A method and system for joint optimization of autonomous intersection communication networking and vehicle trajectory

Abstract

The present invention discloses a method and system for joint optimization of autonomous intersection communication networking and vehicle trajectories, and relates to the technical field of wireless communications. First, basic safety information of intelligent connected vehicles is obtained; then, the intelligent connected vehicles are divided into relay vehicles and ordinary vehicles according to the basic safety information; secondly, vehicle trajectories are planned for relay vehicles and ordinary vehicles, and a backbone network is generated based on the vehicle trajectory planning of all intelligent connected vehicles; finally, an information flow distribution strategy is formulated according to the backbone network, and the intelligent connected vehicles distribute information flow according to the information flow distribution strategy. Through the method and system of the present invention, the negative impact of communication instability on the control strategy of intelligent connected vehicles at autonomous intersections can be significantly reduced, and a closed-loop operation mechanism for coordinated optimization of transportation systems and communication systems can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communications, and in particular to a method and system for jointly optimizing autonomous intersection communication networking and vehicle trajectories. Background Art

[0002] With the development of V2X (Vehicle to Everything) communication technology, intelligent connected vehicles can exchange motion information with other road traffic participants in real time, thereby realizing cooperative driving at signal-free intersections. Most existing cooperative driving studies assume that the communication environment is ideal and ignore the coupling relationship between the control strategy of intelligent connected vehicles and the communication network. Due to the inevitable communication delay and packet loss in reality, it is difficult to achieve the theoretical upper limit of intelligent connected vehicle control, and even serious safety hazards arise. Therefore, in order to adapt to the communication environment of urban intersections, it is urgent to improve the cooperative driving strategy from the perspective of traffic communication coupling.

[0003] Existing research usually separates intelligent connected vehicle control and communication networking into two systems for optimization. Some studies usually quantify the instability of the communication system as communication delay or failure, and study the impact of communication disturbance on the control strategy. Some other work focuses on the passive feedback of vehicles or collaborative systems to communication delays and failures. By improving the fault-tolerant mechanism of the collaborative strategy, the potential threat of communication failures to the traffic system can be eliminated. However, these methods are applicable to column-type convoys and relatively simple collaborative systems (such as ramps), and are not applicable to unsignaled intersections in urban areas with strong coupling relationships. Intelligent connected vehicles can only passively respond to communication instability by strengthening the fault-tolerant mechanism of collaborative technology, but cannot contribute to the improvement of the communication environment.

[0004] The communication of autonomous intersections is characterized by large demand, high concurrency, and non-delay tolerance. In addition, due to the obstruction of trees and buildings, the communication environment of unsignalized intersections is complex. If the communication route is not optimized, the transmission delay of the message packet will increase. The existing routing optimization protocol has some problems in adapting to the cooperative driving system of unsignalized intersections, such as focusing on the routing reliability optimization problem of the individual benefits of a single data packet, ignoring the maximization of the overall benefit of the communication network. In a cooperative driving system with high-density communication requirements, high concurrency communication requirements may cause channel congestion, thereby increasing the transmission delay. The above problems make it difficult for the networking requirements of the communication system to feedback and intervene in the driving control of the vehicle; in addition, the applicable scenarios usually only involve one-dimensional vehicle following motion, and do not pay attention to the complex interactive behaviors (such as following, merging, conflict, etc.) and complex communication environments (such as severe transmission signal fading caused by obstruction of buildings and facilities) brought about by the two-dimensional road characteristics of the intersection, which brings more uncertainty to the networking model. There is a problem that the communication demand cannot be fed back to the traffic system, resulting in the problem that the trajectory of the intelligent connected vehicle and the communication network cannot actively provide a good communication environment. Summary of the invention

[0005] In order to overcome the problem in the prior art that communication needs cannot be fed back to the traffic system, resulting in the inability of the intelligent connected vehicle trajectory and communication networking to actively provide a good communication environment, the present invention, through a method and system for jointly optimizing autonomous intersection communication networking and vehicle trajectory, can significantly reduce the negative impact of unstable communication on the control strategy of intelligent connected vehicles at autonomous intersections, and realize a closed-loop operation mechanism for coordinated optimization of the traffic system and the communication system.

[0006] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0007] A method for jointly optimizing autonomous intersection communication networking and vehicle trajectory, the method comprising the following steps:

[0008] Obtain basic safety information of intelligent connected vehicles;

[0009] Classifying intelligent connected vehicles into relay vehicles and ordinary vehicles according to the basic safety information;

[0010] Perform vehicle trajectory planning for relay vehicles and ordinary vehicles, and generate a backbone network based on the vehicle trajectory planning of all intelligent connected vehicles;

[0011] An information flow distribution strategy is formulated according to the backbone network, and the intelligent connected vehicles distribute information flow according to the information flow distribution strategy.

