A Communication Networking Method and System for Autonomous Intersection Management

Abstract

The present invention relates to the field of wireless communication technologies, and discloses a communication networking method and system for autonomous intersection management, including: connected and autonomous vehicles planning trajectories based on basic safety information; roadside devices generating distance tensors according to the trajectories; the roadside devices converting the distance tensors into delay tensors; constructing a directed graph of intersections with obstructions and no signals, and establishing an adjacency matrix; the roadside devices constructing a revenue matrix and constraint conditions for routing according to the delay tensors and the adjacency matrix, and establishing a first objective function; calculating the node degree centrality of each node in the graph, establishing the induced information of the nodes, and using the induced information to modify the first objective function to obtain a second objective function; calling the modified second algorithm to solve the second objective function, and the roadside devices updating the node degree centrality to obtain an optimal routing scheme; obtaining the final network topology and sending it to the connected and autonomous vehicles. The present invention can reduce communication latency and improve the robustness of communication networking.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and particularly to a communication networking method and system for autonomous intersection management. Background Art

[0002] With the development of society, the number of vehicles on the road is increasing continuously, and their intelligence level is also higher, and the vehicle networking system has developed accordingly. The vehicle networking system can enable information intercommunication among vehicles, between vehicles and roads, and between vehicles and people through mutual communication. It is the core component of the future intelligent transportation system, which can effectively improve road safety, relieve traffic congestion, and promote the realization of driverless driving. Autonomous intersection management is an important part of the vehicle networking. It means that autonomous vehicles can pass through signal-free intersections smoothly without collision. Whether it can operate effectively depends on whether the vehicular ad hoc network can provide real-time and reliable communication services. However, the time delay and malicious attacks of the vehicular ad hoc network will pose serious challenges to the effective control of intelligent connected vehicles, including destroying the stability of cooperative control and increasing the possibility of cascade events. Considering that communication routes are coupled into a network at autonomous intersections, the overall performance should be improved at the network level. Communication routing refers to the path of vehicles through which information passes during the process of a source vehicle transmitting information to a destination vehicle. Other vehicles on the routing except the source vehicle and the destination vehicle are called relay nodes. A robust network consists of communication routes with high communication quality, and high-quality communication routes require the robustness of the network as a guarantee. Therefore, it is necessary to optimize the entire path of a single route in a long time domain, and improve the robustness of the network against malicious attacks self-organizingly during this process. The existing research on the planning of vehicular ad hoc networks only focuses on the optimization of the end-to-end transmission routing of a single piece of information, and mostly searches for routes based on the maximum single-hop gain in a short time domain. The communication delay is large, and it is vulnerable to malicious attacks and has poor robustness.

[0003] In the prior art, a Chinese patent with the patent number CN116390062A discloses a relay selection method and system for a vehicle-to-vehicle multi-hop cooperative communication system. A random geometric model of a vehicle network is constructed, and the position and size of a candidate relay area are determined according to the no-relay probability threshold and the communication service quality of the relay link. All candidate relay vehicles within the relay selection area are obtained to form a candidate relay set, and relay vehicles are selected from the candidate relay vehicles according to a relay selection strategy that ensures the service quality between the source node and the relay vehicle. The source node sends messages to the relay vehicle and the destination node respectively. After the relay vehicle decodes the message, it forwards the message to the next relay vehicle or the destination node according to its relative position with the destination node. When the distance between the relay vehicle and the destination node is greater than the communication range, after one hop, the currently selected relay node is used as the source node, and the process is repeated until the message is forwarded to the destination node. When there are multiple relay nodes (vehicles) between the source node and the destination node in this prior art, information needs to be sent by selecting relay nodes one by one starting from the source node and finally reaching the destination node. Selecting one by one consumes a large amount of time, resulting in a large communication delay. Moreover, this prior art does not consider malicious attacks on the network, resulting in poor robustness of the system. Summary of the Invention

[0004] The primary object of the present invention is to provide a communication networking method for autonomous intersection management, which can reduce the communication delay of communication networking and improve the robustness of communication networking.

[0005] As another object of the present invention, a system adapted to the method based on the foregoing object is also provided.

[0006] To achieve the above object, the present invention provides a communication networking method for autonomous intersection management, including:

[0007] Step S1: The connected and autonomous vehicle sends its own basic safety information to other connected and autonomous vehicles with interaction requirements, and the connected and autonomous vehicle plans its trajectory in the next time domain according to the basic safety information.

[0008] Step S2: The connected and autonomous vehicle sends the trajectory to the roadside device, and the roadside device obtains the distance tensor between the connected and autonomous vehicles according to the trajectory. Each element in the distance tensor represents the relative distance between two connected and autonomous vehicles.

