V2V communication broadcast routing algorithm based on combined weight

Abstract

The invention belongs to the technical field of Internet of Vehicles, and particularly relates to a V2V communication broadcast routing algorithm based on a combined weight, and the algorithm comprises the steps: constructing a two-way lane Internet of Vehicles system model based on a vehicle ad hoc network; determining a communication index of the Internet of Vehicles communication scene; and constructing a routing algorithm: randomly determining a source node and a destination node of a vehicle, finding the vehicle in a communication range for information needing to be transmitted according to a mode of broadcasting and constructing a neighbor table, broadcasting outwards from the source node to find a relay node, continuously finding from the source node, and transmitting the information to the destination node. Stopping until a destination node is found; weights and weight coefficients involved in a routing algorithm are determined, then the weight coefficients are determined according to an analytic hierarchy process, a relay node most suitable for information transmission is selected as an optimal path, and the information is transmitted to a destination node. According to the invention, on the premise of ensuring information transmission fast recording, the routing selection efficiency is improved, and the link stability is ensured.

Description

Technical Field

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

[0002] With the combination of 5G network, mobile Internet and Internet of Things, the era of "Internet of Everything" is becoming a reality. In particular, the progress of wireless communication and automobile technology has pushed the research of intelligent transportation system (ITS) to a new height. However, traditional routing algorithms are gradually unable to solve practical vehicle problems due to their low selection efficiency.

[0003] Among the existing routing methods, Karimi et al. proposed a predictive geographic routing protocol (PGRP) method, which uses the direction and angle information of the vehicle to predict the future network topology and assigns weights to neighbors based on 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 the road into different sections, selects gateways according to vehicle locations, establishes a routing table containing section information, and finally uses a responsive 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 performs reinforcement learning by perceiving the surrounding environment information to continue with subsequent path selection.

[0004] However, the existing routing algorithms consider too few weight factors when selecting paths, resulting in low routing efficiency. In addition, nodes are prone to fall into local optimality under traditional algorithm selection, that is, nodes closest to the destination node are always selected as candidate nodes. In this case, greedy forwarding fails and nodes fall into a routing hole state. In addition, RSU's excessive perception of surrounding environment information will increase system overhead and lead to 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 while ensuring the speed of information transmission.

[0006] To achieve the above objectives, the present invention provides a V2V communication broadcast routing algorithm based on combined weights, comprising the following steps: S1. Construct a two-way lane vehicle networking system model based on vehicle ad hoc network; S2. Determine the communication indicators of the Internet of Vehicles communication scenario, including the signal-to-interference-and-noise ratio (SINR), the information transmission rate between vehicles as relay nodes, and the vehicle driving speed; S3. Construct routing algorithm: In the two-way lane vehicle networking system model, randomly determine the vehicle source node and destination node. For the information that needs to be transmitted, find the vehicle within the communication range by broadcasting and constructing the neighbor table. Between the source node and the destination node, broadcast outward from the source node to find the relay node. Every node within the communication range is a relay node. Keep searching from the source node until the destination node is found. S4. Determine the weights and weight coefficients involved in the routing algorithm, and then establish the weight coefficients according to the hierarchical analysis method, select the relay node that is most suitable for transmitting information as the optimal path, and transmit the information to the destination node.

[0007] As a preferred solution of the present invention, in S1, the construction process is as follows: assuming that the geographical location of each vehicle is known, and each vehicle in the entire vehicular ad hoc network has a unique ID, the lanes are arranged in 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 range of change is ; The direction of the vehicle on the road is 0, , , , respectively represent the north, north, south, and west directions; the direction of vehicles at the intersection is: , , , , representing the northeast, southeast, southwest and northwest directions respectively; the vehicles are directed by the command lights; the channel model is used to control and describe the transmission characteristics of the wireless communication link between vehicles.

[0008] As a preferred solution of the present invention, the channel model adopts a normal shadow model.