[0012] In the above technical scheme, by dividing the intelligent connected vehicles into relay vehicles and ordinary vehicles based on their basic safety information, it is possible to identify the nodes that play a key role in optimizing the communication network, thereby realizing centralized networking and distributed vehicle control; by planning the vehicle trajectories of relay vehicles and ordinary vehicles, it is possible to ensure that they are in a line-of-sight communication environment throughout the communication cycle, thereby improving the robustness of the communication networking; a backbone network is generated based on the vehicle trajectory planning of all intelligent connected vehicles, and an information flow distribution strategy is formulated based on the backbone network. This phased networking mode can reduce the complexity of the problem and support rapid online optimization of the networking solution, thereby significantly reducing the negative impact of communication instability on the control strategy of the intelligent connected vehicles at autonomous intersections, and realizing a closed-loop operation mechanism for coordinated optimization of the transportation system and the communication system.

[0013] Furthermore, the process of classifying the intelligent connected vehicles into relay vehicles and ordinary vehicles according to the basic safety information includes:

[0014] Collect basic safety information of all intelligent connected vehicles in the control area and send the basic safety information to the edge computing unit;

[0015] The maximum deceleration movement distance of the intelligent connected vehicle is calculated based on the basic safety information, and the expression is:

[0016]

[0017] Based on the maximum deceleration movement distance of intelligent connected vehicles, intelligent connected vehicles are divided into relay vehicles and ordinary vehicles, and the expression is:

[0018]

[0019] Where, ΔT RO Represents the communication cycle, Indicates the communication cycle ΔT RO The moving distance of intelligent connected vehicle i in the figure, i represents the i-th intelligent connected vehicle, l represents the moving distance, represents the speed of intelligent connected vehicle i at time T, a min represents the maximum deceleration, ω1 represents the first classification coefficient, ω2 represents the second classification coefficient, represents the set of relay vehicles, Represents a collection of common vehicles, and the basic safety information includes vehicle number, location and speed.

[0020] Furthermore, according to the maximum deceleration movement distance of the intelligent networked vehicle, it is judged whether the intelligent networked vehicle can stop moving within the boundary of the line-of-sight communication area. The expression is:

[0021]

[0022] Calculate the route traffic pressure of intelligent connected vehicles, the expression is:

[0023]

[0024] Among them, e LA represents the boundary of the line-of-sight communication area, represents the driving distance of intelligent connected vehicle i in lane l at time T, p th Indicates the threshold value of the flow pressure, Represents the traffic pressure of intelligent connected vehicle i in lane l at time T.

[0025] In the above technical solution, the intelligent connected vehicles in the AIM system are divided into relay vehicles and ordinary vehicles according to the maximum deceleration movement distance and the route traffic pressure, which can identify the nodes that play a key role in optimizing the communication network, thereby realizing centralized networking and distributed vehicle control.

[0026] Furthermore, the process of vehicle trajectory planning for relay vehicles and ordinary vehicles includes:

[0027] Building a trajectory planning model for each intelligent connected vehicle, wherein the trajectory planning model sets an objective function for each intelligent connected vehicle;

[0028] Constraints are set for ordinary vehicles and relay vehicles respectively. Based on the constraints of ordinary vehicles and relay vehicles, the objective function of each vehicle is minimized to generate vehicle trajectory planning information for each intelligent connected vehicle.

[0029] Furthermore, the process of setting the objective function for each intelligent connected vehicle by the trajectory planning model includes:

[0030] The trajectory planning model takes the sum of the distances between the position of the intelligent connected vehicle at each decision time step and the end point of the intersection as the objective function of the intelligent connected vehicle and calculates the movement distance of the intelligent connected vehicle at each time step. The expression is:

[0031]

[0032] Calculate the movement speed of the intelligent connected vehicle at each time step, the expression is:

[0033]

[0034] in, represents the driving distance of intelligent connected vehicle i in lane l at time T, represents the speed of intelligent connected vehicle i at time t, ΔT TP Indicates the communication cycle.

