A method and system for evaluating the multi-performance of intelligent connected vehicle platooning

Abstract

This invention discloses a method and system for evaluating the multi-performance of connected vehicle (CAV) platooning. The method includes: constructing CAV platooning control models based on different car-following strategies to adapt to different traffic conditions and platooning performance requirements; establishing a multi-criteria decision-making evaluation system by setting different weights for different criteria to meet the diverse performance requirements of CAV platooning; applying fuzzy log-least squares (FLLS) to determine the weights of each performance criterion for different decision-makers, enabling the system to fully reflect the priority of different decision-makers in comparing different criteria; and constructing a GRA-TOPSIS method by combining grey relational analysis (GRA) and ideal solution similarity ranking method (TOPSIS) to calculate a comprehensive evaluation index for evaluating the multi-performance of CAV platooning, thereby ranking the evaluation results under different strategies. This invention can provide decision support for decision-makers to select the optimal CAV platooning strategy in complex traffic environments.

Description

Technical Field

[0001] This invention relates to the fields of intelligent transportation technology and autonomous driving technology, and in particular to a method and system for evaluating the multi-performance of intelligent connected vehicle platooning. Background Technology

[0002] With the rapid development of intelligent transportation and autonomous driving technologies, connected vehicles (CAVs) have gradually become an important component of intelligent transportation systems. CAVs offer significant advantages in reducing road congestion, improving traffic efficiency, and lowering energy consumption and emissions through information sharing and automatic control technologies among vehicles. Currently, CAV platooning technology (i.e., "platooning") is gaining attention. This technology uses vehicle spacing control strategies to enable multiple CAVs to travel in platoons, aiming to achieve more efficient road use and resource consumption management.

[0003] In CAV (Carrier-to-Vehicle) queuing, vehicle spacing control strategy is the core component. Common control strategies include Constant Time Interval (CTG) and Constant Spacing (CS). The CTG strategy sets a time interval between vehicles and calculates the real-time spacing by multiplying the vehicle speeds, making it suitable for scenarios requiring high energy efficiency and safety. The CS strategy maintains a fixed distance between vehicles, effectively improving traffic efficiency in low-flow scenarios. In recent years, hybrid control strategies (CC) combining CTG and CS have been proposed to leverage the advantages of each strategy. However, different control strategies exhibit significant differences in emissions, energy consumption, traffic flow, and safety, and currently, there is a lack of systematic evaluation methods for each strategy under multiple criteria. Summary of the Invention

[0004] Purpose of the invention: To address the problems existing in the prior art, the purpose of this invention is to provide a multi-performance evaluation method and system for intelligent connected vehicle platooning. This method combines fuzzy log least squares (FLLS), grey relational analysis (GRA), and TOPSIS to evaluate the multi-criteria performance of CAV platoons under different spacing control strategies, and determines the optimal platooning strategy by integrating the decision-maker's preferences.

[0005] Technical Solution: To achieve the above-mentioned objectives, this invention provides a method for evaluating the multi-performance characteristics of connected vehicle platooning, comprising the following steps:

[0006] (1) Based on different car-following strategies, establish a CAV queue control model adapted to different traffic conditions; the different car-following strategies include one or more of the following strategies: fixed time interval (CTG) strategy, fixed spacing (CS) strategy, and a combination strategy of CTG and CS (CTG-CS);

[0007] (2) Establish a multi-criteria evaluation system, and set different weights for different criteria to meet the diverse needs of decision-makers for platooning performance; the multi-criteria evaluation system includes one or more performance standards among emissions, energy consumption, efficiency and safety.

[0008] (3) Based on the decision-makers' language variable data, the fuzzy log least squares method (FLLS) is applied to accurately calculate the weights of each criterion. The resulting weights can reflect the priorities of different decision-makers in comparing different criteria.