[0009] Step S3: The roadside device uses the lognormal shadowing model to describe the successful communication probability of the connected and autonomous vehicles at intersections with occlusion and no signal, and converts each element in the distance tensor from the relative distance between two connected and autonomous vehicles into the time delay generated by the establishment of communication between two autonomous vehicles, obtaining a time delay tensor.

[0010] Step S4: Construct a directed graph of the occluded intersection without signals, and establish an adjacency matrix according to the directed graph;

[0011] Step S5: The roadside device constructs a revenue matrix and constraint conditions for the route according to the delay tensor and the adjacency matrix, and establishes a first objective function using the revenue matrix and the constraint conditions. Among them, the revenue matrix is as follows:

[0012]

[0013] The constraint conditions are as follows:

[0014]

[0015] Among them respectively represent the starting point and the ending point of the route , is an element of the adjacency matrix , which is a binary variable. Its value of 1 indicates that information is transmitted between the connected intelligent vehicle and the connected intelligent vehicle , and a value of 0 indicates that information cannot be transmitted, as shown in Equation (1); Equation (2) limits the maximum number of multi-hop relays in the route , is the threshold of the number of hops; Equation (3) indicates that the route starts from point O and sends information to other connected intelligent vehicles, and the information can only be received by one node; Equation (4) indicates that the information is finally received by the destination point D after transmission, and point D only receives information from one connected intelligent vehicle; Equation (5) indicates that the information sent from the connected intelligent vehicle can be received by at most one connected intelligent vehicle and cannot be sent to multiple connected intelligent vehicles; Equation (6) indicates that the connected intelligent vehicle can receive information from at most one connected intelligent vehicle and cannot receive information from multiple connected intelligent vehicles; Equation (7) constrains the continuity of the element values of the adjacency matrix , among which is a very large constant; the first objective function is determined by the following formula:

[0016]

[0017] Among them, represents the revenue matrix, represents the delay, represents the element of the adjacency matrix ;

[0018] Step S6: Calculate the node degree centrality of each node in the directed graph, establish the induced information of the nodes according to the node degree centrality, and use the induced information to modify the first objective function to obtain a second objective function. Specifically, step S6 includes the following steps:

[0019] Step S6.1: Calculate the node degree centrality of each node according to the directed graph. The node degree centrality represents the number of times a node is selected as a routing relay node, and the node centrality is determined by the following formula:

[0020] where

[0021]

[0022] indicates that there is a communication relationship between node and node in the communication network and each node represents a connected autonomous vehicle;

[0023] Step 6.2: Establish the induced information of the nodes according to the node degree centrality. The induced information is determined by the following formula:

[0024]

[0025] where represents a variable constant parameter;

[0026] Step 6.3: Use the induced information to modify the first objective function to obtain a second objective function. The second objective function is determined by the following formula:

[0027]

[0028] where represents the induced information;

[0029] Step S7: Construct a first algorithm based on the induced information, use the first algorithm to modify the second algorithm, call the modified second algorithm to solve the second objective function, and the roadside device updates the node degree centrality of the directed graph to obtain an optimal routing scheme. The first algorithm is the Upper Confidence Bound (UCB) algorithm, and the second algorithm is the Monte Carlo Tree Search (MCTS) algorithm;

[0030] Step S8: The roadside device superimposes each optimal routing graph to obtain the vehicular ad hoc network topology of the occluded and signal-free intersection, and sends the topology to the connected autonomous vehicles.

[0031] Further, the basic safety information in step S1 includes the time step and the identity of the connected autonomous vehicle ​

[0032] ID and location.

[0033] Furthermore, step S2 specifically includes:

[0034] Step S2.1: The roadside device calculates the relative distance between connected autonomous vehicles according to the trajectory ; The calculation formula is as follows:

[0035]

[0036] Wherein, represents a connected autonomous vehicle, N represents the total number of connected autonomous vehicles at the intersection with occlusion and no signal, T represents the intervention time of the roadside device, and respectively represent the coordinates of two connected autonomous vehicles, is the time length of the roadside device intervention period;

[0037] Step S2.2: Represent the relative distance between connected autonomous vehicles at each time step t as a two-dimensional distance matrix;

[0038] Step S2.3: Integrate the two-dimensional distance matrix along the time dimension to obtain the distance tensor.