[0035] Furthermore, based on the constraints of ordinary vehicles and relay vehicles, the process of minimizing the objective function of each vehicle includes:

[0036] Set the following safety distance constraint for ordinary vehicles and relay vehicles, and the expression is:

[0037]

[0038] The conflict safety distance constraint is set for ordinary vehicles and relay vehicles, and the expression is:

[0039]

[0040] Set the line-of-sight environment constraint for the relay vehicle, the expression is:

[0041]

[0042] Based on the constraints of intelligent connected vehicles, the objective function of each vehicle is minimized, and the expression is:

[0043]

[0044] The objective function of each intelligent connected vehicle is iteratively optimized and solved using a rolling time domain to minimize the objective function of each vehicle. When the intelligent connected vehicle exits the intersection, the optimization result is output and used as the vehicle trajectory planning information of the intelligent connected vehicle.

[0045] in, Indicates the safety distance of the following safety constraint, L v Indicates the length of the vehicle body. represents the slack variable of the car-following safety constraint, represents the safety distance of the conflict safety constraint, F l,h 、F h,l Respectively represent the distance from the conflict point of lane l and lane h to the starting point of each lane, represents the slack variable of the conflict safety constraint, t i,l Indicates the end of the exit lane corresponding to lane l.

[0046] In the above technical solution, by performing vehicle trajectory planning for relay vehicles and ordinary vehicles, the relay vehicles are subject to additional line-of-sight environment constraints, which can ensure that they are in a line-of-sight communication environment throughout the communication cycle, thereby improving the robustness of the communication network.

[0047] Furthermore, the process of generating a backbone network based on vehicle trajectory planning of all intelligent connected vehicles includes:

[0048] Collect the trajectory planning information of all intelligent connected vehicles in the control area, and evaluate the communication benefits between intelligent connected vehicles based on the trajectory planning information. The expression is:

[0049]

[0050] The communication benefits between intelligent connected vehicles are calculated based on the evaluation results. The expression is:

[0051]

[0052] Construct a backbone network optimization model. The backbone network optimization model sets the objective function of the communication benefits between all intelligent connected vehicles according to the calculated communication benefits. The expression is:

[0053]

[0054] Solve the objective function to obtain the backbone network;

[0055] Among them, d i,j Indicates that in the communication cycle ΔT RO The average distance between the internal intelligent connected vehicle i and the intelligent connected vehicle i, represents the unit vector in the direction of motion of the intelligent connected vehicle i, represents the vector between intelligent connected vehicle i and intelligent connected vehicle j, e i,j Indicates a 0-1 variable, d r Represents the communication radius, and M represents a very large number.

[0056] In the above technical solution, a backbone network is generated based on the vehicle trajectory planning of all intelligent connected vehicles, and an information flow distribution strategy is formulated based on the backbone network. This phased networking mode can reduce the complexity of the problem and support rapid online optimization of the networking solution, thereby significantly reducing the negative impact of communication instability on the control strategy of intelligent connected vehicles at autonomous intersections, and realizing a closed-loop operation mechanism for coordinated optimization of the transportation system and the communication system.

[0057] Furthermore, the active and passive edge mechanism is adopted to build the backbone network optimization model, and the Gurobi solver is used to solve the objective function of the communication benefits among all intelligent connected vehicles.

[0058] In the above technical solution, the adopted solver Gurobi can better solve the backbone network.

[0059] Furthermore, the process of formulating an information flow distribution strategy according to the backbone network includes:

[0060] An information flow distribution model is constructed. The information flow distribution model sets the objective function of the total information transmission delay between all intelligent connected vehicles according to the backbone network. The expression is:

[0061]

[0062] Adopting adaptive large neighborhood search algorithm to solve the objective function of total information transmission delay, so as to minimize the total information transmission delay and obtain the multi-hop transmission path of all information packets;

[0063] Formulate an information flow distribution strategy according to the multi-hop transmission path of all information packets, and each intelligent connected vehicle distributes information flow according to the information flow distribution strategy;

[0064] in, Indicates that through the edge e i,j The number of message packets transmitted, Represents the edge e i,j The average transmission delay.

[0065] In the above technical solution, an information flow distribution strategy is formulated according to the backbone network. This phased networking mode can reduce the complexity of the problem and support rapid online optimization of the networking solution, thereby significantly reducing the negative impact of unstable communication on the control strategy of intelligent connected vehicles at autonomous intersections, and realizing a closed-loop operation mechanism for coordinated optimization of the transportation system and the communication system.

[0066] An autonomous intersection communication networking and vehicle trajectory joint optimization system, the system comprising:

[0067] An acquisition module is used to obtain basic safety information of intelligent connected vehicles;

[0068] A processing module, used for classifying intelligent connected vehicles into relay vehicles and ordinary vehicles according to the basic safety information;

[0069] Vehicle trajectory planning module, used to plan vehicle trajectories for relay vehicles and ordinary vehicles;

[0070] A backbone network generation module, used to generate a backbone network based on vehicle trajectory planning of all intelligent connected vehicles;

[0071] The information flow allocation module is used to formulate an information flow allocation strategy according to the backbone network, and the intelligent connected vehicle allocates information flow according to the information flow allocation strategy.

