Method and system for V2V information routing transmission between urban roads oriented to Internet of Vehicles

Abstract

The invention relates to a V2V (Vehicle to Vehicle) information routing transmission method and system between urban roads facing the Internet of Vehicles, and the method comprises the steps: calculating a packet routing global path through employing a Dijkstra algorithm according to the real-time traffic flow density information of the roads; when a data packet is transmitted in a road section, comprehensively evaluating the quality scores of neighbor vehicles by adopting a fuzzy inference system, and selecting the vehicle with the highest score as a next hop node; and when the data packet needs to be transmitted across the road, a crossroad dynamic group forwarding strategy is adopted, vehicles in the crossroad range are fully utilized as cross-road transmission relay nodes, and V2V information routing transmission is realized. The invention aims to construct an optimal path for information transmission in a complex vehicle environment so as to reduce the average end-to-end time delay and packet loss rate of V2V communication.

Description

Technical Field

[0001] The present invention relates to a V2V information routing transmission method and system for urban roads in a vehicle networking environment, belonging to the technical field of routing transmission. Background Art

[0002] As an important part of the intelligent transportation system, the Internet of Vehicles (IoV) realizes information transmission and sharing between vehicles and between vehicles and roads through the on-board unit (OBU) to provide diversified business services to meet the needs of various application scenarios such as autonomous driving, traffic coordination and entertainment. Studies have shown that the application of IoV can significantly improve road safety, ease traffic congestion and reduce energy consumption. Specifically, it has the potential to reduce traffic accident rates by 80%, reduce traffic congestion by more than 60%, and reduce energy consumption by 20%.

[0003] Mobile users can obtain convenient and fast services such as voice calls and infotainment through cellular networks, but the forwarding communication of data packets through base stations has more hops and longer latency than direct communication between vehicles and vehicles and between vehicles and infrastructure. Direct communication between vehicles and vehicles and between vehicles and roadside units (RSU) in vehicular ad hoc networks (VANET) is more advantageous, allowing nodes to send and receive message data packets with the minimum latency under current traffic conditions.

[0004] Reliable information transmission is the key to the implementation of Internet of Vehicles applications. The design of efficient routing mechanisms can effectively reduce the delay and failure rate of information transmission and improve the service quality of Internet of Vehicles applications. However, the complexity of the urban Internet of Vehicles environment brings challenges to the design of routing protocols. On the one hand, the communication distance of the vehicle itself is limited, and the speed of the vehicle is relatively fast, so the communication link between vehicles is extremely unstable. On the other hand, the structure of urban roads is more complex than that of highways, with many buildings and serious signal fading. Unreasonable routing schemes are prone to data transmission interruptions, resulting in frequent routing recovery, which leads to longer data transmission delays. In addition, the traffic volume on urban roads is unevenly distributed. On roads with low vehicle density, the routing process is prone to fall into a local optimal state, causing transmission interruptions. On roads with high vehicle density, the increase in communication nodes is prone to network congestion. Therefore, in the face of the complex and changeable urban Internet of Vehicles environment, a fast and reliable information transmission solution that can meet the service quality requirements of different services in the Internet of Vehicles has become a research hotspot in domestic and foreign academic and industrial circles.

[0005] Most of the existing urban vehicle self-organizing network routing schemes strive to maximize the single-hop distance of the route when selecting the next-hop node, that is, to select the neighbor node closest to the destination. However, in actual operation, the candidate vehicle node with the largest single-hop distance is often at the edge of the communication range, with poor link stability, which can easily lead to transmission failure. In terms of road section selection, the existing scheme uses the method of periodically broadcasting routing control packets within or between road sections to collect information, which has a large network resource overhead and can only obtain local road section information. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a V2V information routing transmission method between urban roads for vehicle networking, aiming to reduce the average end-to-end delay and packet loss rate of V2V communication.

[0007] The present invention proposes a next-hop selection scheme based on a fuzzy inference system, which comprehensively considers factors such as the location of neighboring vehicle nodes, link duration, channel quality, and message queue idle rate to select the best forwarding node. In order to overcome the "short-sightedness" problem caused by local information, global information-road vehicle density is introduced as a road weight factor, and the global transmission path is calculated by the Dijkstra algorithm. In order to reduce the impact of obstacles on the cross-road transmission of information near the intersection, a dynamic group forwarding model for the intersection is established to make full use of the communication resources of the vehicle nodes within the intersection to provide reliable relay for the cross-road transmission of information.

[0008] The present invention also proposes a V2V information routing transmission system between urban roads for vehicle networking.