[0009] (4) The Grey Relational Analysis (GRA) is embedded into the Ideal Solution Similarity Ranking Method (TOPSIS) to construct the GRA-TOPSIS method. Based on simulation data or measured data, a comprehensive evaluation index for evaluating the multi-performance of CAV formation driving is calculated, thereby ranking the evaluation results of different strategies.

[0010] In some embodiments, the performance of CAV platooning is evaluated and verified using data generated by a simulation system. First, vehicle position and speed data are generated to simulate the driving trajectory of the CAV platoon. Then, using a set car-following control model and control parameters, one or more performance parameters of the vehicles, including emissions, energy consumption, efficiency, and safety, are calculated. The data is then organized into a decision matrix. Finally, FLLS and GRA-TOPSIS methods are used for data analysis to calculate normalized weight values ​​and obtain the performance of the CAV platoon under different strategies.

[0011] In some embodiments, the fixed time interval (CTG) strategy dynamically adjusts the following behavior of vehicles by setting a fixed time interval and calculating the desired distance between vehicles based on the product of the time interval and the speed of the following vehicle; the fixed distance (CS) strategy ensures a stable desired distance between vehicles by maintaining a fixed distance between them; under the hybrid strategy, vehicles in the CAV queue are randomly distributed based on the CTG and CS strategies to form a hybrid queue mode, and a perception system and a V2V communication system are used to obtain the speed, acceleration and position information of the vehicle in front to achieve real-time control.

[0012] In some embodiments, the hybrid platoon improves overall platooning performance by optimizing a combination of emissions, energy consumption, efficiency, and safety metrics, based on fuzzy grey relational analysis and ideal solution similarity ranking (fuzzy GRA-TOPSIS) to determine an optimized vehicle distribution pattern and a comprehensive spacing strategy.

[0013] In some embodiments, the design incorporates four evaluation indicators in a multi-criteria evaluation system that includes emissions, energy consumption, efficiency, and safety, corresponding to carbon dioxide emissions, fuel consumption, traffic throughput, and collision time, respectively.

[0014] Furthermore, the FLLS method is applied to accurately calculate the weights of each criterion, specifically including the following steps:

[0015] (3.1) Represent the fuzzy comparison matrix using linguistic variables;

[0016] (3.2) Convert the linguistic variables of the comparison matrix into fuzzy numerical values ​​for calculation to obtain the comparison matrix of the k-th decision-maker:

[0017]

[0018] Where n represents the number of criteria. Let represent the three element values ​​of criterion i compared to criterion j for the k-th decision-maker, where u represents upper, m represents middle, and l represents lower;

[0019] (3.3) Based on the weighted geometric mean method, the fuzzy comparison matrices of the K decision-makers are summarized, and the average comparison matrix is ​​calculated as follows:

[0020]

[0021] (3.4) Construct an FLLS method to determine the weights of each criterion:

[0022]

[0023] in, It is the weight of the i-th criterion. and It is ω i The three elements, z ij To determine the importance of criterion i compared to criterion j, Each corresponds to a value of one of the three elements.

[0024] In some embodiments, K = 1 or K > 1. When K > 1, the K decision-makers are of the same type.

[0025] Furthermore, the performance of different CAV queues is ranked based on the GRA-TOPSIS method, specifically including the following steps:

[0026] (4.1) Numerical experiments were conducted on CAV queues under different car-following strategies;

[0027] (4.2) Determine the decision matrix based on the simulation results;

[0028] (4.3) Normalize the values ​​of each criterion in the decision matrix of different CAV queues, i.e., r ij :

[0029]

[0030] Where m represents the number of CAV queues, y ij This represents the calculated value of the j-th criterion for the i-th CAV queue;

[0031] (4.4) Based on the criterion weights and the obtained normalization results, calculate the weighted normalized decision matrix. For ease of calculation, the weights of each indicator are converted into clear numerical values. The formula for calculating the weighted normalized matrix is ​​as follows:

[0032]

[0033] x ij =ω j r ij

[0034] Where, x ij Let be the weighted value of the j-th criterion in the i-th CAV queue, and n be the number of criteria;