[0072] Compared with the prior art, the present invention has the following beneficial effects:

[0073] The present invention provides a method and system for joint optimization of communication networking and vehicle trajectories at autonomous intersections. By dividing intelligent connected vehicles into relay vehicles and ordinary vehicles based on basic safety information of intelligent connected vehicles, the nodes that play a key role in optimizing the communication network can be identified, thereby realizing centralized networking and distributed vehicle control. By performing vehicle trajectory planning for relay vehicles and ordinary vehicles, it can be ensured that they are in a line-of-sight communication environment throughout the communication cycle, thereby improving the robustness of the communication networking. A backbone network is generated based on the vehicle trajectory planning of all intelligent connected vehicles, and an information flow distribution strategy is formulated based on the backbone network. This phased networking mode can reduce the complexity of the problem and support rapid online optimization of the networking solution, thereby significantly reducing the negative impact of communication instability on the control strategy of intelligent connected vehicles at autonomous intersections, thereby realizing a closed-loop operation mechanism for coordinated optimization of the transportation system and the communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 A flowchart of the steps of an autonomous intersection communication networking and vehicle trajectory joint optimization method provided in an embodiment of the present application;

[0075] Figure 2 A schematic diagram of an autonomous intersection scenario in an urban area provided in an embodiment of the present application;

[0076] Figure 3 The edge-end collaborative computing framework provided for the embodiments of the present application;

[0077] Figure 4 A schematic diagram of the structure of an autonomous intersection communication networking and vehicle trajectory joint optimization system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0078] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Preferred embodiments of the present invention are provided in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive.

[0079] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0080] Embodiment 1:

[0081] This embodiment proposes a method for jointly optimizing autonomous intersection communication networking and vehicle trajectory. Figure 1 , the method comprises the following steps:

[0082] S1: Obtain basic safety information of intelligent connected vehicles;

[0083] S2: Classifying the intelligent connected vehicles into relay vehicles and ordinary vehicles according to the basic safety information;

[0084] S3: Perform vehicle trajectory planning for relay vehicles and ordinary vehicles, and generate a backbone network based on the vehicle trajectory planning of all intelligent connected vehicles;

[0085] S4: Formulate an information flow distribution strategy based on the backbone network, and the intelligent connected vehicles distribute information flow according to the information flow distribution strategy.

[0086] In step S2, see Figure 2 and Figure 3 The process of the edge computing unit classifying the intelligent connected vehicle into relay vehicles and ordinary vehicles according to the basic safety information includes:

[0087] The Road Side Unit (RSU) collects basic safety information of all intelligent connected vehicles in the control area and sends the basic safety information to the edge computing unit;

[0088] The roadside edge computing unit (MEC, Multi-access Edge Computing) calculates the maximum deceleration movement distance of the intelligent connected vehicle based on the basic safety information, and the expression is:

[0089]

[0090] Where, ΔT RO Represents the communication cycle, Indicates the communication cycle ΔT RO The moving distance of intelligent connected vehicle i in the figure, i represents the i-th intelligent connected vehicle, l represents the moving distance, represents the speed of intelligent connected vehicle i at time T, a min Indicates the maximum deceleration.

[0091] Furthermore, according to the maximum deceleration movement distance of the intelligent networked vehicle, it is judged whether the intelligent networked vehicle can stop moving within the boundary of the line-of-sight communication area. The expression is:

[0092]

[0093] Calculate the route traffic pressure of intelligent connected vehicles, the expression is:

[0094]

[0095] Among them, ω1 represents the first classification coefficient, that is, the first classification condition, ω2 represents the second classification coefficient, that is, the second classification condition, e LA represents the boundary of the line-of-sight communication area, represents the driving distance of intelligent connected vehicle i in lane l at time T, p th Indicates the threshold value of the flow pressure, Represents the traffic pressure of intelligent connected vehicle i in lane l at time T.

[0096] Maximum deceleration movement distance based on intelligent connected vehicles As well as the first classification condition ω1 and the second classification condition ω2, the intelligent connected vehicles in the AIM system are divided into relay vehicles and ordinary vehicles to identify the nodes that play a key role in optimizing the communication network. The expression is:

[0097]

[0098] in, represents the set of relay vehicles, Represents a collection of common vehicles, and the basic safety information includes vehicle number, location and speed.

