V2V Communication Broadcast Routing Algorithm Based on Combined Weights

Abstract

The present invention belongs to the technical field of vehicle networking, and specifically relates to a V2V communication broadcast routing algorithm based on combined weights. The steps include constructing a two-way lane vehicle networking system model based on vehicle ad hoc networks; determining the communication metrics of vehicle networking communication scenarios; constructing a routing algorithm: randomly determining a vehicle source node and a destination node. For the information to be transmitted, according to the methods of broadcasting and constructing a neighbor table, find the vehicles within the communication range, start broadcasting from the source node to find relay nodes outward. Each node existing within the communication range is a relay node, and keep searching from the source node until the destination node is found and the search stops; determine the weights and weight coefficients involved in the routing algorithm, and then establish the weight coefficients according to the analytic hierarchy process, select the most suitable relay node for transmitting information as the optimal path, and transmit the information to the destination node. The present invention can improve the routing selection efficiency and ensure the stability of the link on the premise of ensuring the information transmission speed record.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle networking, and specifically relates to a V2V communication broadcast routing algorithm based on combined weights. Background Art

[0002] In the context of the combination of 5G networks with mobile Internet and the Internet of Things, the era of "Internet of Everything" is becoming a reality. In particular, the progress of wireless communication and automotive technologies has pushed the research on intelligent transportation systems (ITS) to a new height. However, due to the low selection efficiency of traditional routing algorithms, they are gradually unable to solve actual vehicle problems.

[0003] Among the existing routing methods, Karimi et al. proposed the Predictive Geographic Routing Protocol (PGRP) method, which uses the direction and angle information of vehicles to predict the future network topology and assigns weights to neighbors according to the prediction results. Lochert proposed a method for identifying the best path from source to destination. This method uses the Dijkstra algorithm and street information to assign weights to each intersection and adopts a greedy forwarding strategy between adjacent intersections. Ling et al. proposed the RCR protocol, which uses digital map knowledge to divide roads into different sections, selects gateways according to vehicle positions, establishes a routing table containing section information, and finally adopts a response routing strategy to establish a route. Bing et al. proposed an end-to-end edge cloud intelligent architecture. When a data packet is transmitted to an intersection, the roadside unit (RSU) receives the data packet, and then continues with subsequent path selection through reinforcement learning by sensing the surrounding environment information.

[0004] However, the existing routing algorithms consider too few weight factors during path selection, resulting in low routing selection efficiency. And under the selection of traditional algorithms, nodes are prone to falling into local optimality, that is, always selecting the node closest to the destination node as a candidate node. In this case, it is easy to cause greedy forwarding failure and the nodes to fall into a routing hole state. And the excessive sensing of the surrounding environment information by the RSU will lead to an increase in system overhead and cause link instability. Summary of the Invention

[0005] In view of the above deficiencies in the prior art, the purpose of the present invention is to provide a V2V communication broadcast routing algorithm based on combined weights, which can improve the routing selection efficiency and ensure the stability of the link on the premise of ensuring the information transmission speed.

[0006] To achieve the above purpose, the present invention provides a V2V communication broadcast routing algorithm based on combined weights, including the following steps:

[0007] S1. Construct a two-way lane vehicle networking system model based on vehicle ad hoc networks;

[0008] S2. Determine the communication metrics for the vehicle networking communication scenario, including the signal-to-interference-plus-noise ratio (SINR), the information transmission rate between vehicles acting as relay nodes, and the vehicle driving speed.

[0009] S3. Construct a routing algorithm: In the two-way lane vehicle networking system model, randomly determine the vehicle source node and destination node. For the information to be transmitted, find the vehicles within the communication range according to the methods of broadcasting and constructing a neighbor table. Between the source node and the destination node, start broadcasting from the source node to find relay nodes outward. Each node existing within the communication range is a relay node. Keep searching from the source node until the destination node is found and then stop.

[0010] S4. Determine the weights and weight coefficients involved in the routing algorithm, and then establish the weight coefficients according to the analytic hierarchy process. Select the most suitable relay node for transmitting information as the optimal path and transmit the information to the destination node.