[0009] As a preferred solution of the present invention, in S2, the process of determining the communication index of the Internet of Vehicles scenario is: S21. Based on the LTE-V communication scenario, the information transmitted between vehicles is the same, and the definition represents a set of N vehicles, i, j, l are the vehicles in the set, then the signal to interference noise ratio received by vehicle j is It is expressed as: ; In the formula, , They represent the transmission power of vehicle i and vehicle l in the same channel respectively; 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 vehicles l to vehicle j except vehicle i; the channel gain is calculated according to Distribution,independent among different channels; S22. The information transmission rate between vehicles 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 (i.e., a vehicle acting as a relay node); is the signal to interference and noise ratio of vehicle node b; S23. The vehicle speed changes with the time period. For a certain vehicle, its instantaneous speed at time t is ,use represents the random variation of the vehicle's speed, is the speed variation coefficient, expressed as: ; Where, T t is the cycle of vehicle driving state changes (it can also be understood as the time period of vehicle driving state changes. The speed of the vehicle is constant during a period of time, and the speed changes only occur at the beginning and end of this period of time). g is an integer representing the multiple of the cycle, which is used to determine the time point of vehicle driving state change. S is a positive integer representing the maximum number of cycle changes. When hour, , indicating that at the non-periodic initial point, the vehicle's speed is constant; when hour, , indicating that at the initial point of the cycle, the vehicle's speed will change.

[0010] As a preferred embodiment of the present invention, in the S3, in the vehicle network communication scenario, after the source node and the destination node are determined, the communication distance of each vehicle is fixed, and other vehicles find the vehicles within the communication range by broadcasting and constructing a neighbor table. The node that meets the requirements of the neighbor table is called the next-hop forwarding node, and the selection of the node 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 continuously searches for a suitable relay node until it finds the destination node, and stops at this time, at which time several communication links with different hop numbers are constructed.

[0011] As a preferred solution of the present invention, in S4, for the communication links with different hop numbers constructed, when encountering transmission paths with the same hop number, the most suitable transmission path is selected by the constructed routing algorithm, and the routing algorithm takes into account The four weights are: The distance D between vehicles is expressed as: ; x i ,y i is the coordinate of vehicle i; x j ,y j is the coordinate of vehicle j; The vehicle's angle of motion relative to the horizontal , expressed as: ; Where z is the conventional coefficient set; Traffic density , expressed as: ; In the formula, , Respectively represent the number of neighboring vehicles of vehicle i and vehicle j; Indicates 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 entire 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.

[0012] As a preferred solution of the present invention, in S4, for the distance weight ,definition At time 0, the driving state of each vehicle is recorded as , The vehicle driving state changes to ,but: ; Where q is the number of moments (when setting the number of moments, the 0 moment is not included, i.e. q represents the time experienced by the vehicle during the link establishment process); For angle weight At time 0, the motion angle of each vehicle relative to the horizontal direction is recorded as , The vehicle's relative motion angle to the horizontal direction becomes ,but: ; Similarly, obtain the traffic density weight and interruption rate weight ; Establish the weight function LWF, expressed as: ; In the formula, , , , They are , , , The weight coefficient of Using analytic hierarchy process to solve , , , , that is, to select the minimum value of the function as the optimal path.

[0013] As a preferred solution of the present invention, the specific process of solving the problem using the analytic hierarchy process is as follows: 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 elements in the judgment matrix Indicates the relative importance of parameter m relative to parameter n; Set the importance of D to a, The importance of b, The importance of c, The importance of is 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 maximum eigenvalue of the judgment matrix A; α represents the eigenvector corresponding to the maximum eigenvalue; α is normalized to obtain the normalized vector After consistency detection, it is used as the weight coefficient.

[0014] As a preferred solution of the present invention, the consistency detection process is as follows: assuming that the consistency index of the judgment matrix A is CI, the CI solution formula is: ; In the formula, Indicates 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.