[0099] It can be understood that the intelligent connected vehicles in the AIM system are divided into relay vehicles and ordinary vehicles according to the maximum deceleration movement distance and the route traffic pressure, which can identify the nodes that play a key role in optimizing the communication network, thereby realizing centralized networking and distributed vehicle control.

[0100] In step S3, see Figure 2 and Figure 3 ,The process of vehicle trajectory planning for relay vehicles and ordinary vehicles includes:

[0101] S31: constructing a trajectory planning model for each intelligent connected vehicle, wherein the trajectory planning model sets an objective function for each intelligent connected vehicle;

[0102] S32: setting constraints for ordinary vehicles and relay vehicles respectively, minimizing the objective function of each vehicle based on the constraints of ordinary vehicles and relay vehicles, and generating vehicle trajectory planning information for each intelligent connected vehicle.

[0103] In step S31, the process of setting the objective function for each intelligent connected vehicle by the trajectory planning model includes:

[0104] The trajectory planning model takes the sum of the distances between the position of the intelligent connected vehicle at each decision time step and the end point of the intersection as the objective function of the intelligent connected vehicle and calculates the movement distance of the intelligent connected vehicle at each time step. The expression is:

[0105]

[0106] Calculate the movement speed of the intelligent connected vehicle at each time step, the expression is:

[0107]

[0108] in, represents the driving distance of intelligent connected vehicle i in lane l at time T, represents the speed of intelligent connected vehicle i at time t, ΔT TP Indicates the communication cycle.

[0109] Furthermore, based on the constraints of ordinary vehicles and relay vehicles, the process of minimizing the objective function of each vehicle includes:

[0110] Constraints are set for relay vehicles and ordinary vehicles respectively. The following safety distance constraints are set for ordinary vehicles and relay vehicles. The expression is:

[0111]

[0112] The conflict safety distance constraint is set for ordinary vehicles and relay vehicles, and the expression is:

[0113]

[0114] Set the line-of-sight environment constraint for the relay vehicle, the expression is:

[0115]

[0116] Based on the constraints of intelligent connected vehicles, the objective function of each vehicle is minimized, and the expression is:

[0117]

[0118] The objective function of each intelligent connected vehicle is iteratively optimized and solved using a rolling time domain to minimize the objective function of each vehicle. When the intelligent connected vehicle exits the intersection, the optimization result is output and used as the vehicle trajectory planning information of the intelligent connected vehicle.

[0119] in, Indicates the safety distance of the following safety constraint, L v Indicates the length of the vehicle body. represents the slack variable of the car-following safety constraint, represents the safety distance of the conflict safety constraint, F l,h 、F h,l Respectively represent the distance from the conflict point of lane l and lane h to the starting point of each lane, represents the slack variable of the conflict safety constraint, t i,l Indicates the end of the exit lane corresponding to lane l.

[0120] It can be understood that by performing vehicle trajectory planning for relay vehicles and ordinary vehicles, the relay vehicles are subject to additional line-of-sight environment constraints, which can ensure that they are in a line-of-sight communication environment throughout the communication cycle, thereby improving the robustness of the communication network.

[0121] In this embodiment, by dividing the intelligent connected vehicles into relay vehicles and ordinary vehicles based on their basic safety information, it is possible to identify nodes that play a key role in optimizing the communication network, thereby realizing centralized networking and distributed vehicle control; by performing vehicle trajectory planning for relay vehicles and ordinary vehicles, it is possible to ensure that they are in a line-of-sight communication environment throughout the communication cycle, thereby improving the robustness of the communication networking; a backbone network is generated based on the vehicle trajectory planning of all intelligent connected vehicles, and an information flow distribution strategy is formulated based on the backbone network. This phased networking mode can reduce the complexity of the problem and support rapid online optimization of the networking solution, thereby significantly reducing the negative impact of communication instability on the control strategy of the intelligent connected vehicle at the autonomous intersection, and realizing a closed-loop operation mechanism for coordinated optimization of the transportation system and the communication system.

[0122] Embodiment 2:

[0123] This embodiment is further described based on step S3 to step S4 of embodiment 1, which are as follows:

[0124] As a preferred embodiment, in step S3, see Figure 2 and Figure 3 ,The process of generating a backbone network based on vehicle trajectory planning of all intelligent connected vehicles includes:

[0125] The roadside unit will collect the trajectory planning information of all intelligent connected vehicles in the control area and send it to the roadside edge computing unit. The RSU calculates the Euclidean distance between every two intelligent connected vehicles at each time step t based on the trajectory information. The expression is:

[0126]

[0127] in, represents the coordinates of the intelligent connected vehicle i, d i,j Indicates that in the communication cycle Δt RO The average distance between intelligent connected vehicles i and j.