[0011] As a preferred solution of the present invention, in S1, the construction process is as follows: Assume that the geographical location of each vehicle is known, and each vehicle in the entire vehicular ad hoc network has a unique ID. The lane adopts a two-way six-lane layout, and the initial positions of the vehicles on the road are randomly distributed according to the Poisson distribution; the average vehicle speed , The variation range of is ; The running directions of the vehicles on the road are 0, , , which represent due north, due north, due south, and due west directions respectively; The running directions of the vehicles at intersections are: , , , , which represent northeast, southeast, southwest, and northwest directions respectively; The vehicles are under the command of traffic lights; The channel model is used to control and describe the transmission characteristics of the wireless communication links between vehicles.

[0012] As a preferred solution of the present invention, the channel model adopts the normal shadow model.

[0013] As a preferred solution of the present invention, in S2, the process of determining the communication metrics for the vehicle networking scenario is as follows:

[0014] S21. Based on the LTE-V communication scenario, the information transmitted between vehicles is the same. Define to represent the set of N vehicles. For i, j, l being vehicles in the set, the signal-to-interference-plus-noise ratio received by vehicle j is expressed as:

[0015] ;

[0016] In the formula, , respectively represent the transmission powers of vehicle i and vehicle l in the same channel; represents the channel gain from vehicle i to vehicle j; represents the channel gain from vehicle l to vehicle j; represents the power spectral density of additive white Gaussian noise; represents the total interference power caused by all other vehicles l except vehicle i to vehicle j; The channel gain is distributed according to and is independent among different channels;

[0017] S22. The information transmission rate between vehicles acting as relay nodes is expressed as:

[0018] ;

[0019] In the formula, B is the channel bandwidth of the node in the wireless channel; represents the data transmission rate of vehicle node b (i.e., a vehicle acting as a relay node); is the signal-to-interference-plus-noise ratio of vehicle node b;

[0020] S23. The vehicle speed changes with the time period. For a certain vehicle, its instantaneous speed at time t is , and is used to represent the random change of the vehicle speed, is the speed change coefficient and is expressed as:

[0021] ;

[0022] In the formula, T t is the period when the vehicle driving state changes (it can also be understood as the time period when the vehicle driving state changes. The vehicle speed is constant within a period of time, and the speed change only occurs at the beginning and end of this period), g is an integer representing the multiple of the period, used to determine the time point when the vehicle driving state changes; S is a positive integer representing the maximum number of period changes; When , , indicating that at the non-period initial point, the vehicle driving speed is constant; When , , indicating that at the period initial point, the vehicle driving speed will change.

[0023] As a preferred embodiment of the present invention, in step S3, in the vehicle-to-everything (V2X) communication scenario, after determining the source node and the destination node, the communication distance of each vehicle is fixed. Other vehicles find the vehicles within the communication range by broadcasting and constructing a neighbor table. Nodes that meet the requirements of the neighbor table are called next-hop forwarding nodes. The selection of nodes depends on the previous forwarding node. The predicted position of each relay node should not exceed the communication distance of the vehicle from the current position of the previous forwarding node, otherwise a normal communication link cannot be established. Vehicles continuously search for suitable relay nodes until the destination node is found and then stop. At this time, several communication links with different hop counts are constructed.

[0024] As a preferred embodiment of the present invention, in step S4, for the constructed communication links with different hop counts, when encountering transmission paths with the same hop count, the most suitable transmission path is selected through the constructed routing algorithm. The routing algorithm considers four weights, namely:

[0025] The distance D between vehicles, expressed as:

[0026] ;

[0027] x i 、y i are the coordinates of vehicle i; x j 、y j are the coordinates of vehicle j;

[0028] The movement angle of the vehicle relative to the horizontal direction , expressed as:

[0029] ;

[0030] where z is a set conventional coefficient;

[0031] Traffic density , expressed as:

[0032] ;

[0033] where 、 respectively represent the number of neighbor vehicles of vehicle i and vehicle j; represents the maximum number of neighbors of the vehicle;

[0034] Link interruption rate , expressed as:

[0035] ;

[0036] where sum is the sum of the number of disconnections and connections between two nodes in the entire path, and M is the total number of nodes in the link;

[0037] In any link, when the distance between two nodes exceeds the communication distance, the link is disconnected, and the distance between the two nodes is marked as 1; otherwise, it is marked as 0.