[0039] Furthermore, the successful communication probability in step S3 is determined by the following formula:

[0040]

[0041] Wherein, represents the connected autonomous vehicle successful communication probability, connected autonomous vehicle the received power level between, represents the received power threshold, represents the transmission range in the absence of shadow, is a Gaussian random variable with a mean of zero and a unit variance of zero the probability that is greater than z,

[0042] the connected autonomous vehicle the received power level between is determined by the following formula:

[0043]

[0044] Wherein, represents the Euclidean distance node between the transmitter and the receiver, represents the transmission power, At the reference distance the reference path loss where represents the path loss exponent and represents a Gaussian random variable with zero mean and standard deviation

[0045] Further, the constraint conditions described in step S5 include the physical relationship of nodes being selected as routing relay nodes, the routing length, and the routing integrity

[0046] Further, in step S7, the node degree centrality is updated using the following formula

[0047] .

[0048] To achieve another object of the present invention, the present invention provides a communication networking system for autonomous intersection management, including

[0049] The first module: The connected autonomous vehicle sends its own basic safety information to other connected autonomous vehicles with interaction requirements, and the connected autonomous vehicle plans its next-period trajectory according to the basic safety information

[0050] The second module: The connected autonomous vehicle sends the trajectory to the roadside device, and the roadside device obtains the distance tensor between the connected autonomous vehicles according to the trajectory, and each element in the distance tensor represents the relative distance between two connected autonomous vehicles

[0051] The third module: The roadside device uses the log-normal shadow model to describe the successful communication probability of the connected autonomous vehicles at the occluded and signal-free intersection, and converts each element in the distance tensor from the relative distance between two connected autonomous vehicles into the time delay generated by the establishment of communication between two autonomous vehicles, and obtains the time delay tensor

[0052] The fourth module: Construct a directed graph of the occluded and signal-free intersection, and establish an adjacency matrix according to the directed graph

[0053] The fifth module: The roadside device constructs the revenue matrix and constraint conditions of the route according to the time delay tensor and the adjacency matrix, and uses the revenue matrix and constraint conditions to establish the first objective function, where the revenue matrix is as follows

[0054]

[0055] The constraint conditions are as follows

[0056]

[0057] where respectively represent the route The starting point and the ending point is the adjacency matrix element, which is a binary variable. Its value of 1 indicates that information is transmitted between the connected intelligent vehicle and the connected intelligent vehicle. A value of 0 indicates that information cannot be transmitted, as shown in Equation (1); Equation (2) limits the maximum number of multi-hop relays in the route, is the threshold of the number of hops; Equation (3) indicates that the route starts from point O and sends information to other connected intelligent vehicles, and the information can only be received by one node; Equation (4) indicates that the information is finally received by the destination point D after transmission, and point D only receives information from one connected intelligent vehicle; Equation (5) indicates that the information sent from the connected intelligent vehicle can be received by at most one connected intelligent vehicle and cannot be sent to multiple connected intelligent vehicles; Equation (6) indicates that the connected intelligent vehicle can receive information from at most one connected intelligent vehicle and cannot receive information from multiple connected intelligent vehicles; Equation (7) constrains the continuity of the element values of the adjacency matrix , where is a very large constant; The first objective function is determined by the following formula:

[0058]

[0059] Among them, represents the revenue matrix, represents the time delay, represents the element of the adjacency matrix ;

[0060] Sixth module: Calculate the node degree centrality of each node in the directed graph, establish the induced information of the nodes according to the node degree centrality, and use the induced information to modify the first objective function to obtain the second objective function. Specifically, step S6 specifically includes:

[0061] Step S6.1: Calculate the node degree centrality of each node according to the directed graph. The node

[0062] degree centrality represents the number of times a node is selected as a routing relay node. The node centrality is determined by the following formula:

[0063]

[0064] Among them, represents that on the communication network , there is a communication relationship between node and node . Each node represents a connected autonomous vehicle;

[0065] Step 6.2: Establish the induced information of the nodes according to the node degree centrality, and the induced information is determined by the following formula:

[0066]

[0067] where represents a variable constant parameter;

[0068] Step 6.3: Modify the first objective function using the induced information to obtain a second objective function, and the second objective function is determined by the following formula:

[0069]

[0070] where, represents the induced information;

[0071] Seventh module: Construct a first algorithm based on the induced information, use the first algorithm to modify the second algorithm, call the modified second algorithm to solve the second objective function, the roadside device updates the node degree centrality of the directed graph, and obtains an optimal routing scheme. The first algorithm is the upper confidence bound algorithm, and the second algorithm is the Monte Carlo tree search algorithm;

[0072] Eighth module: The roadside device superimposes each of the optimal routing graphs to obtain the vehicle ad hoc network topology of the occluded and signal-free intersection, and sends the topology to the connected autonomous vehicle.

