Vehicle networking intersection routing method and system based on fuzzy multi-factor decision

Abstract

The invention belongs to the technical field of Internet of Vehicles intersection routing, and particularly relates to an Internet of Vehicles intersection routing method and system based on fuzzy multi-factor decision. Performing fuzzy language quantization on three evaluation factors of the lane number, the direction angle and the traffic flow of the candidate road section by using a triangular fuzzy number; a clear value is obtained through defuzzification, a standard value is obtained through standardization, evaluation factor information entropy is calculated based on the standard value, and a dynamic weight value on each evaluation factor is obtained after normalization; and calculating a comprehensive fuzzy number score of each candidate road section, performing defuzzification to obtain a clear optimal non-fuzzy performance value BNP, and selecting the road section with the highest BNP value as a target road section. The technical scheme provided by the invention has the effects of high decision accuracy, strong adaptability, good robustness and strong pertinence.

Description

Technical Field

[0001] This invention belongs to the field of vehicle-to-everything (V2X) intersection routing technology, specifically relating to a V2X intersection routing method and system based on fuzzy multi-factor decision-making. Background Technology

[0002] Vehicular Ad Hoc Networks (VANETs) are applications of traditional Mobile Ad Hoc Networks (MANETs) on transportation routes. They share some characteristics with MANETs, ​​such as autonomy, lack of fixed structure, multi-hop routing, and dynamic changes in network topology. However, VANETs operate in more unique environments, such as narrow roads, high-density node distribution, and high-speed node movement. This makes some routing protocols suitable for MANETs perform poorly in VANETs, ​​necessitating the development of specialized routing technologies tailored to the specific characteristics of VANETs.

[0003] In urban vehicle-to-everything (VANET) environments, network topology is highly dynamic and strictly constrained by road infrastructure. Data packets must follow road routes and can only change direction at intersections. Therefore, intersections are not only physical traffic hubs but also critical decision points for data flow. An efficient intersection routing strategy is crucial for ensuring the reliability and real-time performance of communication across the entire urban VANET network. However, existing mainstream routing protocols have significant shortcomings: Static or single decision criteria: Many geographic routing protocols (such as GPSR) select the next hop solely based on the Euclidean distance to the destination, which can easily lead to the "local maxima" problem in complex urban scenarios. Even if some protocols consider link stability or multi-hop information, their evaluation factors are often fixed and cannot be adaptively adjusted according to rapidly changing traffic conditions (such as sudden congestion or lane closures).

[0004] Ignoring the dynamic relationship between attributes: At a certain moment, some evaluation factors (such as direction angle) may be extremely similar to all candidate road segments (homogenization). If they are still given high weights, it will seriously interfere with the judgment of truly discriminative evaluation factors (such as a road suddenly becoming extremely smooth).

[0005] Lack of modeling for uncertainty: Key assessment factors such as vehicle numbers and traffic flow are inherently fuzzy and uncertain. Using precise numerical models for decision-making makes it difficult to accurately capture this inherent randomness and subjectivity, resulting in less robust decision outcomes. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for intersection routing in vehicle-to-everything (VANETs) based on fuzzy multi-factor decision-making. This method addresses the key challenges of intersection routing decision-making in urban VANETs by proposing a systematic solution: at each intersection, a two-stage intelligent decision-making process is executed by a Roadside Unit (RSU): First, three evaluation factors for candidate road segments at the current intersection—number of lanes, direction angle, and traffic flow—are acquired and processed as fuzzy linguistic variables. The triangular fuzzy number score for each candidate road segment on each evaluation factor is calculated to handle the uncertainty of real-world information. Second, each triangular fuzzy number score is defuzzified to obtain a clear value. Each clear value is then standardized to obtain a standard value. Based on the standard value, the information entropy on each evaluation factor is calculated. The difference coefficient is obtained based on the information entropy and normalized to obtain the dynamic weight value on each evaluation factor, thereby ensuring that the decision focuses on the most discriminative evaluation factor. Finally, based on the triangular fuzzy number score of each candidate road segment on each evaluation factor and the dynamic weight value of each evaluation factor, the weighted total fuzzy number score of each candidate road segment is calculated. The total fuzzy number score is then defuzzified and converted into a clear best unfuzzy performance value (BNP). The candidate road segment with the highest BNP is selected as the target road segment. This invention effectively overcomes the problems of poor adaptability and weak robustness caused by the single decision factor and static weights in traditional routing protocols, significantly improving the packet delivery rate and reducing end-to-end latency.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows: A vehicle-to-everything (V2X) intersection routing method based on fuzzy multi-factor decision-making, the system includes the following steps: S1: Obtain the three evaluation factors of the current intersection candidate road segments: number of lanes, direction angle and traffic flow, process them as fuzzy linguistic variables, and calculate the triangular fuzzy number score of each candidate road segment on each evaluation factor.