[0038] As a preferred solution of the present invention, in the above-mentioned S4, for the distance weight , it is defined that At the moment of 0, the driving state of each vehicle is recorded as , When it is , the driving state of the vehicle becomes

[0039] ;

[0040] In the formula, q is the number of moments (when setting the number of moments, the moment of 0 is not included, that is, q represents the time experienced by the vehicle during the link establishment process);

[0041] For the angle weight , at the moment of 0, the movement angle of each vehicle relative to the horizontal direction is recorded as , When it is , the movement angle of the vehicle relative to the horizontal direction becomes

[0042] ;

[0043] Similarly, the traffic density weight and the interruption rate weight are obtained;

[0044] A weight function LWF is established, which is expressed as:

[0045] ;

[0046] In the formula, , , , are the weight coefficients of , , , respectively;

[0047] The analytic hierarchy process is used to solve , , , , that is, the minimum value of the selection function is used as the optimal path.

[0048] As a preferred solution of the present invention, the specific process of solving by the analytic hierarchy process is as follows:

[0049] Construct a judgment matrix, and the parameters for judgment are , compare the parameters pairwise to form a judgment matrix. Let the judgment matrix be . The element in the judgment matrix represents the relative importance of parameter m relative to parameter n;

[0050] Set the importance of D to a, the importance of to b, the importance of

[0051] ;

[0052] Solve the maximum eigenvalue and eigenvector of the judgment matrix, which are expressed as:

[0053] ;

[0054] In the formula, represents the maximum eigenvalue of the judgment matrix A; α represents the eigenvector corresponding to the maximum eigenvalue; perform normalization processing on α, and the obtained normalized vector is used as the weight coefficient after passing the consistency test.

[0055] As a preferred solution of the present invention, the process of the consistency test is as follows. Let the consistency index of the judgment matrix A be CI, and the CI solution formula is:

[0056] ;

[0057] In the formula, represents the number of eigenvalues of the judgment matrix;

[0058] The consistency ratio CR is expressed as:

[0059] ;

[0060] In the formula, RI is the random consistency index;

[0061] When , the matrix passes the consistency test, otherwise readjust the elements in the judgment matrix.

[0062] The algorithm involved in the present invention can be executed by an electronic device. The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. The above algorithm calculation is implemented by the processor executing the software.

[0063] The beneficial effects of the present invention are:

[0064] The present invention significantly improves the information transmission efficiency and link stability in the vehicle-to-everything (V2X) network. The algorithm comprehensively considers multiple key factors such as the distance between vehicles, the movement angle, traffic density, and link interruption rate, and optimizes the weight coefficients through the analytic hierarchy process, enabling it to intelligently select the optimal transmission path. It not only solves the problem of low efficiency caused by a single weight factor in traditional routing algorithms but also effectively avoids the risk of nodes falling into local optima and routing holes. In addition, the algorithm reduces the excessive perception of the surrounding environment information of roadside units (RSUs), reduces system overhead, enhances the stability of communication links, and provides strong technical support for the development of intelligent transportation systems. Brief Description of the Drawings

[0065] Figure 1 is the schematic flow chart of the method of the present invention;

[0066] Figure 2 is the schematic diagram of the V2X system model for a two-way lane in an embodiment of the present invention;

[0067] Figure 3 is the schematic diagram showing the variation of vehicle speed with time period in an embodiment of the present invention;

[0068] Figure 4 is the schematic diagram showing the broadcast connectivity of vehicle nodes in an embodiment of the present invention;

[0069] Figure 5 is the relationship curve between the routing change degree and vehicle driving time during the verification process of the present invention;

[0070] Figure 6 is the relationship curve between the minimum survival time and vehicle communication range during the verification process of the present invention;

[0071] Figure 7 is the variation curve of the average minimum survival time of vehicles with the vehicle communication range during the verification process of the present invention;

[0072] Figure 8 is the variation curve of the vehicle minimum survival time with vehicle density during the verification process of the present invention;

[0073] Figure 9 is the variation curve of the average survival time of vehicles with vehicle density during the verification process of the present invention;

[0074] Figure 10 is the variation curve of the average routing transmission rate with the vehicle communication radius during the verification process of the present invention;

[0075] Figure 11 is the variation curve of the average routing transmission rate with vehicle density during the verification process of the present invention. Detailed Embodiments

[0076] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings:

[0077] As Figure 1 shown, the V2V communication broadcast routing algorithm based on combined weights includes the following steps:

[0078] S1. Construct a two-way lane vehicle networking system model based on vehicle ad hoc network;