[0009] Terminology explanation:

[0010] 1. V2V, Vehicle-to-Vehicle, a wireless communication technology between vehicles;

[0011] 2. Dijkstra algorithm, a classic algorithm proposed by Dutch computer scientist Edsger Dijkstra for calculating the shortest path between two points in a graph;

[0012] 3. Fuzzy Inference System (FIS) is a reasoning model based on fuzzy set theory and fuzzy logic, which is used to simulate the human decision-making process. It can flexibly process fuzzy information in the real world, process multiple input variables and draw conclusions from complex nonlinear relationships. In the process of packet transmission, routing performance is affected by multiple variables such as single-hop distance, link duration, signal strength and queuing delay. The fuzzy inference system can combine these multiple input variables, calculate the output through rule evaluation and reasoning, and thus select appropriate neighbor vehicles as forwarding nodes to improve routing performance. This process does not require a precise mathematical model and has strong fault tolerance, flexibility, interpretability and scalability. Figure 4 This is the architecture diagram of the fuzzy reasoning system. Fuzzy reasoning mainly includes three steps, namely fuzzification, reasoning process, and defuzzification. Among them, fuzzification converts the input clear data into fuzzy sets, and expresses the membership of each input in each fuzzy set through the membership function; the reasoning process is to reason on the fuzzy input according to the fuzzy rule base, calculate the output of each rule, and combine the output of all rules into a comprehensive fuzzy output; defuzzification is to convert the aggregated fuzzy output into a clear value.

[0013] The technical solution of the present invention is:

[0014] A V2V information routing transmission method for urban roads in a vehicle network, comprising:

[0015] According to the real-time road traffic density information, the Dijkstra algorithm is used to calculate the global path of the group routing;

[0016] When a data packet is transmitted within a road section, a fuzzy inference system is used to comprehensively evaluate the quality scores of neighboring vehicles and select the vehicle with the highest score as the next hop node;

[0017] When data packets need to be transmitted across roads, a dynamic group forwarding strategy for intersections is adopted to fully utilize vehicles within the intersection as cross-road transmission relay nodes to achieve V2V information routing transmission.

[0018] Preferably, according to the present invention, the global path of packet routing is calculated using the Dijkstra algorithm based on real-time road traffic density information; comprising:

[0019] The urban road map is represented as an undirected weighted graph G(V, E). For any two adjacent intersections I i , I j ∈V, the road segment between them is represented as (I i , I j)∈E, where V and E are finite sets of intersections and roads respectively. The vehicle density in the road section is used as an indicator to measure the quality factor of the road section as a route selection. i , I j The road weight ω(I i , I j ) is expressed as:

[0020]

[0021] Among them, K ub , K lb are the upper and lower bounds of the road vehicle density suitable for group routing, μ is the proportional factor, For roads (I i , I j ) Actual traffic density, N ij , L ij They are roads (I i , I j ) Number of vehicles and length of road sections;

[0022] The corresponding objective function of global path planning is expressed as:

[0023]

[0024] Where I0=I s , I n =I d ;

[0025] The source vehicle node executes the Dijkstra algorithm to calculate the global path of the packet routing; including:

[0026] The cost function is defined as:

[0027]

[0028] The cost function represents the distance from the source intersection I0 to any intersection I k The required cost is initialized to:

[0029]

[0030] Construct a minimum heap, the heap element structure is {I k ,cost(I k ), {I0, ..., I k}}, the first item is the intersection number, the second and third items are the source intersection I0 to intersection I k The required cost and corresponding path points;

[0031] Put the elements {I0, 0, {I0}} into the heap and repeat the following steps until the number of elements in the heap is zero; including:

[0032] a) The top element of the heap {I top ,cost(I top ), {I0, ..., I top}}Out of the stack;

[0033] b) Traversal I top Adjacent intersection L next , if cost(I top )+w(I top , I next )<cost(I next ), then the element {I next ,cost(I top )+w(I top ,I next ),{I0,...,I top , I next}}Enter the heap and update the heap arrangement;

[0034] Finally, the global path points {I0, I1, ..., I n}, inserted into the packet header as a path label, and transmission begins.

[0035] Preferably, according to the present invention, when a data packet is transmitted in a road section, a fuzzy inference system is used to comprehensively evaluate the quality scores of neighboring vehicles, and the vehicle with the highest score is selected as the next hop node; including:

[0036] The surrounding vehicles sense the presence of the current vehicle by receiving beacons and store the current vehicle related information in the neighbor list; the current vehicle related information includes location, speed and routing queue idle rate; when the vehicle sends a packet or relays a packet, it obtains the path label from the packet header and extracts the current intersection coordinate information in are the horizontal and vertical coordinates of the current intersection respectively;

[0037] Neighbor vehicles include candidate neighbor vehicles and non-candidate neighbor vehicles. Candidate neighbor vehicles c n The following constraints are met:

[0038]

[0039] Among them, c0 represents the current vehicle, represents the set of neighboring vehicles of vehicle c0;

[0040] The quality scores of candidate neighbor vehicles are calculated through a fuzzy inference system; the fuzzy inference system has four input variables, namely, distance X1, link time X2, signal strength X3, and routing queue idle rate X4, and an output parameter, namely, the quality score Q;

[0041] The distance is defined as:

[0042]

[0043] Among them, c0 is the current vehicle, c n is the candidate neighbor vehicle, x and y are the horizontal and vertical coordinates of the vehicle respectively; the input variable distance is divided into three levels: Short, Medium, and Long. These three levels constitute the fuzzy set of distance, recorded as:

[0044]

[0045] in, Respectively indicate the degree to which the distance belongs to Short, Medium, and Long;

[0046] Link time is defined as:

[0047]

[0048] in,

[0049]