[0073] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0074] The present invention generates the trajectory in the next time domain by using basic safety information such as time step, the identity ID and position of the connected autonomous vehicle, and then uses the trajectory for the communication node planning of the connected autonomous vehicle, realizing the long-time domain and full-path optimization of the communication routing between vehicles, and can avoid the problem of large communication delay caused by searching for routing through single-hop in the prior art, thereby reducing the communication delay; it also introduces the node degree centrality and uses it to construct the induced information, and then uses the induced information to optimize the performance of communication routing and communication networking, and can effectively resist malicious attacks, thereby improving the robustness of communication networking. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 is a flowchart of a communication networking method for autonomous intersection management according to an embodiment of the present invention;

[0076] Figure 2 is a system block diagram of a communication networking system for autonomous intersection management according to an embodiment of the present invention;

[0077] Figure 3 It is a scene diagram of an urban autonomous intersection for a communication networking method for autonomous intersection management according to an embodiment of the present invention. Specific embodiments

[0078] The following will further describe in detail the specific embodiments of the present invention in conjunction with the accompanying drawings and embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0079] Embodiment 1

[0080] As Figure 1 shown, a communication networking method for autonomous intersection management according to a preferred embodiment of an embodiment of the present invention includes:

[0081] Step S1: The connected and autonomous vehicle sends its own basic safety information to other connected and autonomous vehicles with interaction requirements, and the connected and autonomous vehicle plans its next trajectory within a time domain according to the basic safety information;

[0082] Step S2: The connected and autonomous vehicle sends the trajectory to the roadside device, and the roadside device obtains the distance tensor between the connected and autonomous vehicles according to the trajectory. Each element in the distance tensor represents the relative distance between two connected and autonomous vehicles;

[0083] Step S3: The roadside device uses the log-normal shadow model to describe the successful communication probability of the connected and autonomous vehicles at the intersection with occlusion and no signal, and converts each element in the distance tensor from the relative distance between two connected and autonomous vehicles into the time delay generated by the establishment of communication between the two autonomous vehicles to obtain the time delay tensor;

[0084] Step S4: Construct a directed graph of the intersection with occlusion and no signal, and establish an adjacency matrix according to the directed graph;

[0085] Step S5: The roadside device constructs the revenue matrix and constraint conditions of the routing according to the time delay tensor and the adjacency matrix, and uses the revenue matrix and constraint conditions to establish the first objective function;

[0086] Step S6: Calculate the node degree centrality of each node in the directed graph, establish the induced information of the node according to the node degree centrality, and use the induced information to modify the first objective function to obtain the second objective function;

[0087] Step S7: Construct a first algorithm based on the induced information, use the first algorithm to modify the second algorithm, call the modified second algorithm to solve the second objective function, and the roadside device updates the node degree centrality of the directed graph to obtain the optimal routing scheme;

[0088] Step S8: The roadside device superimposes the graphs of each optimal route to obtain a vehicular ad hoc network topology of intersections with obstructions and no signal, and sends the topology to the connected autonomous vehicle.

[0089] In this embodiment, by generating the trajectory in the next time domain from basic safety information such as time step, the identity ID and location of the connected autonomous vehicle, and then using the trajectory for the communication node planning of the connected autonomous vehicle, the long-time domain and full-path optimization of the communication routing between vehicles are realized, which can avoid the problem of large communication delay caused by searching for routes through single-hop in the prior art, thereby reducing the communication delay; by also introducing node degree centrality and using it to construct induced information, and then using the induced information to optimize the performance of communication routing and communication networking, malicious attacks can be effectively resisted, thereby improving the robustness of the communication network.

[0090] Embodiment 2

[0091] As Figure 1 and 3 shown, the flowchart and the urban autonomous intersection scenario diagram of a communication networking method for autonomous intersection management according to a preferred embodiment of the present invention are specifically as follows:

[0092] Step S1: The connected autonomous vehicle sends its own basic safety information to other connected autonomous vehicles with interaction requirements, and the connected autonomous vehicle plans its trajectory in the next time domain according to the basic safety information.

[0093] In this embodiment, the basic safety information includes time step, the identity ID and location of the connected autonomous vehicle. The connected autonomous vehicle plans its trajectory in the next time domain according to the time step, the identity ID and location of the connected autonomous vehicle.

[0094] Step S2: The connected autonomous vehicle sends the trajectory to the roadside device, and the roadside device obtains the distance tensor between the connected autonomous vehicles according to the trajectory, and each element in the distance tensor represents the relative distance between two connected autonomous vehicles.