[0079] S2. Determine the communication metrics of the vehicle networking communication scenario, including signal-to-interference-plus-noise ratio (SINR), information transmission rate between vehicles as relay nodes, and vehicle driving speed;

[0080] S3. Construct a routing algorithm: In the two-way lane vehicle networking system model, randomly determine the vehicle source node and destination node. For the information to be transmitted, find the vehicles within the communication range according to the methods of broadcasting and constructing neighbor tables. Between the source node and the destination node, start broadcasting from the source node to find relay nodes outward. Each node existing within the communication range is a relay node. Keep searching from the source node until the destination node is found and stop;

[0081] S4. Determine the weights and weight coefficients involved in the routing algorithm, then establish the weight coefficients according to the analytic hierarchy process, select the most suitable relay node for transmitting information as the optimal path, and transmit the information to the destination node.

[0082] In S1, the construction process is as follows: Assume that the geographical location of each vehicle is known, and each vehicle in the entire vehicle ad hoc network has a unique ID. The lane adopts a two-way six-lane layout, and the initial positions of the vehicles on the road are randomly distributed according to the Poisson distribution; the average vehicle speed , the variation range of is , , ; the running directions of the vehicles on the road are 0, , , , , representing due north, due north, due south, and due west respectively; the running directions of the vehicles at intersections are: , , , , representing northeast, southeast, southwest, and northwest respectively; the vehicles are commanded by traffic lights; the channel model is used to control and describe the transmission characteristics of the wireless communication links between vehicles, and the normal shadow model is adopted for the channel model.

[0083] An exemplary two-way lane vehicle networking system model is as Figure 2 shown, which includes roadside units, base stations, and cellular networks, etc.

[0084] The Poisson distribution is a discrete probability distribution that expresses the probability of a certain number of events occurring within a fixed time or space interval. A channel model is a mathematical model used in the field of communication to describe the behavior and characteristics of signals in a transmission medium.

[0085] In S2, the process of determining the communication metrics for the vehicle-to-everything (V2X) scenario is as follows:

[0086] S21. Based on the LTE-V communication scenario, where the information transmitted between vehicles is the same, define to represent the set of N vehicles. Let i, j, and l be the vehicles in the set. Then, the signal-to-interference-plus-noise ratio (SINR) received by vehicle j is expressed as:

[0087] ;

[0088] In the formula, , respectively represent the transmission powers of vehicle i and vehicle l in the same channel; represents the channel gain from vehicle i to vehicle j; represents the channel gain from vehicle l to vehicle j; represents the power spectral density of additive white Gaussian noise (AWGN); represents the total interference power caused by all other vehicles l except vehicle i to vehicle j. The channel gains are distributed according to and are independent among different channels;

[0089] S22. The wireless channel capacity represents the maximum information transmission rate during error-free transmission and is an important parameter characterizing the communication quality of a wireless link, directly affecting the communication transmission delay. Therefore, the information transmission rate between vehicles acting as relay nodes is expressed as:

[0090] ;

[0091] In the formula, B is the channel bandwidth of the node in the wireless channel; represents the data transmission rate of vehicle node b; is the SINR of vehicle node b;

[0092] S23. The vehicle speed varies with the time period. For a certain vehicle, its instantaneous speed at time t is , and is used to represent the random variation of the vehicle speed. is the speed variation coefficient and is expressed as:

[0093] ;

[0094] In the formula, T tis the period during which the vehicle driving state changes, g is an integer representing the multiple of the period and is used to determine the time point of the vehicle driving state change; S is a positive integer representing the maximum number of period changes; when at this time , it means that at the non-period initial point, the driving speed of the vehicle is constant; when at this time , it means that at the period initial point, the driving speed of the vehicle will change. If an emergency occurs, the speed will change suddenly, and it is often a sharp deceleration.

[0095] Figure 3 is a schematic diagram of the vehicle speed changing periodically with time. It can be seen that the vehicle enters at time 0 and travels at a speed of v4. Between the first stage and the second stage, the speed remains unchanged, and then it increases to v6 in the third stage, and so on. Special attention should be paid to emergencies during the T4 period of the vehicle's emergency braking, and then it accelerates slowly to v4. Figure 4 is a schematic diagram of the vehicle node broadcast connectivity.