[0128] Based on trajectory planning information and traffic-information flow coupling mechanism (i.e., information is transmitted along the direction of traffic flow), the communication benefits between intelligent connected vehicles are evaluated, and the expression is:

[0129]

[0130] The communication benefits between intelligent connected vehicles are calculated based on the evaluation results. The expression is:

[0131]

[0132] Here, if d i,j No more than the communication radius d r , then the profit function is expressed as d i,j With w i,j Otherwise, the profit function is set to a very large negative number;

[0133] Furthermore, a backbone network optimization model is constructed based on the active and passive edge mechanism. Formula (19) indicates that the total number of transmission edges (i.e., active edges) that intelligent connected vehicle i sends information to other intelligent connected vehicles cannot exceed the threshold This threshold needs to be determined according to the type of intelligent connected vehicle. is the threshold of relay nodes, is the threshold of ordinary nodes, e i,j is a 0-1 variable, which takes the value of 1 when there is an edge between intelligent connected vehicles i and intelligent connected vehicles j, otherwise it takes the value of 0:

[0134]

[0135] Formula (20) indicates that the total number of transmission edges (i.e., passive edges) and active edges of information received by intelligent connected vehicle j from other intelligent connected vehicles cannot exceed the threshold This threshold needs to be determined according to the type of intelligent connected vehicle. is the threshold of relay nodes, is the threshold of a normal node:

[0136]

[0137] The backbone network optimization model sets the objective function of the communication benefits between all intelligent connected vehicles according to the calculated communication benefits, and the expression is:

[0138]

[0139] Solve the objective function to obtain the backbone network;

[0140] Among them, d i,j Indicates that in the communication cycle ΔT RO The average distance between the internal intelligent connected vehicle i and the intelligent connected vehicle i, represents the unit vector in the direction of motion of the intelligent connected vehicle i, represents the vector between intelligent connected vehicle i and intelligent connected vehicle j, e i,j Indicates a 0-1 variable, d r Represents the communication radius, and M represents a very large number.

[0141] It can be understood that the backbone network is generated based on the vehicle trajectory planning of all intelligent connected vehicles, and the information flow distribution strategy is formulated according to the backbone network. This phased networking mode can reduce the complexity of the problem and support the rapid online optimization of the networking solution, thereby significantly reducing the negative impact of communication instability on the control strategy of intelligent connected vehicles at autonomous intersections, and realizing a closed-loop operation mechanism for coordinated optimization of the transportation system and the communication system.

[0142] In some preferred embodiments, an active and passive edge mechanism is used to construct a backbone network optimization model, and the Gurobi solver is used to solve the objective function of the communication benefits between all intelligent connected vehicles.

[0143] It can be understood that the adopted solver Gurobi can better solve the backbone network.

[0144] In step S4, see Figure 2 and Figure 3 The process of formulating an information flow distribution strategy according to the backbone network includes:

[0145] Based on the backbone network, an information flow distribution model is constructed. According to formula (22), the number of edge e at the time step t is calculated. i,j The transmission rate c i,j,t , the expression is:

[0146]

[0147] Among them, B is the communication transmission bandwidth, P V is the transmitted power, and N0 is the thermal noise power received by the receiver.

[0148] In the communication cycle, the edge e i,j The average transmission delay It can be expressed as:

[0149]

[0150] Among them, s p is the size of the message packet, For the edge e i,j The number of transmitted message packets can be calculated using equations (24)-(25), as follows:

[0151]

[0152] in, Indicates the path The number of message packets on It is a binary variable, indicating the path between the source node S and the destination node D of the message packet. Whether to use edge e i,j .

[0153] The information flow allocation model sets the objective function of the total information transmission delay between all intelligent connected vehicles according to the backbone network, and the expression is:

[0154]

[0155] Adopting adaptive large neighborhood search algorithm to solve the objective function of total information transmission delay, so as to minimize the total information transmission delay and obtain the multi-hop transmission path of all information packets;

[0156] Formulate an information flow distribution strategy according to the multi-hop transmission path of all information packets, and each intelligent connected vehicle distributes information flow according to the information flow distribution strategy;

[0157] in, Indicates that through the edge e i,j The number of message packets transmitted, Represents the edge e i,j The average transmission delay.

[0158] In this embodiment, an information flow distribution strategy is formulated based on the backbone network. This phased networking mode can reduce the complexity of the problem and support rapid online optimization of the networking solution, thereby significantly reducing the negative impact of unstable communication on the control strategy of intelligent connected vehicles at autonomous intersections, and realizing a closed-loop operation mechanism for coordinated optimization of the transportation system and the communication system.