[0015] The algorithm involved in the present invention can be executed by an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the above algorithm calculation is implemented by executing the software by the processor.

[0016] The beneficial effects of the present invention are: The present invention significantly improves the information transmission efficiency and link stability in the Internet of Vehicles. The algorithm comprehensively considers multiple key factors such as the distance between vehicles, movement angle, traffic density and link interruption rate, and optimizes the weight coefficient through the hierarchical analysis method, so as to intelligently select the optimal transmission path; it not only solves the inefficiency problem caused by the single weight factor in the traditional routing algorithm, but also effectively avoids the risk of nodes falling into local optimality and routing holes. In addition, the algorithm reduces the excessive perception of the surrounding environment information of the RSU, reduces the system overhead, enhances the stability of the communication link, and provides strong technical support for the development of intelligent transportation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the process of the method of the present invention; Figure 2 is a schematic diagram of a two-way lane vehicle networking system model in an embodiment of the present invention; Figure 3 is a schematic diagram of the change of vehicle speed over a time period in an embodiment of the present invention; Figure 4 is a schematic diagram of vehicle node broadcast connectivity in an embodiment of the present invention; Figure 5 is a curve showing the relationship between the route change degree and the vehicle travel time during the verification process of the present invention; Figure 6 is a relationship curve between the minimum survival time and the vehicle communication range during the verification process of the present invention; Figure 7 is a curve showing the change of the average minimum survival time of a vehicle with the communication range of the vehicle during the verification process of the present invention; Figure 8 is a curve showing the change of the minimum survival time of a vehicle with the vehicle density during the verification process of the present invention; Fig. 9 is a curve showing the average survival time of vehicles changing with vehicle density during the verification process of the present invention; Fig.10 is a curve showing the average routing transmission rate changing with the vehicle communication radius during the verification process of the present invention; Fig.11 It is a curve showing the average routing transmission rate changing with vehicle density during the verification process of the present invention. DETAILED DESCRIPTION

[0018] The embodiments of the present invention are further described below in conjunction with the accompanying drawings: like Figure 1 As shown, the V2V communication broadcast routing algorithm based on combined weights includes the following steps: S1. Construct a two-way lane vehicle networking system model based on vehicle ad hoc network; S2. Determine the communication indicators of the Internet of Vehicles communication scenario, including the signal-to-interference-and-noise ratio (SINR), the information transmission rate between vehicles as relay nodes, and the vehicle driving speed; S3. Construct routing algorithm: In the two-way lane vehicle networking system model, randomly determine the vehicle source node and destination node. For the information that needs to be transmitted, find the vehicle within the communication range by broadcasting and constructing the neighbor table. Between the source node and the destination node, broadcast outward from the source node to find the relay node. Every node within the communication range is a relay node. Keep searching from the source node until the destination node is found. S4. Determine the weights and weight coefficients involved in the routing algorithm, and then establish the weight coefficients according to the hierarchical analysis method, select the relay node that is most suitable for transmitting information as the optimal path, and transmit the information to the destination node.

[0019] In S1, the construction process is as follows: assuming that the geographical location of each vehicle is known, and each vehicle in the entire VANET has a unique ID, the lanes are arranged in a bidirectional six-lane layout, and the initial positions of vehicles on the road are randomly distributed according to the Poisson distribution; the average speed , The change range is ; The direction of the vehicle on the road is 0, , , , respectively represent the north, north, south, and west directions; the direction of vehicles at the intersection is: , , , , representing the northeast, southeast, southwest and northwest directions respectively; the vehicles are directed by the command lights; the channel model is used to control and describe the transmission characteristics of the wireless communication link between vehicles, and the channel model adopts the normal shadow model.

[0020] An exemplary two-way lane vehicle networking system model is as follows: Figure 2As shown, this includes roadside units, base stations, and cellular networks.

[0021] 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. Channel model is a mathematical model used in the communication field to describe the behavior and characteristics of signals in the transmission medium.