[0096] The LTE-V communication scenario refers to the communication environment and application scenarios based on LTE-V (Long Term Evolution-Vehicle to Everything) technology. LTE-V is a wireless communication technology designed specifically for vehicle-to-vehicle communication. It evolves from LTE technology and aims to provide communication capabilities between vehicles and other vehicles, infrastructure, pedestrians, and the network. Additive white Gaussian noise is a noise model widely used in the fields of communication and signal processing.

[0097] In S3, in the vehicle-to-vehicle communication scenario, after determining the source node and the destination node, the communication distance of each vehicle is fixed. Other vehicles find the vehicles within the communication range by broadcasting and constructing a neighbor table. The nodes that meet the neighbor table requirements are called the next-hop forwarding nodes. The selection of nodes depends on the previous forwarding node. The distance between the predicted position of each relay node and the current position of the previous forwarding node cannot exceed the vehicle's communication distance, otherwise a normal communication link cannot be established; the vehicle continuously searches for a suitable relay node until it finds the destination node and then stops. At this time, several communication links with different hop counts will be constructed.

[0098] In S4, for the communication links with different hop counts constructed, when encountering transmission paths with the same hop count, the most suitable transmission path is selected through the constructed routing algorithm. The routing algorithm considers four weights, which are respectively:

[0099] The distance D between vehicles, expressed as:

[0100] ;

[0101] x i 、y i are the coordinates of vehicle i; x j 、y j are the coordinates of vehicle j;

[0102] The movement angle of the vehicle relative to the horizontal direction , expressed as:

[0103] ;

[0104] In the formula, z is a set conventional coefficient, which is determined according to the actual situation or experimental data;

[0105] Traffic density , expressed as:

[0106] ;

[0107] In the formula, 、 respectively represent the number of neighbor vehicles of vehicle i and vehicle j; represents the maximum number of neighbors of the vehicle;

[0108] Link interruption rate , expressed as:

[0109] ;

[0110] In the formula, sum is the sum of the number of disconnections and connections between two nodes in the entire path, and M is the total number of nodes in the link;

[0111] In any link, when the distance between two nodes exceeds the communication distance, the link is disconnected, and the distance between the two nodes is marked as 1; otherwise, it is marked as 0.

[0112] In S4, for the distance weight , it is defined that At time 0, the driving state of each vehicle is recorded as , When , the driving state of the vehicle becomes

[0113] ;

[0114] In the formula, q is the number of time instants;

[0115] For the angle weight , at time 0, the movement angle of each vehicle relative to the horizontal direction is recorded as , When , then:

[0116] ;

[0117] Similarly, obtain the traffic density weight and the interruption rate weight ;

[0118] Establish a weight function LWF, expressed as:

[0119] ;

[0120] In the formula, , , , are respectively , , , 's weight coefficients;

[0121] Use the analytic hierarchy process to solve , , , , that is, select the minimum value of the function as the optimal path.

[0122] The specific process of solving using the analytic hierarchy process is as follows:

[0123] Construct a judgment matrix, and the parameters for judgment are . Compare the parameters pairwise to form a judgment matrix. Let the judgment matrix be . The element in the judgment matrix represents the relative importance of parameter m relative to parameter n;

[0124] Set the importance of D as a, 's importance as b, 's importance as c, 's importance as d. The judgment matrix A is expressed as:

[0125] ;

[0126] Solve the maximum eigenvalue and eigenvector of the judgment matrix, expressed as:

[0127] ;

[0128] In the formula, represents the maximum eigenvalue of the judgment matrix A; α represents the eigenvector corresponding to the maximum eigenvalue; perform standardization processing on α, and the obtained standardized vector is used as the weight coefficient after passing the consistency test.

[0129] The process of consistency detection is as follows. Let the consistency index of the judgment matrix A be CI, and the solution formula for CI is:

[0130] ;

[0131] In the formula, represents the number of eigenvalues of the judgment matrix;

[0132] The consistency ratio CR is expressed as:

[0133] ;

[0134] In the formula, RI is the random consistency index;

[0135] When the matrix passes the consistency detection, otherwise the elements in the judgment matrix are readjusted.