[0008] S2: Defuzzify the score of each triangular fuzzy number to obtain a clear value. Standardize each clear value to obtain a standard value. Calculate the information entropy of each evaluation factor based on the standard value. Obtain the difference coefficient based on the information entropy. After normalization, obtain the dynamic weight value of each evaluation factor.

[0009] S3: Calculate the weighted total fuzzy number score of each candidate road segment based on the triangular fuzzy number score of each evaluation factor and the dynamic weight value of each evaluation factor. Convert the total fuzzy number score into a clear best unfuzzy performance value through defuzzification. Select the candidate road segment with the highest best unfuzzy performance value as the target road segment.

[0010] In S2, each sharpness value is standardized to obtain a standard value. This can be achieved through the following formula: in This represents the clarity value of the i-th candidate road segment after deblurring on the j-th evaluation factor. The value of i ranges from 1 to n, where n is a natural number, and the value of j ranges from 1 to 3.

[0011] In S2, the information entropy of each evaluation factor is calculated based on the standard value. This can be achieved through the following formula: in Representing the The candidate road segment is in the first The standardized probability values ​​under each evaluation factor are: i represents the i-th candidate road segment, j represents the j-th evaluation factor, i ranges from 1 to n (where n is a natural number), and j ranges from 1 to 3. This represents the standard value of the i-th candidate road segment on the j-th evaluation factor.

[0012] The difference coefficient g in S2 is obtained based on information entropy. j After normalization, the dynamic weight value w of each evaluation factor is obtained. j This can be achieved through the following formula: in Let represent the information entropy of the j-th evaluation factor, where j represents the j-th evaluation factor and the value of j ranges from 1 to 3.

[0013] The weighted total fuzzy number score for each candidate road segment in S3 This can be achieved through the following formula: Where x̃ ij The triangular fuzzy number score represents the i-th candidate road segment on the j-th evaluation factor. represents the dynamic weight value of the j-th evaluation factor, where i ranges from 1 to n, n is a natural number, and j ranges from 1 to 3.

[0014] In S3, the total fuzzy score is defuzzified and converted into a clear, best unfuzzy performance value (BNP). The candidate road segment with the highest BNP is selected as the target road segment, which is achieved through the following formula: Where i represents the i-th candidate road segment, and j represents the j-th evaluation factor. r represents the weighted total fuzzy number score of the i-th candidate road segment. iThe value represents the standard value of the i-th candidate road segment, where i ranges from 1 to n, n is a natural number, and j ranges from 1 to 3.

[0015] A vehicle-to-everything (V2X) intersection routing system based on fuzzy multi-factor decision-making is characterized by including the following functional modules: Fuzzy processing module: Obtains three evaluation factors for the current intersection candidate road segments: number of lanes, direction angle, and traffic flow, processes them as fuzzy linguistic variables, and calculates the triangular fuzzy number score for each candidate road segment on each evaluation factor.

[0016] Dynamic weight acquisition module: The score of each triangular fuzzy number is defuzzified into a clear value, and each clear value is standardized to obtain a standard value. The information entropy of each evaluation factor is calculated based on the standard value, and the difference coefficient is obtained based on the information entropy. After normalization, the dynamic weight value of each candidate road segment on each evaluation factor is obtained.