[0022] In S2, the process of determining the communication indicators of the Internet of Vehicles scenario is as follows: S21. Based on the LTE-V communication scenario, the information transmitted between vehicles is the same, and the definition represents a set of N vehicles, i, j, l are the vehicles in the set, then the signal to interference noise ratio received by vehicle j is It is expressed as: ; In the formula, , They represent the transmission power of vehicle i and vehicle l in the same channel respectively; 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 vehicles l to vehicle j except vehicle i; the channel gain is calculated according to Distribution,independent among different channels; S22. Wireless channel capacity indicates the maximum information transmission rate when information is transmitted without error. It is an important parameter to characterize the communication quality of wireless links and directly affects the communication transmission delay. Therefore, the information transmission rate between vehicles 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 and noise ratio of vehicle node b; S23. The vehicle speed changes with the time period. For a certain vehicle, its instantaneous speed at time t is ,use represents the random variation of the vehicle's speed, is the speed variation coefficient, expressed as: ; Where, T t is the cycle of vehicle driving state change, g is an integer, indicating the multiple of the cycle, used to determine the time point of vehicle driving state change; S is a positive integer, indicating the maximum number of cycle changes; when hour, , indicating that at the non-periodic initial point, the vehicle's speed is constant; when hour, , indicating that at the beginning of the cycle, the vehicle's speed will change. If an emergency occurs, the speed will change suddenly, often by a sharp deceleration.

[0023] Figure 3 It is a schematic diagram of the change of vehicle speed over time period. It can be seen that the vehicle enters at time 0 and travels at speed v4. Between the first and second stages, the speed remains unchanged, and then increases to v6 in the third stage, and so on. Pay special attention to emergencies during the T4 period when the vehicle brakes urgently, and then slowly accelerate to v4. Figure 4 It is a schematic diagram of the broadcast connectivity of vehicle nodes.

[0024] LTE-V communication scenarios refer 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 communications. It is based on the evolution of LTE technology and aims to provide communication capabilities between vehicles and other vehicles, infrastructure, pedestrians, and networks. Additive Gaussian white noise is a noise model widely used in the fields of communication and signal processing.

[0025] In S3, in the vehicle network communication scenario, after the source node and the destination node are determined, the communication distance of each vehicle is fixed. Other vehicles find vehicles within the communication range by broadcasting and constructing a neighbor table. The node that meets the requirements of the neighbor table is called the next-hop forwarding node. The selection of the node 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 continuously searches for a suitable relay node until it finds the destination node, at which time several communication links with different hop numbers are constructed.

[0026] In S4, for the communication links with different hop numbers, when encountering the transmission paths with the same hop number, the most suitable transmission path is selected through the constructed routing algorithm. The routing algorithm takes into account The four weights are: The distance D between vehicles is expressed as: ; x i ,y i is the coordinate of vehicle i; x j ,y j is the coordinate of vehicle j; The vehicle's angle of motion relative to the horizontal , expressed as: ; In the formula, z is a set conventional coefficient, which is determined according to actual conditions or experimental data; Traffic density , expressed as: ; In the formula, , Respectively represent the number of neighboring vehicles of vehicle i and vehicle j; Indicates 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 entire 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.

[0027] In S4, for distance weight ,definition At time 0, the driving state of each vehicle is recorded as , The vehicle driving state changes to ,but: ; Where q is the number of moments; For angle weight At time 0, the motion angle of each vehicle relative to the horizontal direction is recorded as , The vehicle's relative motion angle to the horizontal direction becomes ,but: ; Similarly, traffic density weight is obtained and interruption rate weight ; Establish the weight function LWF, expressed as: ; In the formula, , , , They are , , , The weight coefficient of Using analytic hierarchy process to solve , , , , that is, to select the minimum value of the function as the optimal path.