[0095] In this embodiment, step S2 specifically includes:

[0096] Step S2.1: The roadside device calculates the relative distance between the connected autonomous vehicles according to the trajectory; the calculation formula is as follows:

[0097] Step S2.1: The roadside device calculates the relative distance between the connected autonomous vehicles according to the trajectory ; the calculation formula is as follows:

[0098]

[0099] where Denote the connected autonomous vehicle, N denotes the total number of connected autonomous vehicles at the occluded and signal - free intersection, T denotes the intervention time of the roadside device, and respectively represent the coordinates of two connected autonomous vehicles, is the time length of the roadside device intervention period;

[0100] Step S2.2: Represent the relative distance between connected autonomous vehicles at each time step t as a two - dimensional distance matrix, as follows:

[0101]

[0102] Step S2.3: Integrate the two - dimensional distance matrix along the time dimension to obtain the distance tensor, as follows:

[0103]

[0104] Step S3: The roadside device uses the log - normal shadowing model to describe the successful communication probability of the connected autonomous vehicles at the occluded and signal - free intersection, and converts each element in the distance tensor from the relative distance between two connected autonomous vehicles into the time delay generated by the establishment of communication between two autonomous vehicles, obtaining the time - delay tensor;

[0105] In this embodiment, the successful communication probability is determined by the following formula:

[0106]

[0107] where, denotes the connected autonomous vehicle successful communication probability, connected autonomous vehicle the received power level between, denotes the received power threshold, denotes the transmission range in the absence of shadow, is a Gaussian random variable with a mean of zero and a unit variance of zero the probability that is greater than z,

[0108] connected autonomous vehicle the received power level between is determined by the following formula:

[0109]

[0110] where, denotes the Euclidean distance node between the transmitter and the receiver, denotes the transmission power, at the reference distance The reference path loss on represents the path loss exponent, represents a Gaussian random variable with zero mean and standard deviation.

[0111] The time delay is determined by the following formula:

[0112]

[0113] where represents the expected time delay for information to propagate between two nodes.

[0114] Step S4: Construct a directed graph of the intersection with occlusion and no signal, and establish an adjacency matrix according to the directed graph;

[0115] In this embodiment, the nodes of the directed graph are all intelligent connected vehicles in the autonomous intersection management system. The communication connection relationship between vehicles is represented by an adjacency matrix, and the adjacency matrix is as follows:

[0116]

[0117] Adjacency matrix can be split into the sum of the adjacency matrices of each route, that is , where represents the routing topology formed by the communication OD.

[0118] Step S5: The roadside device constructs the revenue matrix and constraint conditions of the route according to the time delay tensor and the adjacency matrix, and establishes the first objective function using the revenue matrix and the constraint conditions;

[0119] In this embodiment, the constraint conditions include the physical relationship of the node being selected as the routing relay node, the routing length, and the routing integrity. In this embodiment, the revenue matrix and constraint conditions of the route are constructed according to the Hadamard product of the time delay tensor and the adjacency matrix, and the revenue matrix is as follows:

[0120]

[0121] The constraint conditions are as follows:

[0122]

[0123] where respectively represent the starting point and the ending point of the route , is an element of the adjacency matrix , which is a binary variable, and its value of 1 indicates that the intelligent connected vehicle and the intelligent connected vehicle Transmit information. A value of 0 indicates that information cannot be transmitted, as shown in Equation (1); Equation (2) limits the maximum number of multi-hop relays in the route and is the limit of the number of hops; is the threshold of the number of hops; Equation (3) indicates that the route starts from point O and sends information to other connected intelligent vehicles, and the information can only be received by one node; Equation (4) indicates that the information is finally received by the destination point D after transmission, and point D only receives information from one connected intelligent vehicle. Equation (5) indicates that the information sent from a connected intelligent vehicle can be received by at most one connected intelligent vehicle and cannot be sent to multiple connected intelligent vehicles; Equation (6) indicates that a connected intelligent vehicle can receive information from at most one connected intelligent vehicle and cannot receive information from multiple connected intelligent vehicles; Equation (7) constrains the continuity of the element values of the adjacency matrix , where is a very large constant. The first objective function is determined by the following formula:

[0124]

[0125] where represents the revenue matrix, represents the time delay, represents the adjacency matrix element.