[0136] The verification process is as follows:

[0137] Based on the two-way lane vehicle networking system model and communication requirements, the algorithm (TPWR) of this embodiment is compared with the existing vehicle networking routing algorithms LSPR, the ad hoc on-demand distance vector routing protocol AODV, and the stateless greedy perimeter routing protocol GPSR. The comparison results are as Figures 5 - 11 shown, where Figure 5 shows the routing change degree of different routing algorithms with the change of vehicle driving time under the conditions of vehicle density of 25 vehicles / (lane×kilometer) and communication radius R of 150 meters. It can be seen that for the routing selected by TPWR, its change degree gradually increases and the fluctuation range is small; as Figure 6 and Figure 7 shown, under the condition of vehicle density of 20 vehicles / (lane×kilometer), as the communication range (communication radius R) expands, the routing survival time of the path selected by the TPWR algorithm is longer than that of other routings. This is because the algorithm selects the optimal path by taking the link interruption rate as one of the communication parameters, effectively avoiding link interruption and routing holes.

[0138] As Figure 8 and Figure 9 shown, in the case of communication radius R of 200 meters, as the vehicle density increases, the routing survival time of the path selected by TPWR is longer, and the more vehicles there are, the better the algorithm effect. This is because in the case of road congestion, the algorithm can preferentially select relatively fewer relay nodes for data transmission, effectively avoiding the routing congestion problem.

[0139] Figure 10 and Figure 11 in, the vehicle density is 20 vehicles / (lane×kilometer), Figure 10It shows that as the communication radius of the vehicle continues to increase, the routing transmission rate continues to improve, ensuring fast and reliable data transmission, improving the transmission efficiency, and increasing the stability of the link. Figure 11 It shows that the average transmission rate of the routing decreases with the increase in vehicle density. This is because too high vehicle density will cause routing congestion and affect data transmission. From Figure 10 and Figure 11 it can also be seen that the effect of the TPWR algorithm is significantly better than other algorithms because this algorithm takes traffic density as one of the selection indicators when selecting routes, so it can efficiently select the optimal path.

Claims

1. V2V communication broadcast routing algorithm based on combined weights, characterized in that It includes the following steps: S1. Construct a two-way lane vehicle networking system model based on vehicle ad hoc network; S2. Determine the communication metrics of the vehicle networking communication scenario, including signal-to-interference-plus-noise ratio (SINR), information transmission rate between vehicles acting as relay nodes, and vehicle driving speed; S3. Construct a routing algorithm: In the two-way lane vehicle networking system model, randomly determine the vehicle source node and destination node. For the information to be transmitted, find the vehicles within the communication range according to the methods of broadcasting and constructing neighbor tables. Between the source node and the destination node, start broadcasting from the source node to find relay nodes outward. Each node existing within the communication range is a relay node. Keep searching from the source node until the destination node is found and then stop; S4. Determine the weights and weight coefficients involved in the routing algorithm, and then establish the weight coefficients according to the analytic hierarchy process, select the most suitable relay node for transmitting information as the optimal path, and transmit the information to the destination node; In S4 described above, for the distance weight , it is defined as at the moment of 0, the driving state of each vehicle is recorded as , when the driving state of the vehicle changes to , then: ； In the formula, q is the number of moments; For the angular weight , at time 0, the movement angle of each vehicle relative to the horizontal direction is denoted as , when the movement angle of the vehicle relative to the horizontal direction becomes , then: ； Similarly, obtain the traffic density weight and the interruption rate weight ; Establish a weight function LWF, expressed as: ； In the formula, , , , are respectively , , , 's weight coefficients; Solve using the Analytic Hierarchy Process , , , , that is, select the minimum value of the selection function as the optimal path.

2. The V2V communication broadcast routing algorithm based on combined weights according to claim 1, wherein In the above-mentioned S1, the construction process is as follows: Assume that the geographical location of each vehicle is known, and each vehicle in the entire vehicular ad-hoc network has a unique ID. The lane layout is a two-way six-lane layout, and the initial positions of the vehicles on the road are randomly distributed according to the Poisson distribution; the average vehicle speed , varies within the range of ; the running directions of the vehicles on the road are 0,[[]] , , , representing due north, true north, due south, and due west respectively; the running directions of the vehicles at intersections are: , , , , representing northeast, southeast, southwest, and northwest respectively; the vehicles are commanded by traffic lights; a channel model is used to control and describe the transmission characteristics of the wireless communication links between vehicles.

3. The V2V communication broadcast routing algorithm based on combined weights according to claim 2, wherein The described channel model adopts a normal shadow model.