[0035] (4.5) Calculate PIS, representing the best possible ideal solution, and NIS, representing the worst possible solution, respectively:

[0036]

[0037] (4.6) The grey relational coefficients between each indicator and PIS and NIS can be expressed by the following formula:

[0038]

[0039] (4.7) The grey relational level of each CAV queue to PIS and NIS is calculated as follows:

[0040]

[0041] (4.8) Determine the relative closeness of the grey relational degree of the CAV queues, and compare them based on this value to obtain the sorting order of the CAV queues:

[0042]

[0043] Where, μ i It represents the relative proximity of the gray relational degree of the i-th CAV queue, and its value is located in the interval [0,1].

[0044] A multi-performance evaluation system for intelligent connected vehicle platooning includes:

[0045] The car-following control module is used to establish a CAV queue control model adapted to different traffic conditions based on different car-following strategies; the different car-following strategies include one or more of the following: fixed time interval (CTG) strategy, fixed spacing (CS) strategy, and a combination strategy of CTG and CS (CTG-CS);

[0046] The evaluation index module is used to establish a multi-criteria evaluation system. By setting different weights for different criteria, it can meet the diverse needs of decision-makers for platooning performance. The multi-criteria evaluation system includes one or more performance standards among emissions, energy consumption, efficiency, and safety.

[0047] The weight determination module is used to accurately calculate the weights of each criterion based on the decision-maker's linguistic variable data using the fuzzy log least squares (FLLS) method. The resulting weights can reflect the priorities of different decision-makers in comparing different criteria.

[0048] The module also includes a comprehensive ranking module, which embeds Grey Relational Analysis (GRA) into the Ideal Solution Similarity Ranking Method (TOPSIS) to construct the GRA-TOPSIS method. Based on simulation data or measured data, it calculates a comprehensive evaluation index to assess the multi-performance of CAV formation driving, thereby ranking the evaluation results of different strategies.

[0049] A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps of the aforementioned method for evaluating the multi-performance characteristics of intelligent connected vehicle platooning.

[0050] Beneficial Effects: Compared with existing technologies, this invention develops a hybrid multi-criteria decision-making (MCDM) method, combining FLLS, GRA, and TOPSIS methods to measure CAV platooning performance under different spacing strategies, thereby determining the CAV platooning order. This invention can evaluate CAV platooning performance from the perspectives of two different decision-makers: corporate managers and administrative managers, considering emissions, energy consumption, efficiency, and safety impacts. It provides decision support for decision-makers to select the optimal CAV platooning strategy in complex traffic environments. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating a multi-performance evaluation method for intelligent connected vehicle platooning provided in an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of the CAV queues under study, where (a) CTG-CAVP represents CAV queues using different CTG strategies; (b) CS-CAVP represents CAV queues using the CS strategy; and (c) CC-CAVP represents CAV queues using both CTG and CS strategies. Detailed Implementation

[0053] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0054] This invention provides a multi-performance evaluation method for intelligent connected vehicle platooning, aiming to scientifically and comprehensively evaluate the emissions, energy consumption, efficiency, and safety of CAV platoons under different vehicle spacing control strategies. Through a series of modeling, calculations, and optimizations, this invention can provide decision support for decision-makers to select the optimal platooning strategy in complex traffic environments.

[0055] like Figure 1 As shown in the figure, an embodiment of the present invention discloses a multi-performance evaluation method for intelligent connected vehicle platooning, which mainly includes:

[0056] (1) Based on different car-following strategies, establish CAV queue control models adapted to different traffic conditions to ensure the accuracy of queue driving performance evaluation; the specific different car-following strategies include one or more of the following strategies: Constant Time Gap (CTG) strategy, Constant Spacing (CS) strategy, and a combination strategy of CTG and CS (CTG-CS).

[0057] (2) Establish a multi-criteria decision-making system and design a multi-criteria evaluation system that includes one or more performance standards such as emissions, energy consumption, efficiency and safety. By setting different weights for different criteria, the system can meet the diverse needs of decision-makers (such as corporate decision-makers and administrative managers) for platooning performance.