[0050] Where D and ΔV represent the distance vector and speed difference vector between the main vehicle and the candidate neighbor vehicles, respectively, and R is the vehicle communication range. The input variable link time is divided into two levels: Short and Long. These two levels constitute the fuzzy set of link time, which is recorded as:

[0051]

[0052] in, Respectively indicate the degree to which the link time belongs to Short and Long;

[0053] The signal strength is expressed as:

[0054] X3(c n )(dB)=P t (dB)+G t (dB)+G r (dB)-L p (dB) (11);

[0055] Among them, P t is the transmitted signal power, G t , G r are the transmit and receive antenna gains, Lp is the path loss, including free space propagation loss, shadow fading and multipath fading; the input variable signal strength is divided into two levels, Low and High, which constitute the fuzzy set of signal strength, recorded as:

[0056]

[0057] in, Respectively indicate the degree to which the signal strength belongs to Low and High;

[0058] The routing queue idle rate is defined as:

[0059]

[0060] Among them, k and r represent the idle and occupied space size of the routing queue respectively. The input variable routing queue idle rate is divided into two levels: Low and High. These two levels constitute the fuzzy set of routing queue idle rate, which is recorded as:

[0061]

[0062] in, Indicates the degree of signal strength being Low and High respectively.

[0063] A fuzzy inference system is used to comprehensively evaluate the quality scores of neighbor vehicles, including:

[0064] Fuzzification of input variables; the continuous input variable value X is fuzzified by membership function. i Mapping to predefined fuzzy sets middle;

[0065] Rule matching and fuzzy reasoning; construct fuzzy rules based on empirical knowledge, and determine the activation strength of each rule through the fuzzy rule base according to the fuzzy set to which the input value belongs;

[0066] Fuzzy reasoning; based on the calculated activation strength of each rule, the fuzzy set of output variables is derived

[0067] in, They represent the degree to which the output variable Q belongs to the grades Worst, Bad, Tolerable, Good, and Perfect, respectively;

[0068] Output defuzzification: converting the inference results into specific clear values ​​based on the membership function and the fuzzy set of the output variable;

[0069] Repeat the above steps to calculate the quality scores of all candidate neighbor nodes, and select the node with the highest quality score as the next hop node.

[0070] Further preferably, the centroid method is used for defuzzification:

[0071]

[0072] Preferably, according to the present invention, when a data packet needs to be transmitted across roads, a dynamic group forwarding strategy for an intersection is adopted to make full use of vehicles within the intersection as relay nodes for cross-road transmission; including:

[0073] Define an influence range R of the intersection relay , that is, the distance between the vehicle and the center of the intersection (x I ,y I ) of the maximum permissible distance; if vehicle c i The location meets:

[0074]

[0075] Then vehicle c i As a possible cross-road transmission relay node, that is, a dynamic group node at an intersection; calculate the set of potential relay vehicles in the vicinity of an intersection Include all vehicles that meet formula (16); take the distance from the intersection center as the consideration for selecting vehicles; the nodes in the vehicle group are regarded as candidate vehicle nodes, and the normalized value of the distance between the candidate vehicle and the intersection center is defined as:

[0076]

[0077] Among them, d i Indicates the distance between the vehicle and the center of the intersection, d max and d min They represent the maximum and minimum distances between the vehicles in the group and the intersection, respectively;

[0078] Vehicle i The probability of being selected as a relay is set to:

[0079]

[0080] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a V2V information routing transmission method between urban roads for a vehicle network are implemented.

[0081] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a V2V information routing transmission method between urban roads in a vehicle network.

[0082] A V2V information routing transmission system for urban roads in a vehicle network, comprising:

[0083] The group routing global path calculation unit is configured to: calculate the group routing global path using the Dijkstra algorithm according to the real-time road traffic density information;

[0084] The next-hop node selection unit is configured to: when a data packet is transmitted in a road section, comprehensively evaluate the quality scores of neighboring vehicles using a fuzzy inference system and select the vehicle with the highest score as the next-hop node;

[0085] The relay node acquisition unit is configured to: when a data packet needs to be transmitted across roads, adopt a dynamic group forwarding strategy at an intersection, make full use of vehicles within the intersection range as relay nodes for cross-road transmission, and realize V2V information routing transmission.

[0086] The beneficial effects of the present invention are:

[0087] This paper proposes a distributed routing framework (FRUV) based on fuzzy inference system for V2V communication tasks in urban vehicle networks, aiming to build the best path for information transmission in complex vehicle environments to reduce the average end-to-end delay and packet loss rate of V2V communication. FRUV includes three main innovations:

[0088] 1) A next-hop selection scheme based on a fuzzy reasoning system is proposed, and a multi-index evaluation system is used to comprehensively evaluate the quality of candidate nodes, effectively improving the reliability and stability of the link.

[0089] 2) Design a global path planning algorithm for information transmission based on real-time road traffic density to ensure efficient transmission of data packets on roads with appropriate vehicle density.