4. The V2V communication broadcast routing algorithm based on combined weights according to claim 2, characterized in that, In the described S2, the process of determining the communication metrics of the vehicle networking scenario is: S21. Based on the LTE-V communication scenario, where the information transmitted between vehicles is the same, define as the set of N vehicles. If i, j, and l are vehicles in the set, then the signal-to-interference-plus-noise ratio (SINR) received by vehicle j is expressed as: ； Wherein, and respectively represent the transmission powers of vehicle i and vehicle l in the same channel; represents the channel gain from vehicle i to vehicle j; represents the channel gain from vehicle l to vehicle j; represents the power spectral density of additive white Gaussian noise; represents the total interference power caused by all other vehicles l to vehicle j except vehicle i; the channel gains are distributed according to and are independent among different channels; S22. The information transmission rate between vehicles acting as relay nodes is expressed as: ； Where B is the channel bandwidth of the node in the wireless channel; represents the data transmission rate of vehicle node b; is the signal-to-interference-plus-noise ratio of vehicle node b; S23. The vehicle speed changes over time periods. For a certain vehicle, its instantaneous speed at time t is , and use to represent the random change in the vehicle speed, is the speed change coefficient, expressed as: ； where T t is the period when the vehicle driving state changes, and g is an integer representing the multiple of the period, which is used to determine the time point of the vehicle driving state change; S is a positive integer representing the maximum number of times of periodic change; when then , it means that at the non-periodic initial point, the driving speed of the vehicle is constant; when then , it means that at the periodic initial point, the driving speed of the vehicle will change.

5. The V2V communication broadcast routing algorithm based on combined weights according to claim 4, wherein In the described S3, in the vehicle networking communication scenario, after determining the source node and the destination node, the communication distance of each vehicle is fixed. Other vehicles find the vehicles within the communication range through the methods of broadcasting and constructing neighbor tables. The nodes meeting the requirements of the neighbor table are called the next-hop forwarding nodes. The selection of nodes depends on the previous forwarding node. The distance between the predicted position of each relay node and the current position of the previous forwarding node cannot exceed the communication distance of the vehicle, otherwise a normal communication link cannot be established; The vehicle keeps searching for suitable relay nodes until the destination node is found and then stops. At this time, several communication links with different hop counts will be constructed.

6. The V2V communication broadcast routing algorithm based on combined weights according to claim 5, characterized in that In S4, for the communication links with different hop counts constructed, when encountering transmission paths with the same hop count, the most suitable transmission path is selected through the constructed routing algorithm, and the routing algorithm considers four weights, which are respectively: The distance D between vehicles is expressed as: ； x i 、y i are the coordinates of vehicle i; x j 、y j are the coordinates of vehicle j; The movement angle of the vehicle relative to the horizontal direction , expressed as: ； In the formula, z is a set conventional coefficient; Traffic density , expressed as: ； In the formula, and respectively represent the number of neighbor vehicles of vehicle i and vehicle j; represents the maximum number of neighbors of a vehicle; Link interruption rate , expressed as: ； In the formula, sum is the sum of the disconnection number and connection number of two nodes in the whole path, and M is the total number of nodes in the link; In any link, when the distance between two nodes exceeds the communication distance, the link is disconnected, and the distance between the two nodes is marked as 1; otherwise, it is marked as 0.

7. The V2V communication broadcast routing algorithm based on combined weights according to claim 6, characterized in that, The specific process of solving by the analytic hierarchy process is: Construct a judgment matrix, and the parameters for judgment are , compare the parameters pairwise to form a judgment matrix. Let the judgment matrix be . The element in the judgment matrix represents the relative importance of parameter m relative to parameter n; Set the importance of D as a, as b, as c, as d, and the judgment matrix A is expressed as: ； Solve the maximum eigenvalue and eigenvector of the judgment matrix, expressed as: ； In the formula, represents the largest eigenvalue of the judgment matrix A; α represents the eigenvector corresponding to the largest eigenvalue; after normalizing α, the obtained normalized vector is used as the weight coefficient after passing the consistency test.

8. The V2V communication broadcast routing algorithm based on combined weights according to claim 7, wherein The process of consistency detection is that the consistency index of the judgment matrix A is set as CI, and the CI solution formula is: ； In the formula, represents the number of eigenvalues of the judgment matrix; The consistency ratio CR is expressed as: ； In the formula, RI is the random consistency index; When the matrix passes the consistency test; otherwise, the elements in the judgment matrix are readjusted.

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