[0058] (3) Based on the decision-makers' language variable data, the fuzzy log least squares method (FLLS) is applied to accurately calculate the weights of each criterion. The resulting weights can reflect the priorities of different decision-makers in comparing different criteria.

[0059] (4) The Grey Relational Analysis (GRA) is embedded into the Ideal Solution Similarity Ranking Method (TOPSIS) to construct the GRA-TOPSIS method. Based on simulation data or measured data, a comprehensive evaluation index for evaluating the multi-performance of CAV formation driving is calculated, thereby ranking the evaluation results of different strategies.

[0060] This invention optimizes CAV platooning performance by setting different vehicle spacing control strategies to control the following behavior of each CAV. This embodiment introduces CTG, CS, and hybrid CTG-CS (CC) control strategies, enabling CAVs to flexibly control their platooning according to different road conditions, traffic flow, and environmental requirements. The CTG strategy is suitable for safety control under high traffic flow conditions, maintaining a fixed time interval between vehicles to dynamically adjust the distance, enhancing platoon stability and energy efficiency. The CS strategy, based on a fixed spacing, is more suitable for scenarios with lower traffic flow, helping to improve the efficiency and stability of CAV platooning. The hybrid strategy combines the advantages of both CTG and CS strategies, dynamically switching between them under different traffic conditions to achieve a balance between safety and efficiency in CAV platooning. The steps are described in detail below.

[0061] (1) Establishment of CAV queue car-following control model

[0062] The CAV queue car-following control model is based on different interval strategies, including one or more of the following: fixed time interval (CTG) strategy, fixed spacing (CS) strategy, and a combined CTG and CS strategy (CTG-CS). Specifically, the CAV queue car-following control model is as follows:

[0063]

[0064]

[0065] In the formula, and This represents the target distance between the preceding vehicle i-1 and the target vehicle i under CTG and CS strategies; h is a fixed time interval, and d is the distance between the preceding vehicle i-1 and the target vehicle i. s For a safe distance; p i (t),v i (t) and a i (t) represents the position, velocity, and actual acceleration of vehicle i in the CAV queue at time t, respectively; g i Indicates time delay, satisfying δ i This represents the perception delay of vehicle i. This represents the upper bound of the communication delay between vehicle i and the preceding vehicle i-1; and This represents the spacing error between two consecutive CAVs employing CTG and CS strategies; based on previous research (Li et al., 2022; Zheng et al., 2023a), Let Bian be the expected acceleration of vehicle i at time t based on the CTG strategy. Let k be the expected acceleration of vehicle i at time t using the Ploeg method based on the CTG strategy; s,1 k v,1 k a,1 k s,2 k v,2 k a,2 k represents the control parameters of the car-following control model employing the CTG spacing strategy. s,1 k v,1 k a,1 Let k represent the parameter in the Bian method (Li et al., 2022). s,2 k v,2 k a,2 σ represents the parameter in the Ploeg method (Zheng et al., 2023a); i This indicates the cumulative time delay between the lead vehicle and the target vehicle, i.e. q1, q3, q4, and λ are control parameters based on the CS control model.

[0066] In this embodiment, a linear third-order model is used as the dynamic model for all CAVs. The following are the car-following control models for three representative CAV queues: CAVs with different CTG strategies, CAVs with CS strategies, and CAVs with both CTG and CS strategies. CTG-CAVP: The expected acceleration of the CAV car-following control model was calculated based on the above formulas. For CTG-CAVP, CAVs with CTG strategies using Bian's and Ploeg's methods can be randomly distributed in queues of the same size. Therefore, this embodiment will study the mixed random distribution of CAV control models based on CTG in the queues. A large number of randomly generated CAV queues can be used for case study analysis. CS-CAVP: The expected acceleration of the CS-based control model in CS-CAVP was calculated based on the above formulas. For CS-CAVP, CAVs with CS strategies can form CAV queues for case analysis. CC-CAVP: The expected acceleration of the CTG-based control model and the CS-based control model in CC-CAVP were calculated based on the above formulas. For CC-CAVP, CAVs with CTG and CS strategies can be randomly distributed in queues of equal size. A large number of randomly generated CAV queues can be used for case study analysis.