[0028] The specific process of solving the problem using the hierarchical analysis method is as follows: 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 elements in the judgment matrix Indicates the relative importance of parameter m relative to parameter n; Set the importance of D to a, The importance of b, The importance of c, The importance of is 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 maximum eigenvalue of the judgment matrix A; α represents the eigenvector corresponding to the maximum eigenvalue; α is normalized to obtain the normalized vector After consistency detection, it is used as the weight coefficient.

[0029] The consistency detection process is as follows: let the consistency index of the judgment matrix A be CI, and the CI solution formula is: ; In the formula, Indicates 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.

[0030] The verification process is: Based on the two-way lane IoV system model and communication requirements, the algorithm (TPWR) of this embodiment is compared with the existing IoV routing algorithm LSPR, the on-demand self-organizing network distance vector routing protocol AODV, and the stateless greedy peripheral routing protocol GPSR. The comparison results are as follows: Figure 5-Figure 11 As shown, Figure 5The route variability of different routing algorithms with vehicle travel time under the conditions of vehicle density of 25 vehicles / (lane × km) and communication radius R of 150 meters is shown. It can be seen that the route selected by TPWR has a gradually increasing variability and a small fluctuation; Figure 6 and Figure 7 As shown in the figure, under the condition of vehicle density of 20 vehicles / (lane × km), as the communication range (communication radius R) increases, the routing survival time of the path selected by the TPWR algorithm is longer than that of other routes. This is because the algorithm uses the link interruption rate as one of the communication parameters to select the optimal path, effectively avoiding link interruption and routing holes.

[0031] like Figure 8 and Fig. 9 As shown in the figure, when the communication radius R is 200 meters, as the vehicle density increases, the path routing selected by TPWR survives longer, and the more vehicles there are, the better the algorithm effect is. This is because in the case of road congestion, the algorithm can give priority to relay nodes with relatively fewer vehicles for data transmission, effectively avoiding the problem of routing congestion.

[0032] Fig.10 and Fig.11 In the example, the vehicle density is 20 vehicles / (lane × km), Fig.10 It shows that as the vehicle communication radius continues to increase, the routing transmission rate continues to increase, ensuring fast and reliable data transmission, improving transmission efficiency, and increasing link stability. Fig.11 It shows that the average transmission rate of the route decreases with the increase of vehicle density. This is because too high vehicle density will cause route congestion and affect data transmission. Fig.10 and Fig.11 It can also be seen that the effect of TPWR algorithm is obviously 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. A V2V communication broadcast routing algorithm based on combined weights, characterized by The following steps are involved: S1. Construct a two-way lane vehicle networking system model based on vehicle ad hoc network; S2. Determine the communication indicators of the Internet of Vehicles communication scenario, including the signal-to-interference-and-noise ratio (SINR), the information transmission rate between vehicles as relay nodes, and the vehicle driving speed; S3. Construct routing algorithm: In the two-way lane vehicle networking system model, randomly determine the vehicle source node and destination node. For the information that needs to be transmitted, find the vehicle within the communication range by broadcasting and constructing the neighbor table. Between the source node and the destination node, broadcast outward from the source node to find the relay node. Every node within the communication range is a relay node. Keep searching from the source node until the destination node is found. S4. Determine the weights and weight coefficients involved in the routing algorithm, and then establish the weight coefficients according to the hierarchical analysis method, select the relay node that is most suitable for transmitting information as the optimal path, and transmit the information to the destination node.

2. The V2V communication broadcast routing algorithm based on combined weights according to claim 1, characterized in that: In the above S1, the construction process is as follows: assume that the geographical location of each vehicle is known, and each vehicle in the entire VANET has a unique ID, the lanes are arranged in a bidirectional six-lane layout, and the initial positions of the vehicles on the road are randomly distributed according to the Poisson distribution; the average speed , The range of change is ; The direction of the vehicle on the road is 0, , , , respectively represent the north, north, south, and west directions; the direction of vehicles at the intersection is: , , , , representing the northeast, southeast, southwest and northwest directions respectively; the vehicles are directed by the command lights; the channel model is used to control and describe the transmission characteristics of the wireless communication link between vehicles.

