3>L3, ④ Then the merging order at the confluence point is the intelligent networked queue 1, the intelligent networked queue 3, and the intelligent networked queue 2;

[0087] If all of the following conditions are met simultaneously: ① L1≥L3, ② L3>I f , ③ I r ≥L1+L3, ④ Then the merging order at the confluence point is the intelligent networked queue 2, the intelligent networked queue 3, and the intelligent networked queue 1;

[0088] If all of the following conditions are met simultaneously: ① L1≥L3, ② L3>I f , ③ I r ≥L1+L3, ④ Then the merging order at the confluence point is the intelligent networked queue 2, the intelligent networked queue 1, and the intelligent networked queue 3;

[0089] If all of the following conditions are met simultaneously: ① L1≥L3, ② L3>I f , ③ L1+L3>I r ≥L1 or L1>I r ≥L3 or L3>I r , ④ Then the merging order at the confluence point is the intelligent networked queue 3, the intelligent networked queue 1, and the intelligent networked queue 2;

[0090] If all of the following conditions are met simultaneously: ① L1≥L3, ② L3>I f , ③ L1+L3>I r ≥L1 or L1>I r ≥L3 or L3>I r , ④ Then the merging order at the confluence point is the intelligent networked queue 1, the intelligent networked queue 3, and the intelligent networked queue 2;

[0091] If all of the following conditions are met simultaneously: ① L1<L3, ② I f ≥L3+L1, ③ I r ≥L3+L1 or L3+L1>I r ≥L3 or L3>I r ≥L1 or L1>I r , ④ Then the merging order at the confluence point is the intelligent networked queue 3, the intelligent networked queue 1, and the intelligent networked queue 2;

[0092] If all of the following conditions are met simultaneously: ① L1 < L3, ② I f ≥ L3 + L1, ③ I r ≥ L3 + L1 or L3 + L1 > I r ≥ L3 or L3 > I r ≥ L1 or L1 > I r , ④ then the merging order at the confluence point is Connected and Automated Vehicle Queue 1, Connected and Automated Vehicle Queue 3, Connected and Automated Vehicle Queue 2;

[0093] If all of the following conditions are met simultaneously: ① L1 < L3, ② L3 + L1 > I f ≥ L3, ③ I r ≥ L3 + L1 or L3 + L1 > I r ≥ L3 or L3 > I r ≥ L1, then the merging order at the confluence point is Connected and Automated Vehicle Queue 3, Connected and Automated Vehicle Queue 2, Connected and Automated Vehicle Queue 1;

[0094] If all of the following conditions are met simultaneously: ① L1 < L3, ② L3 + L1 > I f ≥ L3, ③ L1 > I r , ④ then the merging order at the confluence point is Connected and Automated Vehicle Queue 3, Connected and Automated Vehicle Queue 1, Connected and Automated Vehicle Queue 2;

[0095] If all of the following conditions are met simultaneously: ① L1 < L3, ① L3 + L1 > I f ≥ L3, ② L1 > I r , ③ then the merging order at the confluence point is Connected and Automated Vehicle Queue 1, Connected and Automated Vehicle Queue 3, Connected and Automated Vehicle Queue 2;

[0096] If all of the following conditions are met simultaneously: ① L1 < L3, ② L3 > I f ≥ L1, ③ I r ≥ L3 + L1 or L3 + L1 > I r ≥ L3, then the merging order at the confluence point is Connected and Automated Vehicle Queue 1, Connected and Automated Vehicle Queue 2, Connected and Automated Vehicle Queue 3;

[0097] If all of the following conditions are met simultaneously: ① L1 < L3, ② L3 > I f ≥ L1, ③ L3 > I r ≥ L1 or L1 > I r , ④ then the merging order at the confluence point is Connected and Automated Vehicle Queue 3, Connected and Automated Vehicle Queue 1, Connected and Automated Vehicle Queue 2;