[0067] In highway scenarios with unstable traffic flow, the CC strategy can switch to the appropriate control mode based on real-time traffic flow; while in congested urban environments where road utilization needs to be improved, the CC strategy can effectively reduce queue gaps.

[0068] In specific experiments, vehicles within the CAV queue can be randomly distributed based on both CTG and CS strategies to form a hybrid queue pattern. A sensing system and a V2V communication system are used to acquire the speed, acceleration, and position information of the vehicle in front for real-time control. The hybrid queue can improve overall queue performance by optimizing comprehensive indicators of emissions, energy consumption, efficiency, and safety performance. Based on fuzzy grey relational analysis and the ideal solution similarity ranking method (fuzzy GRA-TOPSIS), an optimized vehicle distribution pattern and comprehensive spacing strategy can be determined.

[0069] (2) Multi-criteria decision evaluation system

[0070] The design incorporates a multi-criteria evaluation system encompassing emissions, energy consumption, efficiency, and safety, with varying weightings to meet the diverse performance needs of decision-makers. The standard system for measuring CAV platooning performance consists of four performance criteria: emissions, energy consumption, efficiency, and safety. These four evaluation indicators consider four standards: CO2 emissions, fuel consumption, traffic throughput, and collision time.

[0071] (3) Fuzzy Log-Least Squares (FLLS) method is used to calculate the criterion weights. During the decision-making process, the weight of each criterion is set according to the preferences of different decision-makers. Decision-makers express the relative importance of different criteria through linguistic variables, including "extremely important," "very important," and "moderately important." This embodiment uses fuzzy log-least squares (FLLS) to determine the criterion weights to reflect the relative importance of emissions, energy consumption, efficiency, and safety in the evaluation system. The steps include:

[0072] (3.1) Represent the fuzzy comparison matrix using linguistic variables;

[0073] (3.2) Convert the linguistic variables of the comparison matrix into fuzzy numerical values ​​for calculation to obtain the comparison matrix of the k-th decision-maker:

[0074]

[0075] Where n represents the number of criteria. Let represent the three element values ​​of criterion i compared to criterion j for the k-th decision-maker, where u represents upper, m represents middle, and l represents lower;

[0076] (3.3) Based on the weighted geometric mean method, the fuzzy comparison matrices of K (K≥1) decision-makers are summarized, and the average comparison matrix is ​​calculated:

[0077]

[0078] (3.4) Construct an FLLS method to determine the weights of each criterion:

[0079]

[0080] in, It is the weight of the i-th criterion. and It is ω i The three elements, z ij To determine the importance of criterion i compared to criterion j, Each corresponds to a value of one of the three elements.

[0081] (4) After determining the weights, this invention uses Grey Relational Analysis-Ideal Solution Ranking Method (GRA-TOPSIS) to rank the CAV queue strategy based on multiple criteria. The specific steps are as follows:

[0082] (4.1) Numerical experiments were conducted on CAV queues under different car-following strategies;

[0083] (4.2) Determine the decision matrix based on the simulation results;

[0084] (4.3) Normalize the values ​​of each criterion in the decision matrix of different CAV queues, i.e., r ij :

[0085]

[0086] Where m represents the number of CAV queues, y ij This represents the calculated value of the j-th criterion for the i-th CAV queue;

[0087] (4.4) Based on the criterion weights and the obtained normalization results, calculate the weighted normalized decision matrix. For ease of calculation, the weights of each indicator are converted into clear numerical values. In this embodiment... The formula for calculating the weighted normalized matrix is ​​as follows:

[0088]