[0017] Decision and Execution Module: Based on the triangular fuzzy number score of each candidate road segment on each evaluation factor and the dynamic weight value on each evaluation factor, the total fuzzy number score of each candidate road segment is calculated. The total fuzzy number score is defuzzified and converted into a clear best unfuzzy performance value. The candidate road segment with the highest best unfuzzy performance value is selected as the target road segment.

[0018] Standard value in the dynamic weight acquisition module The calculation is performed using the following formula: in This represents the clarity value of the i-th candidate road segment after deblurring on the j-th evaluation factor. The value of i ranges from 1 to n, where n is a natural number, and the value of j ranges from 1 to 3.

[0019] Information entropy of each evaluation factor in the dynamic weight acquisition module The calculation is performed using the following formula: in This represents the standardized probability value of the i-th candidate road segment under the j-th evaluation factor, where i represents the i-th candidate road segment, j represents the j-th evaluation factor, i ranges from 1 to n (where n is a natural number), and j ranges from 1 to 3. This represents the standard value of the i-th candidate road segment on the j-th evaluation factor.

[0020] The difference coefficient g is obtained based on information entropy in the dynamic weight acquisition module. j After normalization, the dynamic weight value of each evaluation factor is obtained. This can be achieved through the following formula: in Let represent the information entropy of the j-th evaluation factor, where j represents the j-th evaluation factor and the value of j ranges from 1 to 3.

[0021] Compared with the prior art, the technical solution provided by this invention has the following advantages: (1) High decision accuracy: Through the dynamic weighting mechanism, it can automatically focus on the evaluation factors that are most distinctive in the current scenario, so that the decision results are more in line with actual needs.

[0022] (2) Strong adaptability: The dynamic weights are dynamically adjusted according to the real-time data of the candidate set, without the need for manual intervention, and can perfectly adapt to the dynamic changes in urban traffic.

[0023] (3) Good robustness: Fuzzy logic effectively handles the uncertainty and subjectivity in traffic information.

[0024] (4) Highly targeted: The solution is designed specifically for the key bottleneck of urban vehicle-to-everything (V2X) intersections, which significantly improves the overall routing performance. Detailed Implementation

[0025] To further understand the content of this invention, the invention will be described in detail with reference to the embodiments. Example 1 This invention relates to a vehicle-to-everything (V2X) intersection routing method based on fuzzy multi-factor decision-making. At each intersection, a multi-factor decision-making model based on triangular fuzzy number (TFN) is introduced by a roadside unit (RSU) to comprehensively evaluate multiple evaluation factors in order to select the optimal target road segment. The specific scheme is as follows: Network architecture All vehicles and RSUs are equipped with GPS and IEEE 802.11p communication modules; Roadside units (RSUs) are deployed at intersections and are responsible for collecting traffic information and making routing decisions. Includes the following steps: S1: Obtain the three evaluation factors of the current intersection candidate road segments: number of lanes, direction angle and traffic flow, process them as fuzzy linguistic variables, and calculate the triangular fuzzy number score of each candidate road segment on each evaluation factor.

[0026] When making decisions, the Roadside Unit (RSU) evaluates the following three core evaluation factors for each candidate road segment (leading to the adjacent intersection): Number of Lanes: Reflects the physical traffic capacity of a road segment.

[0027] Direction: The angle between the direction of the candidate road segment and the ideal straight line from the current intersection to the final destination.

[0028] Traffic Flow: Real-time vehicle density on a road segment, used to determine congestion levels.

[0029] Traffic flow calculation: The RSU dynamically estimates the real-time traffic flow status (smooth, normal, congested, sparse) of each adjacent road segment based on the number of vehicles passing through its coverage area per unit time and the length of the road segment.

[0030] Fuzzification and Quantization Rules: To handle the uncertainties of the real world, this invention uses the nine-point triangular fuzzy number (TFN) scale to quantify the above evaluation factors into fuzzy linguistic variables. A TFN is represented as à = (l, m, u), where l and u are the lower and upper bounds, respectively, and m is the mode value. Let the set of candidate road segments at the current intersection be C = {C i | i = 1, 2, ..., n}, the set of evaluation factors is F = {F j | j = 1, 2, 3}, corresponding to the number of lanes, direction, and traffic flow, respectively.