[0098] If all of the following conditions are met simultaneously: ① L1 < L3, ② L3 > I f ≥ L1, ③ L3 > I r≥L1 or L1 > I r ,④ Then the merging order at the confluence point is Connected and Autonomous Vehicle Queue 1, Connected and Autonomous Vehicle Queue 3, Connected and Autonomous Vehicle Queue 2;

[0099] If all of the following conditions are met simultaneously: ① L1 < L3, ② L1 > I f ,③ I r ≥ L3 + L1, ④ Then the merging order at the confluence point is Connected and Autonomous Vehicle Queue 2, Connected and Autonomous Vehicle Queue 3, Connected and Autonomous Vehicle Queue 1;

[0100] If all of the following conditions are met simultaneously: ① L1 < L3, ② L1 > I f ,③ I r ≥ L3 + L1, ④ Then the merging order at the confluence point is Connected and Autonomous Vehicle Queue 2, Connected and Autonomous Vehicle Queue 1, Connected and Autonomous Vehicle Queue 3;

[0101] If all of the following conditions are met simultaneously: ① L1 < L3, ② L1 > I f ,③ L3 + L1 > I r ≥ L1 or L3 > I r ≥ L1 or L1 > I r ,④ Then the merging order at the confluence point is Connected and Autonomous Vehicle Queue 3, Connected and Autonomous Vehicle Queue 1, Connected and Autonomous Vehicle Queue 2;

[0102] If all of the following conditions are met simultaneously: ① L1 < L3, ② L1 > I f ,③ L3 + L1 > I r ≥ L1 or L3 > I r ≥ L1 or L1 > I r ,④ Then the merging order at the confluence point is Connected and Autonomous Vehicle Queue 1, Connected and Autonomous Vehicle Queue 3, Connected and Autonomous Vehicle Queue 2.

[0103] In this embodiment, L1 = 90m, L3 = 120m, I f = 160m, I r = 140m, satisfying the conditions L1 < L3, L3 + L1 > I f ≥ L3, L3 + L1 > I r ≥ L3, then the merging order is Connected and Autonomous Vehicle Queue 3, Connected and Autonomous Vehicle Queue 2, Connected and Autonomous Vehicle Queue 1, and a queue - to - queue communication topology for the following Figure 2 traffic scenario is established.

[0104] (3) Determine the ideal control spacing according to the network topology and the length of the connected and autonomous vehicle queues.

[0105] Specifically, assuming the ideal headway is 30m, then L 1 = 90m, L2 = 90 m, L 3 = 120 m,

[0106] r 3 = r 1st = 0 m, r 2 = r 2n = r 3 + L 3 = 120 m, r 1 = r 3rd = r 2 + L 2 = 210 m.

[0107]

[0108] (4) Design an intelligent connected vehicle platoon controller according to the ideal control spacing.

[0109] Figure 2 The intelligent connected vehicle platoon controller in the traffic scenario is as follows:

[0110] Longitudinal controller design:

[0111]

[0112] Among them is related to the state of the trailing vehicle after merging with the preceding manually driven vehicle.

[0113] Lateral controller design:

[0114]

[0115] f′(q) = (e q - 1)(1 / (M′l) 2 - 1 / q 2 ), 0 < q < 2M′l

[0116]

[0117]

[0118] Since Figure 2 the merging traffic scenario is relatively simple and the lane is a straight lane, the position coordinates as shown in Figure 3 can be established in the earth coordinate system.

[0119] (5) Design an intelligent connected vehicle queue controller to achieve the merging control of the intelligent connected vehicle queue.

[0120] The longitudinal controller of the following vehicle i in the intelligent connected vehicle queue k:

[0121]

[0122] Lateral controller of following vehicle i in intelligent connected queue k:

[0123]

[0124] Under the above-mentioned platoon and queue controllers, all vehicles will converge to the mid-line position of Lane 2, and the traffic conditions are as Figure 4 shown.