[0089] x ij =ω j r ij

[0090] Where, x ij Let be the weighted value of the j-th criterion in the i-th CAV queue, and n be the number of criteria;

[0091] (4.5) Calculate PIS, representing the best possible ideal solution, and NIS, representing the worst possible solution, respectively:

[0092]

[0093] (4.6) The grey relational coefficients between each indicator and PIS and NIS can be expressed by the following formula:

[0094]

[0095] (4.7) The grey relational level of each CAV queue to PIS and NIS is calculated as follows:

[0096]

[0097] (4.8) Determine the relative closeness of the grey relational degree of the CAV queues, and compare them based on this value to obtain the sorting order of the CAV queues:

[0098]

[0099] Where, μ i It represents the relative proximity of the gray relational degree of the i-th CAV queue, and its value is located in the interval [0,1].

[0100] (5) Simulation experiment verification: In order to verify the effectiveness of the system, this embodiment of the invention designed a series of simulation experiments using real vehicle trajectories in the NGSIM dataset.

[0101] Using NGSIM vehicle data with ID 1941 as an example, the simulation of oscillations and dynamic changes caused by an exogenous vehicle in a queue was conducted. The simulation was divided into two phases: the first phase involved significant deceleration and acceleration fluctuations; the second phase involved uniform speed travel with the speed stabilizing. Key experimental parameters included simulation time T = 80 seconds, time step Δt = 0.01 seconds, fixed time interval h = 1 second, stopping distance d_s = 10 meters or 20 meters, sensor delay δ = 0.02 seconds, and communication delay θ between 0.02 and 0.1 seconds. Regarding data acquisition and decision matrix construction, under different strategies, vehicle CO2 emissions, fuel consumption, traffic flow, and collision time data were collected to construct a decision matrix for analysis.

[0102] This simulation experiment verified the differences in environment, energy consumption, efficiency, and security of different CAV queue strategies, providing reliable data support for the practical application of the system.

[0103] (6) Application Cases and Decision-Maker Analysis: The system is applied to the following two typical types of decision-makers:

[0104] (6.1) Enterprise managers (DM1): Focus on reducing energy consumption and improving queue efficiency. The FLLS model sets a higher weight for energy consumption, making the CS strategy the optimal choice due to its higher energy efficiency.

[0105] (6.2) Administrators (DM2): They are more concerned about vehicle safety and environmental benefits. In this context, the CTG strategy is more suitable as the preferred strategy for administrators due to its advantages in safety and emissions.

[0106] Based on the same inventive concept, this invention discloses a multi-performance evaluation system for intelligent connected vehicle platooning, comprising: a car-following control module, used to establish a CAV platooning control model adapted to different traffic conditions based on different car-following strategies; the different car-following strategies include one or more of a fixed time interval (CTG) strategy, a fixed spacing (CS) strategy, and a combined strategy of CTG and CS (CTG-CS); and an evaluation index module, used to establish a multi-criteria evaluation system, by setting different weights for different criteria to meet the diverse needs of decision-makers for platooning performance; the multi-criteria evaluation system includes emissions, The system includes one or more performance criteria such as energy consumption, efficiency, and safety; a weight determination module, which uses fuzzy log-least-squares (FLLS) to accurately calculate the weights of each criterion based on the decision-maker's linguistic variable data, so that the weights can reflect the priorities of different decision-makers in comparing different criteria; and a comprehensive ranking module, which embeds grey relational analysis (GRA) into the ideal solution similarity ranking method (TOPSIS) to construct the GRA-TOPSIS method, calculates a comprehensive evaluation index for evaluating the multi-performance of CAV platooning based on simulation data or measured data, and ranks the evaluation results of different strategies.

[0107] The system provided in this embodiment of the invention can be used to execute the method provided in this embodiment of the invention, and has the corresponding functions and beneficial effects of executing the method. It is worth noting that the method in the above system embodiments can serve as a decision-making tool to determine the optimal queue from candidate CAV queues for two different types of decision-makers.