[0126] Step S6: Calculate the node degree centrality of each node in the directed graph, establish the induced information of the nodes according to the node centrality, and modify the first objective function using the induced information to obtain the second objective function;

[0127] In this embodiment, Step S6 specifically includes:

[0128] Step S6.1: Calculate the node degree centrality of each node according to the directed graph, and the node centrality

[0129] represents the number of times a node is selected as a routing relay node, and the node centrality is determined by the following formula:

[0130]

[0131] where represents that on the communication network , there is a communication relationship between node and node , and each node represents a connected autonomous vehicle;

[0132] Step 6.2: Establish the induced information of the nodes according to the node degree centrality, and the induced information is determined by the following formula:

[0133]

[0134] Among them represents a variable constant parameter, which is taken as 0.1 in this embodiment;

[0135] Step 6.3: Modify the first objective function using the induced information to obtain a second objective function, where the second objective function is determined by the following formula:

[0136]

[0137] Among them, represents the induced information.

[0138] Step S7: Construct a first algorithm based on the induced information, use the first algorithm to modify the second algorithm, call the modified second algorithm to solve the second objective function, and the roadside device updates the node degree centrality of the directed graph to obtain an optimal routing scheme;

[0139] In this embodiment, the first algorithm is the Upper Confidence Bound (UCB) algorithm, and the second algorithm is the Monte Carlo Tree Search (MCTS) algorithm. The second objective function is determined by the following formula:

[0140]

[0141] And in this embodiment, the following formula is used to update the node degree centrality:

[0142]

[0143] Step S8: The roadside device superimposes the graphs of each optimal route to obtain a Vehicular Ad Hoc Network (VANET) topology with occluded and signal-free intersections, and sends the topology to the connected autonomous vehicles.

[0144] In this embodiment, finally, the roadside device sends the connected topology to each connected autonomous vehicle.

[0145] Embodiment III

[0146] As Figure 3 described above, a communication networking system for autonomous intersection management according to an embodiment of the present invention includes:

[0147] First module: The connected autonomous vehicle sends its own basic safety information to other connected autonomous vehicles with interaction requirements, and the connected autonomous vehicle plans its next-period trajectory according to the basic safety information;

[0148] Second module: The connected autonomous vehicle sends the trajectory to the roadside device, and the roadside device obtains the distance tensor between the connected autonomous vehicles according to the trajectory. Each element in the distance tensor represents the relative distance between two connected autonomous vehicles;

[0149] Third module: The roadside device uses the log-normal shadow model to describe the successful communication probability of the connected autonomous vehicles at the intersection with occlusion and no signal, and converts each element in the distance tensor from the relative distance between two connected autonomous vehicles into the time delay generated by the establishment of communication between two autonomous vehicles to obtain a time delay tensor;

[0150] Fourth module: Construct a directed graph of the intersection with occlusion and no signal, and establish an adjacency matrix according to the directed graph;

[0151] Fifth module: The roadside device constructs a revenue matrix and constraint conditions for routing according to the time delay tensor and the adjacency matrix, and establishes a first objective function using the revenue matrix and constraint conditions;

[0152] Sixth module: Calculate the node degree centrality of each node in the directed graph, establish the induced information of the node according to the node centrality, and modify the first objective function using the induced information to obtain a second objective function;

[0153] Seventh module: Construct a first algorithm based on the induced information, use the first algorithm to modify the second algorithm, call the modified second algorithm to solve the second objective function, and the roadside device updates the node degree centrality of the directed graph to obtain an optimal routing scheme;

[0154] Eighth module: The roadside device superimposes the graphs of each optimal route to obtain the vehicle ad hoc network topology of the intersection with occlusion and no signal, and sends the topology to the connected autonomous vehicle.

[0155] In this embodiment, by generating the trajectory in the next time domain from basic safety information such as time steps, the identity ID and position of the connected autonomous vehicle, and then using the trajectory for the communication node planning of the connected autonomous vehicle, the long-time domain and full-path optimization of the communication routing between vehicles are realized, which can avoid the problem of large communication delay caused by searching for routes through single-hop in the prior art, thereby reducing the communication delay; by introducing the node degree centrality and using it to construct the induced information, and then using the induced information to optimize the performance of communication routing and communication networking, malicious attacks can be effectively resisted, thereby improving the robustness of the communication networking.

[0156] In summary, the embodiments of the present invention provide a communication networking method and system for autonomous intersection management. By generating the trajectory in the next time domain based on basic safety information such as time steps, the identity ID and position of connected autonomous vehicles, and then using the trajectory for the communication node planning of connected autonomous vehicles, the long-time domain and full-path optimization of the communication routing between vehicles are achieved, which can avoid the problem of large communication delay caused by searching for routes through single-hop in the prior art, thereby reducing the communication delay. Additionally, by introducing node degree centrality and using it to construct induction information, and then using the induction information to optimize the performance of communication routing and communication networking, malicious attacks can be effectively resisted, thereby improving the robustness of the communication networking.