[0125] and mean:

[0126] Define the communication topology within the queue based on algebraic graph theory. The topology of following vehicles within the queue can be described by G = {V, E, a}, where V is a set of N - 1 nodes, E is the set of edges, and each vehicle is a communication node of the weighted graph. The edge represents the communication link between vehicles; the topology structure of the graph is represented by an adjacency matrix A = [a i,j ij], a i,j ij ≥ 0, and there is a communication link when a i,j ij > 0; the incidence matrix represents the communication link between the following vehicle and the leading vehicle, and its form is ∏ = diag(θ1, θ2,... θ N-1 N - 1), θ i i ≥ 0, and there is a communication link when θ i i > 0.

[0127] Obviously, the above embodiments are only examples for clear illustration and not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all implementation manners here. And the obvious changes or modifications derived therefrom are still within the protection scope of the present invention.

Claims

1. An intelligent networked queue merging control method based on cluster theory, characterized in that It includes the following steps: (1) Conduct system dynamic equation modeling, where the variables in the system dynamic equation include vehicle longitudinal and lateral positions, speed, and control input; (2) At the moment when the intelligent connected vehicle group enters the confluence area section, establish the network topology among the leading vehicles of the intelligent connected vehicle queues according to the confluence order of the intelligent connected vehicle queues; (3) Determine the ideal control spacing according to the network topology among the leading vehicles of the intelligent connected vehicle queues and the length of the intelligent connected vehicle queues; (4) Design an intelligent connected vehicle group controller according to the ideal control spacing; (5) Design an intelligent connected vehicle queue controller to achieve the confluence control of the intelligent connected vehicle queues.

2. The intelligent networked queue merging control method based on the clustering theory according to claim 1, wherein In step (1), the system dynamic equation is: Among them, k is the number of the intelligent connected queue in the intelligent connected group, k = 1,..., M, where M is the number of intelligent connected queues in the intelligent connected group; i is the number of the following vehicle in the intelligent connected queue, i = 1,..., N - 1, and N is the number of vehicles in the intelligent connected queue; is the longitudinal position of the following vehicle i in the intelligent connected queue k at time t; is the lateral position of the following vehicle i in the intelligent connected queue k at time t; is the longitudinal speed of the following vehicle i in the intelligent connected queue k at time t; is the lateral speed of the following vehicle i in the intelligent connected queue k at time t; is the longitudinal control input of the following vehicle i in the intelligent connected queue k at time t; is the lateral control input of the following vehicle i in the intelligent connected queue k at time t; is the longitudinal position of the leading vehicle in the intelligent connected queue k at time t; is the lateral position of the leading vehicle in the intelligent connected queue k at time t; is the longitudinal speed of the leading vehicle in the intelligent connected queue k at time t; is the lateral speed of the leading vehicle in the intelligent connected queue k at time t; is the longitudinal control input of the leading vehicle in the intelligent connected queue k at time t; is the lateral control input of the leading vehicle in the intelligent connected queue k at time t.

3. The intelligent networked queue merging control method based on the clustering theory according to claim 2, wherein, In step (2), the method for establishing the network topology among the leading vehicles of the intelligent connected vehicle queues is: Multiple columns of intelligent connected vehicle queues driving on different lanes and with longitudinal positions satisfying the set conditions form an intelligent connected vehicle group when entering the confluence area. After the confluence order of the intelligent connected vehicle queues in the intelligent connected vehicle group is determined, establish a network topology in which the leading vehicle of the intelligent connected vehicle queue in the first order sends its own state information to the leading vehicles of other queues.

4. The intelligent networked queue merging control method based on the clustering theory according to claim 3, characterized in that, The set condition is: under the set intelligent connected vehicle penetration rate, the maximum longitudinal spacing between any two columns of intelligent connected vehicle queues is less than the steady-state queue length of any other intelligent connected vehicle queue.