[0031] Fuzzy quantization rules for the number of lanes (Num): Based on the lane configuration of the road segment, it is fuzzily quantized into TFN: Fuzzy quantization rules for direction (Dir): Calculate the counterclockwise angle α between the candidate road segment and the ideal direction line (from the current intersection S to the destination D). Based on the magnitude of α, fuzzify it into a TFN: Fuzzy quantization rules for traffic flow (Tra): Based on the traffic conditions of the road segment (based on vehicle density), it is fuzzily quantized into TFN: S2: Defuzzify the score of each triangular fuzzy number to obtain a clear value. Standardize each clear value to obtain a standard value. Calculate the information entropy of each evaluation factor based on the standard value. Obtain the difference coefficient based on the information entropy. After normalization, obtain the dynamic weight value of each evaluation factor.

[0032] The dynamic weights of each evaluation factor are not preset, but are dynamically calculated based on the amount of information provided by each evaluation factor in the candidate road segment set. The greater the amount of information (i.e., the more effectively candidate schemes can be distinguished), the higher its dynamic weight value.

[0033] Data standardization: First, calculate the triangular fuzzy number score x̃ of the i-th candidate road segment on the j-th evaluation factor. ij Deblurring to sharp value x using the Center of Area (COA) method ij Then, all clear values ​​are standardized using a very large index: The triangular fuzzy number (TFN) score x̃ of the i-th candidate road segment on the j-th evaluation factor. ij The calculation process is as follows: 1. Get the original attribute value RSU obtains each candidate road segment C from the map or real-time perception. i The three original attribute values: Num: Number of lanes (integer, such as 2, 4) The angle between the current direction of the road segment and the ideal direction (current intersection → destination). (Angle, such as 30°) Tra: Traffic flow status (estimated by traffic volume per unit time, such as "congested") 2. Apply fuzzy quantization rules: 3. Fuzzy quantize the original value to x̃ ij For the number of lanes (Num): the higher the value, the better → mapped to "high / very high" level.

[0034] For the direction (Dir): the smaller the included angle α, the better (the closer to 0°, the better) → small α → "extremely high", large α (e.g., >90°) → "extremely low".

[0035] For traffic flow (Tra): lower density is better (to avoid congestion) → “sparse” → “extremely high”, “crowded” → “extremely low”.

[0036] Ultimately, each (i, j) corresponds to a TFN: x̃ ij = (l ij , m ij , u ij ) Example: Suppose there are 3 candidate road segments at an intersection: C1, C2, and C3. The destination is due east.

[0037] 1. Obtain the raw data: 2. Apply fuzzy quantization rules: According to "C. Fuzzification Processing and Quantization Rules" Number of lanes: C1: 2 lanes → "Low" → (1,2,3) C2: 4 lanes → "Slightly lower" → (3,4,5) C3: 3 lanes → "Slightly lower" → (3,4,5) Directional angle α (the smaller the better): C1: 15° → “Very high” → (7,8,9) C2: 50° → “Slightly higher” → (5,6,7) C3: 120° → “Slightly lower” → (3,4,5) Traffic flow (the sparser the better): C1: Crowded → "Slightly Low" → (3,4,5) C2: Normal → "Slightly High" → (5,6,7) C3: Sparse → "Low" → (1,2,3) Get x̃ ij Matrix (TFN form): Deblurring to sharp value x ij : According to formula (4), all clear values ​​are standardized using the maximal index: Calculate information entropy E j For the j-th evaluation factor, calculate its information entropy: in This represents the standardized probability value of the i-th candidate road segment under the j-th evaluation factor, which is obtained by standardizing the candidate road segment. The result is obtained by dividing by the sum of the standard values ​​of all candidate road segments under this evaluation factor.