[0108] This invention also discloses a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the aforementioned method for evaluating the multi-performance of intelligent connected vehicle platooning.

[0109] It should be understood that the embodiments and descriptions above are only the principles, main features and advantages of the present invention. Various changes and modifications can be made to the present invention without departing from the spirit and scope of the invention, and all such changes and modifications fall within the protection scope of the present invention.

Claims

1. A method for evaluating the multi-performance characteristics of intelligent connected vehicle platooning, characterized in that, Includes the following steps: (1) Based on different car-following strategies, establish a CAV queue control model adapted to different traffic conditions; the different car-following strategies include one or more of the fixed time interval CTG strategy, fixed distance CS strategy and the combined strategy of CTG-CS of CTG and CS; the fixed time interval CTG strategy sets a fixed time interval and calculates the expected distance between vehicles based on the product of the time interval and the speed of the car-following vehicle to dynamically adjust the car-following behavior of the vehicle. The fixed-distance CS strategy ensures a stable expected distance between vehicles by maintaining a fixed distance between them. Under the hybrid strategy, vehicles in the CAV queue are randomly distributed based on CTG and CS strategies to form a hybrid queue mode. The perception system and V2V communication system are used to obtain the speed, acceleration and position information of the vehicle in front to achieve real-time control. (2) Establish a multi-criteria evaluation system and set different weights for different criteria to meet the diverse needs of decision-makers for platooning performance; The multi-criteria evaluation system includes one or more performance standards among emissions, energy consumption, efficiency, and safety. (3) Based on the decision-makers' language variable data, the fuzzy log least squares method (FLLS) is applied to accurately calculate the weights of each criterion. The resulting weights can reflect the priorities of different decision-makers in comparing different criteria. (4) The grey relational analysis (GRA) is embedded into the ideal solution similarity ranking method (TOPSIS) to construct the GRA-TOPSIS method. Based on simulation data or measured data, a comprehensive evaluation index for evaluating the multi-performance of CAV formation driving is calculated, thereby ranking the evaluation results of different strategies. The performance of CAV platooning is evaluated and verified using data generated by a simulation system. First, vehicle position and speed data are generated to simulate the driving trajectory of the CAV platoon. Then, using a set car-following control model and control parameters, one or more of the vehicle's performance characteristics, including emissions, energy consumption, efficiency, and safety, are calculated. The data is then organized into a decision matrix. Finally, FLLS and GRA-TOPSIS methods are used for data analysis to calculate normalized weight values ​​and obtain the performance of the CAV platoon under different strategies.

2. The method for evaluating the multi-performance characteristics of intelligent connected vehicle platooning as described in claim 1, characterized in that, Hybrid platooning improves overall platooning performance by optimizing a combination of emissions, energy consumption, efficiency, and safety metrics. Based on fuzzy grey relational analysis and the ideal solution similarity ranking method fuzzy GRA-TOPSIS, it determines optimized vehicle distribution patterns and overall spacing strategies.

3. The method for evaluating the multi-performance characteristics of intelligent connected vehicle platooning as described in claim 1, characterized in that, The design incorporates a multi-criteria evaluation system encompassing emissions, energy consumption, efficiency, and safety. The four evaluation indicators correspond to carbon dioxide emissions, fuel consumption, traffic throughput, and collision time, respectively.

4. The method for evaluating the multi-performance characteristics of intelligent connected vehicle platooning as described in claim 1, characterized in that, The FLLS method is used to accurately calculate the weights of each criterion, which includes the following steps: (3.1) Represent the fuzzy comparison matrix using linguistic variables; (3.2) Convert the linguistic variables of the comparison matrix into fuzzy numerical values ​​for calculation to obtain the comparison matrix of the k-th decision-maker: ； ； Where n represents the number of criteria. , , Let represent the three element values ​​of criterion i compared to criterion j for the k-th decision-maker, where u represents upper, m represents middle, and l represents lower; (3.3) Based on the weighted geometric mean method, the fuzzy comparison matrices of the K decision-makers are summarized, and the average comparison matrix is ​​calculated as follows: ； ； (3.4) Construct an FLLS method to determine the weights of each criterion: ； ； in, It is the weight of the i-th criterion. , and yes The three elements, To determine the importance of criterion i compared to criterion j, , , Each corresponds to a value of one of the three elements.