[0090] 3) A dynamic group forwarding strategy for intersections is proposed to prevent packet loss caused by signal shadow fading and vehicle direction changes, while maintaining the reliability of data packet transmission across roads and reducing dependence on infrastructure. The present invention comprehensively considers the real-time traffic flow information and local vehicle information of the road to plan the transmission path. The proposed relay selection method based on the fuzzy inference system not only effectively improves the transmission success rate of multi-hop links, but also helps to balance the routing load between vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 It is a schematic diagram of the present invention using Dijkstra algorithm to calculate the global path of packet routing;

[0092] Figure 2 It is a schematic diagram of the format of the vehicle periodic broadcast beacon message of the present invention;

[0093] Figure 3 It is a schematic diagram of the transmission scheme within the road section of the present invention;

[0094] Figure 4 It is the structural diagram of the fuzzy reasoning system of the present invention;

[0095] Figure 5 It is a schematic diagram of the membership function of the input variable distance of the fuzzy inference system of the present invention;

[0096] Figure 6 It is a schematic diagram of the membership function of the input variable link time of the fuzzy inference system of the present invention;

[0097] Figure 7 It is a schematic diagram of the membership function of the signal strength of the input variable of the fuzzy inference system of the present invention;

[0098] Figure 8 It is a schematic diagram of the membership function of the input variable routing queue idle rate of the fuzzy inference system of the present invention;

[0099] Fig. 9 It is a schematic diagram of the membership function of the output variable of the fuzzy inference system of the present invention;

[0100] Fig.10 It is a schematic diagram of intersection group forwarding of the present invention;

[0101] Fig.11 1 is a schematic diagram comparing the data packet transmission success rate (PDR) performance of the present invention with that of the GPSR protocol under different numbers of vehicles;

[0102] Fig.12 It is a schematic diagram comparing the average delay performance of the present invention with that of the GPSR protocol under different numbers of vehicles. DETAILED DESCRIPTION

[0103] The present invention will be further defined below in conjunction with the accompanying drawings and embodiments, but is not limited thereto.

[0104] Example 1

[0105] A V2V information routing transmission method for urban roads in a vehicle network, comprising:

[0106] According to the real-time road traffic density information, the Dijkstra algorithm is used to calculate the global path of the group routing;

[0107] When a data packet is transmitted within a road section, a fuzzy inference system is used to comprehensively evaluate the quality scores of neighboring vehicles and select the vehicle with the highest score as the next hop node;

[0108] When data packets need to be transmitted across roads, the dynamic group forwarding strategy at the intersection is adopted to make full use of vehicles within the intersection as cross-road transmission relay nodes to achieve V2V information routing transmission and improve the transmission success rate.

[0109] Example 2

[0110] The difference between the V2V information routing transmission method between urban roads in the vehicle network described in Example 1 is that:

[0111] According to the real-time road traffic density information, the Dijkstra algorithm is used to calculate the global path of the group routing; including:

[0112] like Figure 1 As shown, the urban road map is represented as an undirected weighted graph G(V, E). For any two adjacent intersections I i , I j ∈V, the road segment between them is represented as (I i , I j )∈E, where V and E are finite sets of intersections and roads respectively. The vehicle density in the road section is used as an indicator to measure the quality factor of the road section as a route selection. i , I j The road weight ω(I i , I j ) is expressed as:

[0113]

[0114] Among them, K ub , K lb are the upper and lower bounds of the road vehicle density suitable for group routing, μ is the proportional factor, For roads (I i , I j ) Actual traffic density, N ij , L ij They are roads (I i , I j ) Number of vehicles and length of road sections;

[0115] The source vehicle node can obtain the location information of the destination vehicle through the positioning service, and obtain the source vehicle road section information through the digital map. and destination vehicle section information in are the intersections at both ends of the road section where the source vehicle is located, The intersections at both ends of the road section where the destination vehicle is located; thus, four groups of candidate source-destination intersection pairs can be formed Finally, the group with the smallest distance is determined as the source-destination intersection (I s , I d ),like Figure 1 shown.

[0116] The corresponding objective function of global path planning is expressed as:

[0117]

[0118] Where I0=I s , I n =I d ;

[0119] The source vehicle node executes the Dijkstra algorithm to calculate the global path of the packet routing; including:

[0120] The cost function is defined as:

[0121]

[0122] The cost function represents the distance from the source intersection I0 to any intersection I k The required cost is initialized to:

[0123]

[0124] Construct a minimum heap, the heap element structure is {I k ,cost(I k ), {I0, ..., I k}}, the first item is the intersection number, the second and third items are the source intersection I0 to intersection I k The required cost and corresponding path points;

[0125] Put the elements {I0, 0, {I0}} into the heap and repeat the following steps until the number of elements in the heap is zero; including:

[0126] c) The top element of the heap {I top ,cost(I top ), {I0, ..., I top}}Out of the stack;

[0127] d) Traversal I top Adjacent intersection I next , if cost(I top )+w(I oup , I next )<cost(I next ), then the element {I next ,cost(I top )+w(I top , I next ), {I0, ..., I top , I next}}Enter the heap and update the heap arrangement;

[0128] Finally, the global path points {I0, I1, ..., I n}, inserted into the packet header as a path label, and transmission begins.