5. The intelligent connected vehicle queue merging control method based on the clustering theory according to claim 4, wherein, In step (3), the ideal control spacing is divided into the ideal control spacing inside the intelligent connected vehicle group and the ideal control spacing inside the intelligent connected vehicle queue. The specific determination method is: Calculation method of the ideal control spacing inside the intelligent connected vehicle group: Calculate L k = N k × H k , where L k is the length of the intelligent connected vehicle queue k, N k is the number of vehicles in the intelligent connected vehicle queue k, and H k is the ideal headway of the vehicles in the intelligent connected vehicle queue k under queue control; The ideal control spacing of the intelligent connected vehicle queue k in the first order 1st is The ideal control spacing of the remaining intelligent connected vehicle queues is is the length of the intelligent connected vehicle queue in the nth order; The ideal control spacing inside the intelligent connected vehicle platoon is the headway of intelligent connected vehicle platoon k at steady state. They are the expected spacings of following vehicle i, following vehicle j and the leading vehicle in intelligent connected vehicle platoon k respectively. It is the longitudinal position of the leading vehicle in intelligent connected vehicle platoon k at steady state. It is the longitudinal position of the trailing vehicle in intelligent connected vehicle platoon k at steady state.

6. The intelligent networked queue merging control method based on the clustering theory according to claim 5, wherein, Headway of Intelligent Connected Queue k in Steady State 7. The intelligent networked queue merging control method based on the cluster theory according to claim 5, wherein In step (4), the intelligent connected vehicle group controller includes a longitudinal controller and a lateral controller. Specifically: Longitudinal controller: where d1 and d2 are positive parameters related to communication between queues, is the intelligent connected vehicle queue k at the first order, 1st the longitudinal position of the leading vehicle at time t, is the intelligent connected vehicle queue k at the first order, 1st the longitudinal speed of the leading vehicle at time t, is the derivative with respect to time; The lateral controller is divided into two cases: 1) When the number of lanes in the traveling direction is odd and the lane where the confluence point is located is in the middle, the lateral control input is: f′(q) = (e q - 1)(1 / (M′l) 2 - 1 / w 2 ), 0 < q < 2M′l Among them, is the projection of the lateral position of the leading vehicle of the intelligent connected queue k on the outermost lane lines of the driving road at time t, is the lateral control correction of the leading vehicle of the intelligent connected queue k at time t on the road, l is the half-lane width, m is two projected target lane lines, m = 1, 2, and M′ is the number of lanes; 2) In other cases, the lateral control input is: Among them, is the projection of the lateral position of the leading vehicle in the intelligent connected vehicle platoon k at time t on the two-lane line of the lane where the merging point is located. β2 and β3 are positive constants, and q b (t) is the projection of the lateral position of the leading vehicle in the intelligent connected vehicle platoon k at time t on the lane line farthest from the vehicle in the driving direction of the road.

8. The intelligent networked queue merging control method based on the clustering theory according to claim 7, characterized in that In step (5), the intelligent connected vehicle queue controller includes a longitudinal controller and a lateral controller. Specifically: Longitudinal controller of the following vehicle i in the intelligent connected vehicle queue k: where γ1 and γ2 are positive constants; Lateral controller of the following vehicle i in the intelligent connected vehicle queue k: f(q) = (q - l) / (lq), 0 < q < 2l Among them, and are respectively the projections of the lateral position and lateral speed of the leading vehicle at time t on the lane line of the current driving lane in the intelligent connected vehicle queue k, is the lane lateral control correction of the i-th following vehicle in the intelligent connected vehicle queue k, α 1 and α 2 are positive constants; and means that the communication topology within the intelligent connected queue is defined based on algebraic graph theory. Each vehicle in the queue is a communication node of a weighted graph. is the communication parameter between the i-th following vehicle and the j-th following vehicle in the intelligent connected queue k. There is a communication link when is the communication parameter between the leading vehicle and the i-th following vehicle in the intelligent connected queue k. There is a communication link when

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