[0159] The beneficial effects of the first and second embodiments are as follows:

[0160] The networking node location requirements for improving communication quality are integrated into the intelligent connected vehicle trajectory planning strategy to achieve collaborative optimization of the communication system and the traffic control system. A collaborative computing architecture of the edge (roadside edge computing node) and the end (on-board computing unit) is designed, in which the on-board computing unit handles the intelligent connected vehicle trajectory planning tasks, and the roadside edge computing node handles the communication networking optimization tasks. The two transmit information to each other through the roadside unit to achieve centralized networking and distributed vehicle control. The communication networking process is split into two modules, namely, generating the backbone network and allocating information traffic. This phased networking mode can reduce the complexity of the problem and support rapid online optimization of the networking solution.

[0161] Embodiment three:

[0162] This embodiment provides an autonomous intersection communication network and vehicle trajectory joint optimization system, see Figure 4 The system comprises:

[0163] An acquisition module is used to obtain basic safety information of intelligent connected vehicles;

[0164] A processing module, used for classifying intelligent connected vehicles into relay vehicles and ordinary vehicles according to the basic safety information;

[0165] Vehicle trajectory planning module, used to plan vehicle trajectories for relay vehicles and ordinary vehicles;

[0166] A backbone network generation module, used to generate a backbone network based on vehicle trajectory planning of all intelligent connected vehicles;

[0167] The information flow allocation module is used to formulate an information flow allocation strategy according to the backbone network, and the intelligent connected vehicle allocates information flow according to the information flow allocation strategy.

[0168] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for joint optimization of autonomous intersection communication networking and vehicle trajectory, characterized in that: The method comprises the following steps: Obtaining basic safety information of the intelligent connected vehicle, including vehicle number, location and speed; Classifying intelligent connected vehicles into relay vehicles and ordinary vehicles according to the basic safety information; Perform vehicle trajectory planning for relay vehicles and ordinary vehicles, and generate a backbone network based on the vehicle trajectory planning of all intelligent connected vehicles; Formulate an information flow distribution strategy according to the backbone network, and the intelligent connected vehicle distributes information flow according to the information flow distribution strategy; The process of classifying the intelligent connected vehicles into relay vehicles and ordinary vehicles according to the basic safety information includes: Collect the basic safety information of all intelligent networked vehicles in the control area, and calculate the maximum deceleration movement distance of the intelligent networked vehicles based on the basic safety information. The expression is: Based on the maximum deceleration movement distance of intelligent connected vehicles, intelligent connected vehicles are divided into relay vehicles and ordinary vehicles, and the expression is: Where, ΔT RO Represents the communication cycle, Indicates the communication cycle ΔT RO The moving distance of intelligent connected vehicle i in , i represents the i-th intelligent connected vehicle, l represents the lane, represents the speed of intelligent connected vehicle i at time T, a min represents the maximum deceleration, ω1 represents the first classification coefficient, ω2 represents the second classification coefficient, represents the set of relay vehicles, represents a collection of common vehicles; According to the maximum deceleration movement distance of the intelligent networked vehicle, it is judged whether the intelligent networked vehicle can stop moving within the boundary of the line-of-sight communication area. The expression is: Calculate the route traffic pressure of intelligent connected vehicles, the expression is: Among them, e LA represents the boundary of the line-of-sight communication area, represents the driving distance of intelligent connected vehicle i in lane l at time t, p th Indicates the threshold value of the flow pressure, represents the traffic pressure of intelligent connected vehicle i on lane l at time T; Maximum deceleration movement distance based on intelligent connected vehicles As well as the first classification condition ω1 and the second classification condition ω2, the intelligent connected vehicles in the AIM system are divided into relay vehicles and ordinary vehicles to identify the nodes that play a key role in optimizing the communication network. The expression is: in, represents the set of relay vehicles, Represents the set of common vehicles.

2. The autonomous intersection communication networking and vehicle trajectory joint optimization method according to claim 1 is characterized in that: The process of vehicle trajectory planning for relay vehicles and ordinary vehicles includes: Building a trajectory planning model for each intelligent connected vehicle, wherein the trajectory planning model sets an objective function for each intelligent connected vehicle; Constraints are set for ordinary vehicles and relay vehicles respectively. Based on the constraints of ordinary vehicles and relay vehicles, the objective function of each vehicle is minimized to generate vehicle trajectory planning information for each intelligent connected vehicle.