[0129] When a data packet is transmitted within a road section, a fuzzy inference system is used to comprehensively evaluate the quality scores of neighboring vehicles and select the vehicle with the highest score as the next hop node; including:

[0130] Beacon is a specific broadcast message sent by a vehicle, mainly used to transmit the identity, location and other related information of the vehicle within a certain range. It acts like an identifier and can be received by surrounding vehicles to obtain the status information of surrounding vehicles in real time. Beacons are usually broadcast periodically at fixed time intervals to ensure that nearby vehicles can perceive the existence and status of the vehicle in a timely manner.

[0131] The format of the vehicle's periodic broadcast beacon message is as follows: Figure 2 As shown, surrounding vehicles sense the presence of the current vehicle by receiving beacons and store the current vehicle-related information in the neighbor list; the current vehicle-related information includes location, speed, and routing queue idle rate; the neighbor list is a table maintained by each vehicle node, which is used to store information about other nodes directly connected to it. This list contains the identifier, location, speed, and routing queue status information of the neighbor node. The neighbor list is usually dynamic, and the connection between the node and other nodes may change over time. Therefore, the neighbor list needs to be updated regularly. Each node maintains a timeout value in the neighbor list. When there is no communication with the neighbor for a certain period of time, the neighbor will be deleted from the list. When the vehicle sends a packet or relays a packet, it obtains the path label from the packet header and extracts the current intersection coordinate information in are the horizontal and vertical coordinates of the current intersection respectively;

[0132] Neighbor vehicles include candidate neighbor vehicles and non-candidate neighbor vehicles. Candidate neighbor vehicles c n The following constraints are met:

[0133]

[0134] Among them, c0 represents the current vehicle, represents the set of neighboring vehicles of vehicle c0;

[0135] The quality scores of candidate neighbor vehicles are calculated through a fuzzy inference system; the fuzzy inference system has four input variables, namely, distance X1, link time X2, signal strength (RSSI) X3, and routing queue idle rate X4, and an output parameter, namely, the quality score Q;

[0136] The distance is defined as:

[0137]

[0138] Among them, c0 is the current vehicle, c n is the candidate neighbor vehicle, x and y are the horizontal and vertical coordinates of the vehicle respectively; the input variable distance is divided into three levels: Short, Medium, and Long. These three levels constitute the fuzzy set of distance, recorded as:

[0139]

[0140] in, Respectively indicate the degree to which the distance belongs to Short, Medium, and Long;

[0141] Link time is defined as:

[0142]

[0143] in,

[0144]

[0145] Where D and ΔV represent the distance vector and speed difference vector between the main vehicle and the candidate neighbor vehicles, respectively, and R is the vehicle communication range. The input variable link time is divided into two levels: Short and Long. These two levels constitute the fuzzy set of link time, which is recorded as:

[0146]

[0147] in, Respectively indicate the degree to which the link time belongs to Short and Long;

[0148] Considering the path loss model as Friis loss plus Nakagami fading, the received signal strength (RSSI) can be expressed as a combination of these two effects. The path loss model is a commonly used path loss model in wireless communications. It is used to describe the loss of electromagnetic waves propagating from one antenna to another in an ideal free space without the influence of obstacles, reflections or refractions. The formula is:

[0149]

[0150] Among them, P r , P t are the received and transmitted signal powers, G t , G r are the transmit and receive antenna gains, λ is the signal wavelength, and d is the transmission distance. In decibel (dB) form, the path loss is expressed as:

[0151]

[0152] The path loss model assumes that the signal propagates in a straight line between the two antennas. However, in actual environments, signals usually experience multipath propagation due to reflection or scattering from obstacles such as buildings and the ground. The multipath effect will cause delay spread and fading of the signal, which causes the strength of the received signal to fluctuate. Therefore, it is considered to superimpose Nakagami fading to simulate a more realistic propagation environment. The Nakagami fading model describes the fading in the wireless channel. It models the received signal amplitude through the Nakagami-m distribution. Assuming that the received signal power x obeys the Nakagami fading distribution, its probability density function (PDF) is defined as:

[0153]

[0154] Where m is the fading factor and Ω is the average received power;

[0155] Signal strength (RSSI) is expressed as:

[0156] X3(c n )(dB)=P t (dB)+G t (dB)+G r (dB)-L p (dB) (11);

[0157] Among them, L p Including Friis loss and Nakagami fading; the input variable signal strength is divided into two levels: Low and High. These two levels constitute the fuzzy set of signal strength, recorded as:

[0158]

[0159] in, Respectively indicate the degree to which the signal strength belongs to Low and High;

[0160] The routing queue idle rate is defined as:

[0161]

[0162] Among them, k and r represent the idle and occupied space size of the routing queue respectively. The input variable routing queue idle rate is divided into two levels: Low and High. These two levels constitute the fuzzy set of routing queue idle rate, which is recorded as:

[0163]

[0164] in, Indicates the degree of signal strength being Low and High respectively.