[0157] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and replacements can be made, and these improvements and replacements should also be regarded as the protection scope of the present invention.

Claims

1. A communication networking method for autonomous intersection management, characterized in that, Including: Step S1: The connected autonomous vehicle sends its own basic safety information to other connected autonomous vehicles with interaction requirements, and the connected autonomous vehicle plans its next trajectory within the time domain according to the basic safety information; Step S2: The connected autonomous vehicle sends the trajectory to the roadside device, and the roadside device obtains the distance tensor between the connected autonomous vehicles according to the trajectory, and each element in the distance tensor represents the relative distance between two connected autonomous vehicles; Step S3: The roadside device uses the lognormal shadow model to describe the successful communication probability of the connected autonomous vehicles at the intersection with occlusion and no signal, and converts each element in the distance tensor from the relative distance between two connected autonomous vehicles into the time delay generated by the establishment of communication between two autonomous vehicles to obtain a time delay tensor; Step S4: Construct a directed graph of the intersection with occlusion and no signal, and establish an adjacency matrix according to the directed graph; Step S5: The roadside device constructs a revenue matrix and constraint conditions for routing according to the time delay tensor and the adjacency matrix, and establishes a first objective function using the revenue matrix and the constraint conditions, where the revenue matrix is as follows: The constraint conditions are as follows: Among them respectively represent the start point and the end point of the route , is an element of the adjacency matrix which is a binary variable. Its value of 1 indicates that information is transmitted between the intelligent connected vehicle and the intelligent connected vehicle . A value of 0 indicates that information cannot be transmitted, as shown in Equation (1); Equation (2) limits the maximum number of multi-hop relays in the route , being the threshold of the number of hops; Equation (3) indicates that the route starts from point O and sends information to other intelligent connected vehicles, and the information can only be received by one node; Equation (4) indicates that the information is finally received by the destination point D after transmission, and point D only receives information from one intelligent connected vehicle; Equation (5) indicates that the information sent from the intelligent connected vehicle can be received by at most one intelligent connected vehicle and cannot be sent to multiple intelligent connected vehicles; Equation (6) indicates that the intelligent connected vehicle can receive information from at most one intelligent connected vehicle and cannot receive information from multiple intelligent connected vehicles; Equation (7) constrains the continuity of the element values of the adjacency matrix , where is a very large constant; The first objective function is determined by the following formula: Among them, represents the revenue matrix, represents the time delay, represents the elements of the adjacency matrix ; Step S6: Calculate the node degree centrality of each node in the directed graph, establish the induced information of the node according to the node degree centrality, and modify the first objective function using the induced information to obtain a second objective function. Specifically, step S6 specifically includes: Step S6.1: Calculate the node degree centrality of each node according to the directed graph, and the node degree centrality represents the number of times the node is selected as a routing relay node, and the node centrality is determined by the following formula: Among them, it means that on the communication network there is a communication relationship between node and node and each node represents a connected and autonomous vehicle; Step 6.2: Establish the induced information of the node according to the node degree centrality, and the induced information is determined by the following formula: wherein represents a variable constant parameter; Step 6.3: Modify the first objective function using the induced information to obtain a second objective function, and the second objective function is determined by the following formula: Among them, represents induced information; Step S7: Construct a first algorithm based on the induced information, use the first algorithm to modify a second algorithm, call the modified second algorithm to solve the second objective function, and the roadside device updates the node degree centrality of the directed graph to obtain an optimal routing scheme. The first algorithm is the upper confidence bound algorithm, and the second algorithm is the Monte Carlo tree search algorithm; Step S8: The roadside device superimposes each graph of the optimal routing to obtain the vehicle ad hoc network topology of the intersection with occlusion and no signal, and sends the topology to the connected autonomous vehicle.

2. The communication networking method for autonomous intersection management according to claim 1, characterized in that, The basic safety information in step S1 includes the time step, the identity ID of the connected autonomous vehicle, and the location.

3. The communication networking method for autonomous intersection management according to claim 1, characterized in that, Step S2 specifically includes: Step S2.1: The roadside device calculates the relative distance between connected autonomous vehicles based on the trajectory ; The calculation formula is as follows: Among them, represents a connected and autonomous vehicle, N represents the total number of connected and autonomous vehicles at the intersection with occlusion and no signal, T represents the intervention time of the roadside device, and respectively represent the coordinates of two connected and autonomous vehicles, is the time length of the roadside device intervention cycle; Step S2.2: Represent the relative distance between connected autonomous vehicles at each time step t as a two-dimensional distance matrix; Step S2.3: Integrate the two-dimensional distance matrix along the time dimension to obtain the distance tensor.