[0038] According to formula (5), the standardized probability values ​​of each factor for each road segment are calculated: According to formula (6), the information entropy of each factor is calculated: Calculate the coefficient of variation and dynamic weight for each evaluation factor: Calculate the coefficient of variation g j = 1 - E j The dynamic weight w is obtained by normalization. j : Coefficient of difference g i : According to formula (7), the weights w of each factor are calculated. j : S3: Calculate the weighted total fuzzy score of each candidate road segment based on the triangular fuzzy number score of each evaluation factor and the dynamic weight value of each evaluation factor. Convert the total fuzzy number score into a clear best unfuzzy performance value through defuzzification. Select the candidate road segment with the highest best unfuzzy performance value as the target road segment.

[0039] Calculate the comprehensive fuzzy performance score: For each candidate road segment i, calculate its weighted total fuzzy score. : According to formula (8), the total fuzzy value of each road segment is calculated as follows: Optimal route selection: RSU selects the candidate route with the highest fuzzy value as the forwarding route for the data packet. Based on the comparison of the total fuzzy values ​​of each road segment, it can be seen that the total fuzzy value of road segment C2 is the largest. Therefore, RSU selects road segment C2 as the data forwarding road segment.

[0040] Defuzzification: Using the Center of Area (COA) method to determine the fuzzy score Convert to a clear, best unambiguous performance (BNP) value. Ultimately, the candidate road segment with the highest BNP value was selected as the target road segment.

[0041] According to formula (10), the BNP values ​​for each road segment can be obtained as follows: Since road segment C2 has the highest BNP value, road segment C2 is selected as the target road segment.

[0042] Example 2 This invention relates to a vehicle-to-everything (V2X) intersection routing system based on fuzzy multi-factor decision-making, characterized by comprising the following functional modules: Fuzzy processing module: Obtains three evaluation factors for the current intersection candidate road segments: number of lanes, direction angle, and traffic flow, processes them as fuzzy linguistic variables, and calculates the triangular fuzzy number score for each candidate road segment on each evaluation factor.

[0043] Dynamic weight acquisition module: The score of each triangular fuzzy number is defuzzified into a clear value, and each clear value is standardized to obtain a standard value. The information entropy of each evaluation factor is calculated based on the standard value, and the difference coefficient is obtained based on the information entropy. After normalization, the dynamic weight value of each candidate road segment on each evaluation factor is obtained.

[0044] Decision and Execution Module: Based on the triangular fuzzy number score of each candidate road segment on each evaluation factor and the dynamic weight value on each evaluation factor, the total fuzzy number score of each candidate road segment is calculated. The total fuzzy number score is defuzzified and converted into a clear best unfuzzy performance value. The candidate road segment with the highest best unfuzzy performance value is selected as the target road segment.

Claims

1. A vehicle-to-everything (V2X) intersection routing method based on fuzzy multi-factor decision-making, characterized in that: Includes the following steps: S1: Obtain the three evaluation factors of the current intersection candidate road segments: number of lanes, direction angle and traffic flow, process them as fuzzy linguistic variables, and calculate the triangular fuzzy number score of each candidate road segment on each evaluation factor; S2: Defuzzify the score of each triangular fuzzy number to obtain a clear value, standardize each clear value to obtain a standard value, calculate the information entropy of each evaluation factor based on the standard value, obtain the difference coefficient based on the information entropy, and obtain the dynamic weight value of each evaluation factor after normalization. S3: Calculate the weighted total fuzzy number score of each candidate road segment based on the triangular fuzzy number score of each evaluation factor and the dynamic weight value of each evaluation factor. Convert the total fuzzy number score into a clear best unfuzzy performance value through defuzzification. Select the candidate road segment with the highest best unfuzzy performance value as the target road segment.

2. The vehicle-to-everything (V2X) intersection routing method according to claim 1, characterized in that: In S2, each sharpness value is standardized to obtain a standard value. This can be achieved through the following formula: in This represents the clarity value of the i-th candidate road segment after deblurring on the j-th evaluation factor. The value of i ranges from 1 to n, where n is a natural number, and the value of j ranges from 1 to 3.