[0165] A fuzzy inference system is used to comprehensively evaluate the quality scores of neighbor vehicles, including:

[0166] Fuzzification of input variables; the continuous input variable value X is fuzzified by membership function. i Mapping to predefined fuzzy sets The membership functions of the four input parameters are as follows: Figure 5-8 shown. Figure 5 is the membership function graph of the input variable distance; Figure 6 is a schematic diagram of the membership function of the input variable link time; Figure 7 It is a schematic diagram of the membership function of the input variable signal strength; Figure 8 It is a schematic diagram of the membership function of the input variable routing queue idle rate; taking the input variable X1 as an example, it corresponds to the three levels of Short, Medium, and Long to different degrees, so a value is needed to measure the degree to which it corresponds to different levels. This value is called the membership value, such as Figure 5 As shown in the vertical axis, its value range is [0,1]. The mapping relationship between the distance variable X1 and the membership value is represented by the membership function. Figure 5 The three function curves in the figure represent the membership function of the input variable X1 corresponding to the three levels of Short, Medium, and Long. When X1 = 200m, it corresponds to Long and Medium to a higher degree, and the membership value is are 0.5 and 0.5 respectively; the corresponding degree of Short is lower, and the membership value is 0. At this time, the fuzzy set corresponding to the input variable X1 is

[0167] Rule matching and fuzzy reasoning; construct fuzzy rules based on empirical knowledge, as shown in Table 1:

[0168]

[0169] Through the fuzzy rule base, the activation strength of each rule is determined according to the fuzzy set to which the input value belongs;

[0170] Fuzzy rules are a series of "IF-THEN" statements. If the input variable X1 is Short, X2 is Short, X3 is Low, and X4 is Low, then according to the first rule in Table 1, the output Q corresponds to Worst. The activation strength of the rule is determined by the membership value of the input variable, which is calculated using the "minimum method", namely:

[0171]

[0172] in, is the activation intensity corresponding to the first rule.

[0173] Fuzzy reasoning; using Mamdani fuzzy reasoning method, the fuzzy set of output variables is derived based on the activation strength of each rule calculated in, They represent the degree to which the output variable Q belongs to the grades Worst, Bad, Tolerable, Good, and Perfect, respectively;

[0174] When there are multiple rules corresponding to the same level of output variable Q, the “maximum method” is used for calculation. For example, the levels of output variable Q corresponding to rules 1, 2, 3, 5, and 11 are all Worst, and the activation strengths are Then the degree to which the output variable Q belongs to the level Worst is

[0175]

[0176] Output defuzzification: The purpose of this step is to convert the result of fuzzy reasoning into a specific and clear output value. The defuzzification process converts the fuzzy value in the fuzzified output set into a specific value as the final decision output. The membership function of the output variable is as follows: Fig. 9 As shown, the inference results are converted into specific clear values ​​according to the membership function and the fuzzy set of the output variable;

[0177] Repeat the above steps to calculate the quality scores of all candidate neighbor nodes, and select the node with the highest quality score as the next hop node.

[0178] Defuzzification using the centroid method:

[0179]

[0180] When data packets need to be transmitted across roads, a dynamic group forwarding strategy for intersections is adopted to make full use of vehicles within the intersection range as relay nodes for cross-road transmission; including:

[0181] Define an influence range R of the intersection relay , that is, the distance between the vehicle and the center of the intersection (x I ,y I ) of the maximum permissible distance; if vehicle c i The location meets:

[0182]

[0183] Then vehicle c i As a possible cross-road transmission relay node, that is, a dynamic group node at an intersection; calculate the set of potential relay vehicles in the vicinity of an intersection Includes all vehicles that meet formula (16); since the vehicles near the intersection are constantly changing, the vehicle set is also constantly updated. It should be noted that for a vehicle near the intersection in the road section, the vehicle group it perceives is actually the set When a data packet arrives near an intersection and needs to be transmitted across roads, the nodes in the vehicle group will provide relays to ensure that the data packet flows between adjacent sections. The closer the vehicle in the group is to the intersection, the shorter the communication link with the adjacent road vehicles may be, and the greater the probability of unobstructed communication, thereby reducing signal attenuation and improving transmission reliability. Therefore, the distance to the center of the intersection is used as a consideration for selecting vehicles; the nodes in the vehicle group are regarded as candidate vehicle nodes, and the normalized value of the distance between the candidate vehicle and the center of the intersection is defined as:

[0184]

[0185] Among them, d i Indicates the distance between the vehicle and the center of the intersection, d max and d min They represent the maximum and minimum distances between the vehicles in the group and the intersection, respectively;

[0186] Vehicle i The probability of being selected as a relay is set to:

[0187]

[0188] Vehicles closer to the center of the intersection will have a greater probability of being selected, and this probability-based selection method will help balance the routing queue load of vehicles in the group.