3. The V2V communication broadcast routing algorithm based on combined weights according to claim 2, characterized in that: The 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 S2, the process of determining the communication index of the Internet of Vehicles scenario is as follows: S21. Based on the LTE-V communication scenario, the information transmitted between vehicles is the same, and the definition represents a set of N vehicles, i, j, l are the vehicles in the set, then the signal to interference noise ratio received by vehicle j is It is expressed as: ； In the formula, , They represent the transmission power of vehicle i and vehicle l in the same channel respectively; 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 vehicles l to vehicle j except vehicle i; the channel gain is calculated according to Distribution,independent among different channels; S22. The information transmission rate between vehicles 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 and noise ratio of vehicle node b; S23. The vehicle speed changes with the time period. For a certain vehicle, its instantaneous speed at time t is ,use represents the random variation of the vehicle's speed, is the speed variation coefficient, expressed as: ； Where, T t is the period during which the vehicle's driving state changes, and g is an integer representing a multiple of the period, which is used to determine the time point when the vehicle's driving state changes; S is a positive integer, indicating the maximum number of periodic changes; when hour, , indicating that at the non-periodic initial point, the vehicle's speed is constant; when hour, , indicating that at the initial point of the cycle, the vehicle's speed will change.

5. The V2V communication broadcast routing algorithm based on combined weights according to claim 4, characterized in that: In the above S3, in the vehicle network communication scenario, after the source node and the destination node are determined, the communication distance of each vehicle is fixed. Other vehicles find vehicles within the communication range by broadcasting and constructing a neighbor table. The node that meets the requirements of the neighbor table is called the next-hop forwarding node. The selection of the node 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 continuously searches for a suitable relay node until it finds the destination node, at which time several communication links with different hop numbers are constructed.

6. The V2V communication broadcast routing algorithm based on combined weights according to claim 5, characterized in that: In the above S4, for the communication links with different hop numbers constructed, when encountering transmission paths with the same hop number, the most suitable transmission path is selected by the constructed routing algorithm. The routing algorithm takes into account The four weights are: The distance D between vehicles is expressed as: ； x i ,y i is the coordinate of vehicle i; x j ,y j is the coordinate of vehicle j; The vehicle's angle of motion relative to the horizontal , expressed as: ； Where z is the conventional coefficient set; Traffic density , expressed as: ； In the formula, , Respectively represent the number of neighboring vehicles of vehicle i and vehicle j; Indicates 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 entire 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: In S4, for the distance weight ,definition At time 0, the driving state of each vehicle is recorded as , The vehicle driving state changes to ,but: ； Where q is the number of moments; For angle weight At time 0, the motion angle of each vehicle relative to the horizontal direction is recorded as , The vehicle's relative motion angle to the horizontal direction becomes ,but: ； Similarly, traffic density weight is obtained and interruption rate weight ; Establish the weight function LWF, expressed as: ； In the formula, , , , They are , , , The weight coefficient of Using analytic hierarchy process to solve , , , , that is, to select the minimum value of the function as the optimal path.

8. The V2V communication broadcast routing algorithm based on combined weights according to claim 7, characterized in that: The specific process of solving the problem using the hierarchical analysis method is as follows: 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 elements in the judgment matrix Indicates the relative importance of parameter m relative to parameter n; Set the importance of D to a, The importance of b, The importance of c, The importance of is 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 maximum eigenvalue of the judgment matrix A; α represents the eigenvector corresponding to the maximum eigenvalue; α is normalized to obtain the normalized vector After consistency detection, it is used as the weight coefficient.

9. The V2V communication broadcast routing algorithm based on combined weights according to claim 8, characterized in that: The consistency detection process is as follows: let the consistency index of the judgment matrix A be CI, and the CI solution formula is: ； In the formula, Indicates 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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