3. The vehicle-to-everything (V2X) intersection routing method according to claim 1, characterized in that: In S2, the information entropy of each evaluation factor is calculated based on the standard value. This can be achieved through the following formula: in This represents the standardized probability value of the i-th candidate road segment under the j-th evaluation factor, where i represents the i-th candidate road segment, j represents the j-th evaluation factor, i ranges from 1 to n (where n is a natural number), and j ranges from 1 to 3. This represents the standard value of the i-th candidate road segment on the j-th evaluation factor.

4. The vehicle-to-everything (V2X) intersection routing method according to claim 1, characterized in that: The difference coefficient g in S2 is obtained based on information entropy. j After normalization, the dynamic weight value w of each evaluation factor is obtained. j This can be achieved through the following formula: in Let represent the information entropy of the j-th evaluation factor, where j represents the j-th evaluation factor and the value of j ranges from 1 to 3.

5. The vehicle-to-everything (V2X) intersection routing method according to claim 1, characterized in that: The weighted total fuzzy number score for each candidate road segment in S3 This can be achieved through the following formula: Where x̃ ij The triangular fuzzy number score represents the i-th candidate road segment on the j-th evaluation factor. represents the dynamic weight value of the j-th evaluation factor, where i ranges from 1 to n, n is a natural number, and j ranges from 1 to 3.

6. The vehicle-to-everything (V2X) intersection routing method according to claim 1, characterized in that: In S3, the total fuzzy score is defuzzified and converted into a clear, best unfuzzy performance value (BNP). The candidate road segment with the highest BNP is selected as the target road segment, which is achieved through the following formula: Where i represents the i-th candidate road segment, and j represents the j-th evaluation factor. r represents the weighted total fuzzy number score of the i-th candidate road segment. i The value represents the standard value of the i-th candidate road segment, where i ranges from 1 to n, n is a natural number, and j ranges from 1 to 3.

7. A vehicle-to-everything (V2X) intersection routing system based on fuzzy multi-factor decision-making, characterized in that: Includes the following functional modules: Fuzzy processing module: Obtains three evaluation factors for the current intersection candidate road segments: number of lanes, direction angle, and traffic flow, processes them into fuzzy linguistic variables, and calculates the triangular fuzzy number score for each candidate road segment on each evaluation factor; Dynamic weight acquisition module: The score of each triangular fuzzy number is defuzzified into a clear value, each clear value is standardized to obtain a standard value, the information entropy of each evaluation factor is calculated based on the standard value, the difference coefficient is obtained based on the information entropy, and after normalization, the dynamic weight value of each candidate road segment on each evaluation factor is obtained. Decision and Execution Module: Based on the triangular fuzzy number score of each candidate road segment on each evaluation factor and the dynamic weight value on each evaluation factor, the total fuzzy number score of each candidate road segment is calculated. The total fuzzy number score is defuzzified and converted into a clear best unfuzzy performance value. The candidate road segment with the highest best unfuzzy performance value is selected as the target road segment.

8. The vehicle-to-everything (V2X) intersection routing system according to claim 7, characterized in that: Standard value in the dynamic weight acquisition module The calculation is performed using the following formula: in This represents the clarity value of the i-th candidate road segment after deblurring on the j-th evaluation factor. The value of i ranges from 1 to n, where n is a natural number, and the value of j ranges from 1 to 3.

9. The vehicle-to-everything (V2X) intersection routing system according to claim 7, characterized in that: Information entropy of each evaluation factor in the dynamic weight acquisition module The calculation is performed using the following formula: in Representing the The candidate road segment is in the The standardized probability values ​​under each evaluation factor are: i represents the i-th candidate road segment, j represents the j-th evaluation factor, i ranges from 1 to n (where n is a natural number), and j ranges from 1 to 3. This represents the standard value of the i-th candidate road segment on the j-th evaluation factor.

10. The vehicle-to-everything (V2X) intersection routing system according to claim 7, characterized in that: The difference coefficient g is obtained based on information entropy in the dynamic weight acquisition module. j After normalization, the dynamic weight value of each evaluation factor is obtained. This can be achieved through the following formula: in Let represent the information entropy of the j-th evaluation factor, where j represents the j-th evaluation factor and the value of j ranges from 1 to 3.

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