[0189] Figure 1 , 3 , 11 give a specific example of the routing transmission solution proposed in the present invention. Figure 1 The red vehicle in the lower left corner is the source vehicle, generating a data packet, and the red vehicle in the upper right corner is the destination vehicle. Intersections I0 and I8 are the source intersection and destination intersection, respectively. The Dijkstra algorithm is used to search for the optimal path between the source and destination intersections (I0, I1, I4, I7, I8), that is, the data packet needs to be transmitted to intersections I0, I1, I4, I7, and I8 in sequence. The specific transmission process can be divided into intra-section transmission and inter-section transmission. The fuzzy inference system proposed in the present invention is used to select the next hop node for intra-section transmission, and the dynamic group forwarding strategy for intersections proposed in the present invention is used to select a reliable relay for inter-section transmission. First is the intra-section transmission process. It can be seen from the global path that the data packet needs to be transmitted to intersection I0 first. Figure 3There are four neighboring vehicles around the central source vehicle c0. According to formula (5), only c1, c2, and c3 are candidate neighboring vehicles. The fuzzy inference system determines that vehicle c2 has the highest score, so the data packet is forwarded to c2. Repeat this process until the data packet arrives near intersection I0. The transmission process within the sections (I0, I1), (I1, I4), (I4, I7), and (I7, I8) is similar to the above. The second is the transmission process between sections. When the data packet arrives near intersection I1 and needs to be forwarded to intersection I4, Fig.10 As shown, the vehicle group at intersection I1 consists of three vehicles, c2, c3, and c4. Since c4 is far away from c0, c0 cannot correctly receive the beacon message sent by c4 and cannot sense the existence of c4. Therefore, at intersection I1, vehicles c2 and c3 will provide relays for c1. According to the probability allocation scheme in the intersection dynamic group forwarding strategy proposed by the present invention, candidate nodes c2 and c3 are assigned relay probabilities, and c1 selects a relay node for data packet forwarding based on the relay probability. Repeat the transmission within the road section and forwarding between road sections until the data packet is successfully delivered to the destination vehicle.

[0190] Fig.11 It is a performance comparison diagram of the data packet transmission success rate (PDR) of the present invention and the GPSR protocol under different numbers of vehicles; Fig.12 This is a comparison chart of the average delay performance of the present invention and the GPSR protocol under different numbers of vehicles.

[0191] Example 3

[0192] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a V2V information routing transmission method between urban roads for vehicle networking as described in Example 1 or 2 are implemented.

[0193] Example 4

[0194] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of a V2V information routing transmission method between urban roads for a vehicle network as described in Embodiment 1 or 2 are implemented.

[0195] Example 5

[0196] A V2V information routing transmission system for urban roads in a vehicle network, comprising:

[0197] The group routing global path calculation unit is configured to: calculate the group routing global path using the Dijkstra algorithm according to the real-time road traffic density information;

[0198] The next-hop node selection unit is configured to: when a data packet is transmitted in a road section, comprehensively evaluate the quality scores of neighboring vehicles using a fuzzy inference system and select the vehicle with the highest score as the next-hop node;

[0199] The relay node acquisition unit is configured to: when a data packet needs to be transmitted across roads, adopt a dynamic group forwarding strategy at an intersection, make full use of vehicles within the intersection range as relay nodes for cross-road transmission, and realize V2V information routing transmission, thereby improving the transmission success rate.

Claims

1. A V2V information routing transmission method for urban roads in a vehicle network, characterized in that: include: According to the real-time road traffic density information, the Dijkstra algorithm is used to calculate the global path of the group routing; When a data packet is transmitted within a road section, a fuzzy inference system is used to comprehensively evaluate the quality scores of neighboring vehicles and select the vehicle with the highest score as the next hop node; When data packets need to be transmitted across roads, a dynamic group forwarding strategy for intersections is adopted to fully utilize vehicles within the intersection as cross-road transmission relay nodes to achieve V2V information routing transmission.

2. A V2V information routing transmission method for urban roads in a vehicle network according to claim 1, characterized in that: According to the real-time road traffic density information, the Dijkstra algorithm is used to calculate the global path of the group routing; including: The urban road map is represented as an undirected weighted graph G(V, E). For any two adjacent intersections I i , I j ∈V, the road segment between them is represented as (I i , I j )∈E, where V and E are finite sets of intersections and roads respectively. The vehicle density in the road section is used as an indicator to measure the quality factor of the road section as a route selection. i , I j The road weight ω(I i , I j ) is expressed as: Among them, K ub , K lb are the upper and lower bounds of the road vehicle density suitable for group routing, μ is the proportional factor, For roads (I i , I j ) Actual traffic density, N ij , L ij They are roads (I i , I j ) Number of vehicles and length of road sections; The corresponding objective function of global path planning is expressed as: Among them, I0=I s , I n =I d ; The source vehicle node executes the Dijkstra algorithm to calculate the global path of the packet routing; including: The cost function is defined as: The cost function represents the distance from the source intersection I0 to any intersection I k The required cost is initialized to: Construct a minimum heap, the heap element structure is {I k ,cost(I k ), {I0, ..., I k }}, the first item is the intersection number, the second and third items are the source intersection I0 to intersection I k The required cost and corresponding path points; Put the elements {I0, 0, {I0}} into the heap and repeat the following steps until the number of elements in the heap is zero; including: a) The top element of the heap {I top ,cost(I top ), {I0,…,I top }}Out of the stack; b) Traversal I top Adjacent intersection I next , if cost(I top )+ω(I top , I next )<cost(I next ), then the element {I next ,cost(I top )+w(I top , I next ), {I0, ..., I top , I next }}Enter the heap and update the heap arrangement; Finally, the global path points {I0, I1, ...., I n }, inserted into the packet header as a path label, and transmission begins.