3. The autonomous intersection communication networking and vehicle trajectory joint optimization method according to claim 2 is characterized in that: The process of setting the objective function for each intelligent connected vehicle in the trajectory planning model includes: The trajectory planning model takes the sum of the distances between the position of the intelligent connected vehicle at each decision time step and the end point of the intersection as the objective function of the intelligent connected vehicle and calculates the movement distance of the intelligent connected vehicle at each time step. The expression is: Calculate the movement speed of the intelligent connected vehicle at each time step, the expression is: in, represents the driving distance of intelligent connected vehicle i in lane l at time t, Represents the speed of intelligent connected vehicle i at time t.

4. The autonomous intersection communication networking and vehicle trajectory joint optimization method according to claim 3 is characterized in that: Based on the constraints of ordinary vehicles and relay vehicles, the process of minimizing the objective function of each vehicle includes: Set the following safety distance constraint for ordinary vehicles and relay vehicles, and the expression is: The conflict safety distance constraint is set for ordinary vehicles and relay vehicles, and the expression is: Set the line-of-sight environment constraint for the relay vehicle, the expression is: Based on the constraints of intelligent connected vehicles, the objective function of each vehicle is minimized, and the expression is: The objective function of each intelligent connected vehicle is iteratively optimized and solved using a rolling time domain to minimize the objective function of each vehicle. When the intelligent connected vehicle exits the intersection, the optimization result is output and used as the vehicle trajectory planning information of the intelligent connected vehicle. Where, ΔT RO Represents the communication cycle, Indicates the safety distance of the following safety constraint, L v Indicates the length of the vehicle body. represents the slack variable of the car-following safety constraint, represents the safety distance of the conflict safety constraint, F l,h 、F h,l Respectively represent the distance from the conflict point of lane l and lane h to the starting point of each lane, represents the slack variable of the conflict safety constraint, r i,l Indicates the end of the exit lane corresponding to lane l.

5. The autonomous intersection communication networking and vehicle trajectory joint optimization method according to claim 4 is characterized in that: The process of generating a backbone network based on vehicle trajectory planning for all intelligent connected vehicles includes: Collect the trajectory planning information of all intelligent connected vehicles in the control area, and evaluate the communication benefits between intelligent connected vehicles based on the trajectory planning information. The expression is: The communication benefits between intelligent connected vehicles are calculated based on the evaluation results. The expression is: Construct a backbone network optimization model. The backbone network optimization model sets the objective function of the communication benefits between all intelligent connected vehicles according to the calculated communication benefits. The expression is: Solve the objective function to obtain the backbone network; Among them, d i,j Indicates that in the communication cycle ΔT RO The average distance between the internal intelligent connected vehicle i and the intelligent connected vehicle j, represents the unit vector in the direction of motion of the intelligent connected vehicle i, represents the vector between intelligent connected vehicle i and intelligent connected vehicle j, e i,j Indicates a 0-1 variable, d r Represents the communication radius, and M represents a very large number.

6. The autonomous intersection communication networking and vehicle trajectory joint optimization method according to claim 5 is characterized in that: The backbone network optimization model is constructed using the active and passive edge mechanism, and the Gurobi solver is used to solve the objective function of the communication benefits between all intelligent connected vehicles.

7. The autonomous intersection communication networking and vehicle trajectory joint optimization method according to claim 5 is characterized in that: The process of formulating an information flow distribution strategy based on the backbone network includes: An information flow distribution model is constructed. The information flow distribution model sets the objective function of the total information transmission delay between all intelligent connected vehicles according to the backbone network. The expression is: Adopting adaptive large neighborhood search algorithm to solve the objective function of total information transmission delay, so as to minimize the total information transmission delay and obtain the multi-hop transmission path of all information packets; Formulate an information flow distribution strategy according to the multi-hop transmission path of all information packets, and each intelligent connected vehicle distributes information flow according to the information flow distribution strategy; in, Indicates that through the edge e i,j The number of message packets transmitted, Represents the edge e i,j The average transmission delay.

8. An autonomous intersection communication networking and vehicle trajectory joint optimization system, the system is used to implement the method according to any one of claims 1 to 7, characterized in that: The system comprises: An acquisition module is used to obtain basic safety information of intelligent connected vehicles; A processing module, used for classifying intelligent connected vehicles into relay vehicles and ordinary vehicles according to the basic safety information; Vehicle trajectory planning module, used to plan vehicle trajectories for relay vehicles and ordinary vehicles; A backbone network generation module, used to generate a backbone network based on vehicle trajectory planning of all intelligent connected vehicles; The information flow allocation module is used to formulate an information flow allocation strategy according to the backbone network, and the intelligent connected vehicle allocates information flow according to the information flow allocation strategy.

Images