4. The communication networking method for autonomous intersection management according to claim 1, characterized in that, The successful communication probability in step S3 is determined by the following formula: Among them, represents the successful communication probability of connected and autonomous vehicles connected and autonomous vehicles the received power level between represents the received power threshold, represents the transmission range without shadow, is a Gaussian random variable with a mean of zero and a unit variance of zero the probability greater than z The connected and autonomous vehicle The received power level between is determined by the following formula: Among them, represents the Euclidean distance node between the transmitter and the receiver, represents the transmission power, at the reference distance the reference path loss, represents the path loss exponent, represents a zero-mean and standard deviation Gaussian random variable.

5. A communication networking method for autonomous intersection management according to claim 4, wherein, The constraint conditions described in step S5 include the physical relationship of a node being selected as a routing relay node, the routing length, and the routing integrity.

6. A communication networking method for autonomous intersection management according to claim 5, wherein, In step S7, the node degree centrality is updated using the following formula: 。 7. A communication networking system for autonomous intersection management, wherein, Including: The first module: The connected autonomous vehicle sends its own basic safety information to other connected autonomous vehicles with interaction requirements, and the connected autonomous vehicle plans its next-period trajectory according to the basic safety information. The second module: The connected autonomous vehicle sends the trajectory to the roadside device, and the roadside device obtains the distance tensor between the connected autonomous vehicles according to the trajectory, and each element in the distance tensor represents the relative distance between two connected autonomous vehicles. The third module: The roadside device uses the log-normal shadow model to describe the successful communication probability of the connected autonomous vehicles at the occluded and signal-free intersection, and converts each element in the distance tensor from the relative distance between two connected autonomous vehicles into the time delay generated by the establishment of communication between two autonomous vehicles to obtain a time delay tensor. The fourth module: Construct a directed graph of the occluded and signal-free intersection, and establish an adjacency matrix according to the directed graph. The fifth module: The roadside device constructs a revenue matrix and constraint conditions for the routing according to the time delay tensor and the adjacency matrix, and uses the revenue matrix and constraint conditions to establish a first objective function. Among them, the revenue matrix is as follows: The constraint conditions are as follows: Among them respectively represent the start point and the end point of the route ; is an element of the adjacency matrix and is a binary variable. When its value is 1, it means that information is transmitted between the intelligent connected vehicle and the intelligent connected vehicle . When the value is 0, it means that information cannot be transmitted, as shown in Equation (1); Equation (2) limits the maximum number of multi-hop relays in the route ; is the threshold of the hop count; Equation (3) means that the route starts from point O and sends information to other intelligent connected vehicles, and the information can only be received by one node; Equation (4) means that the information is finally received by the destination point D after transmission, and point D only receives information from one intelligent connected vehicle; Equation (5) means that the information sent from the intelligent connected vehicle can be received by at most one intelligent connected vehicle and cannot be sent to multiple intelligent connected vehicles; Equation (6) means that the intelligent connected vehicle can receive information from at most one intelligent connected vehicle and cannot receive information from multiple intelligent connected vehicles; Equation (7) constrains the continuity of the element values of the adjacency matrix , where is a very large constant; The first objective function is determined by the following formula: Among them, represents the revenue matrix, represents the time delay, represents the adjacency matrix element; The sixth module: Calculate the node degree centrality of each node in the directed graph, establish the induced information of the node according to the node degree centrality, and use the induced information to modify the first objective function to obtain a second objective function. Specifically, step S6 specifically includes: Step S6.1: Calculate the node degree centrality of each node according to the directed graph. The node degree centrality represents the number of times a node is selected as a routing relay node, and the node centrality is determined by the following formula: Among them, it means that on the communication network there is a communication relationship between node and node , and each node represents a connected autonomous vehicle; Step 6.2: Establish the induced information of the node according to the node degree centrality. The induced information is determined by the following formula: wherein represents a variable constant parameter; Step 6.3: Use the induced information to modify the first objective function to obtain a second objective function. The second objective function is determined by the following formula: Among them, represents induced information; The seventh module: Construct a first algorithm based on the induced information, use the first algorithm to modify the second algorithm, call the modified second algorithm to solve the second objective function, and the roadside device updates the node degree centrality of the directed graph to obtain an optimal routing scheme. The first algorithm is the upper confidence bound algorithm, and the second algorithm is the Monte Carlo tree search algorithm. The eighth module: The roadside device superimposes the graphs of each optimal route to obtain the vehicle ad hoc network topology of the occluded and signal-free intersection, and sends the topology to the connected autonomous vehicle.

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