3. The V2V information routing transmission method for urban roads in a vehicle network according to claim 1, characterized in that: When a data packet is transmitted within a road section, a fuzzy inference system is used to comprehensively evaluate the quality scores of neighboring vehicles and select the vehicle with the highest score as the next hop node; including: The surrounding vehicles sense the presence of the current vehicle by receiving beacons and store the current vehicle related information in the neighbor list; the current vehicle related information includes location, speed and routing queue idle rate; when the vehicle sends a packet or relays a packet, it obtains the path label from the packet header and extracts the current intersection coordinate information in are the horizontal and vertical coordinates of the current intersection respectively; Neighbor vehicles include candidate neighbor vehicles and non-candidate neighbor vehicles. Candidate neighbor vehicles c n The following constraints are met: Among them, c0 represents the current vehicle, represents the set of neighboring vehicles of vehicle c0; The quality scores of candidate neighbor vehicles are calculated through a fuzzy inference system; the fuzzy inference system has four input variables, namely, distance X1, link time X2, signal strength X3, and routing queue idle rate X4, and an output parameter, namely, the quality score Q; The distance is defined as: Among them, c0 is the current vehicle, c n is the candidate neighbor vehicle, x and y are the horizontal and vertical coordinates of the vehicle respectively; the input variable distance is divided into three levels: Short, Medium, and Long. These three levels constitute the fuzzy set of distance, recorded as: in, Respectively indicate the degree to which the distance belongs to Short, Medium, and Long; Link time is defined as: in, Where D and ΔV represent the distance vector and speed difference vector between the main vehicle and the candidate neighbor vehicles, respectively, and R is the vehicle communication range. The input variable link time is divided into two levels: Short and Long. These two levels constitute the fuzzy set of link time, which is recorded as: in, Respectively indicate the degree to which the link time belongs to Short and Long; The signal strength is expressed as: X3(c n )(dB)=P t (dB)+G t (dB)+G r (dB)-L p (dB) (11); Among them, L p Including Friis loss and Nakagami fading; the input variable signal strength is divided into two levels: Low and High. These two levels constitute the fuzzy set of signal strength, recorded as: in, Respectively indicate the degree to which the signal strength belongs to Low and High; The routing queue idle rate is defined as: Among them, k and r represent the idle and occupied space size of the routing queue respectively. The input variable routing queue idle rate is divided into two levels: Low and High. These two levels constitute the fuzzy set of routing queue idle rate, which is recorded as: in, Respectively indicate the degree to which the signal strength belongs to Low and High; A fuzzy inference system is used to comprehensively evaluate the quality scores of neighbor vehicles, including: Fuzzification of input variables; the continuous input variable value X is fuzzified by membership function. i Mapping to predefined fuzzy sets middle; Rule matching and fuzzy reasoning; construct fuzzy rules based on empirical knowledge, and determine the activation strength of each rule through the fuzzy rule base according to the fuzzy set to which the input value belongs; Fuzzy reasoning; based on the calculated activation strength of each rule, the fuzzy set of output variables is derived in, They represent the degree to which the output variable Q belongs to the grades Worst, Bad, Tolerable, Good, and Perfect, respectively; Output defuzzification: converting the inference results into specific clear values ​​based on the membership function and the fuzzy set of the output variable; Repeat the above steps to calculate the quality scores of all candidate neighbor nodes, and select the node with the highest quality score as the next hop node.

4. The V2V information routing transmission method for urban roads in a vehicle network according to claim 1, characterized in that: Defuzzification using the centroid method:

5. The V2V information routing transmission method for urban roads in a vehicle network according to claim 1, characterized in that: When data packets need to be transmitted across roads, a dynamic group forwarding strategy for intersections is adopted to make full use of vehicles within the intersection range as relay nodes for cross-road transmission; including: Define an influence range R of the intersection relay , that is, the distance between the vehicle and the center of the intersection (x I ,y I ) of the maximum permissible distance; if vehicle c i The location meets: Then vehicle c i As a possible cross-road transmission relay node, that is, a dynamic group node at an intersection; calculate the set of potential relay vehicles in the vicinity of an intersection Include all vehicles that meet formula (16); take the distance from the intersection center as the consideration for selecting vehicles; the nodes in the vehicle group are regarded as candidate vehicle nodes, and the normalized value of the distance between the candidate vehicle and the intersection center is defined as: Among them, d i Indicates the distance between the vehicle and the center of the intersection, d max and d min They represent the maximum and minimum distances between the vehicles in the group and the intersection, respectively; Vehicle i The probability of being selected as a relay is set to:

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of a V2V information routing transmission method between urban roads for vehicle networking are implemented as described in any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a V2V information routing transmission method between urban roads for vehicle networking are implemented as described in any one of claims 1-5.

8. A V2V information routing transmission system for urban roads in a vehicle network, characterized in that: include: The group routing global path calculation unit is configured to: calculate the group routing global path using the Dijkstra algorithm according to the real-time road traffic density information; The next-hop node selection unit is configured to: when a data packet is transmitted in a road section, comprehensively evaluate the quality scores of neighboring vehicles using a fuzzy inference system and select the vehicle with the highest score as the next-hop node; The relay node acquisition unit is configured to: when a data packet needs to be transmitted across roads, adopt a dynamic group forwarding strategy at an intersection, make full use of vehicles within the intersection range as relay nodes for cross-road transmission, and realize V2V information routing transmission.

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