5. The method for evaluating the multi-performance characteristics of intelligent connected vehicle platooning according to claim 4, characterized in that, When K=1 or K>1, the K decision-makers are of the same type.

6. The method for evaluating the multi-performance characteristics of intelligent connected vehicle platooning as described in claim 1, characterized in that, The performance ranking of different CAV queues based on the GRA-TOPSIS method includes the following steps: (4.1) Numerical experiments were conducted on CAV queues under different car-following strategies; (4.2) Determine the decision matrix based on the simulation results; (4.3) Normalize the values ​​of each criterion in the decision matrix of different CAV queues, i.e. : ； Where m represents the number of CAV queues, This represents the calculated value of the j-th criterion for the i-th CAV queue; (4.4) Based on the criterion weights and the obtained normalization results, calculate the weighted normalized decision matrix. For ease of calculation, the weights of each indicator are converted into clear numerical values. The formula for calculating the weighted normalized matrix is ​​as follows: ； ； in, Let be the weighted value of the j-th criterion in the i-th CAV queue, and n be the number of criteria; (4.5) Calculate PIS, representing the best possible ideal solution, and NIS, representing the worst possible solution, respectively: ； ； (4.6) The grey relational coefficients between each indicator and PIS and NIS can be expressed by the following formula: ； ； (4.7) The grey relational level of each CAV queue to PIS and NIS is calculated as follows: ； ； (4.8) Determine the relative closeness of the grey relational degree of the CAV queues, and compare them based on this value to obtain the sorting order of the CAV queues: ； in, It represents the relative proximity of the gray relational degree of the i-th CAV queue, and its value is located in the interval [0,1].

7. A multi-performance evaluation system for intelligent connected vehicle platooning, characterized in that, include: The car-following control module is used to establish a CAV queue control model that adapts to different traffic conditions based on different car-following strategies. The different car-following strategies include one or more of the following strategies: fixed time interval (CTG) strategy, fixed distance (CS) strategy, and a combination of CTG and CS strategy, CTG-CS. The fixed time interval (CTG) strategy dynamically adjusts the car-following behavior of vehicles by setting a fixed time interval and calculating the desired distance between vehicles based on the product of the time interval and the speed of the following vehicle. The fixed-distance CS strategy ensures a stable expected distance between vehicles by maintaining a fixed distance between them. Under the hybrid strategy, vehicles in the CAV queue are randomly distributed based on CTG and CS strategies to form a hybrid queue mode. The perception system and V2V communication system are used to obtain the speed, acceleration and position information of the vehicle in front to achieve real-time control. The evaluation index module is used to establish a multi-criteria evaluation system. By setting different weights for different criteria, it can meet the diverse needs of decision-makers for platooning performance. The multi-criteria evaluation system includes one or more performance standards among emissions, energy consumption, efficiency, and safety. The weight determination module is used to accurately calculate the weights of each criterion based on the decision-maker's linguistic variable data and the fuzzy log least squares method (FLLS). The resulting weights can reflect the priorities of different decision-makers in comparing different criteria. The system also includes a comprehensive ranking module, which embeds Grey Relational Analysis (GRA) into the Ideal Solution Similarity Ranking Method (TOPSIS) to construct the GRA-TOPSIS method. Based on simulation or measured data, it calculates a comprehensive evaluation index to assess the multi-performance characteristics of CAV platooning, thereby ranking the evaluation results of different strategies. The CAV platooning performance is measured and evaluated using data generated by the simulation system. First, vehicle position and speed data are generated to simulate the driving trajectory of the CAV platoon. Then, using a set car-following control model and control parameters, one or more performance characteristics of the vehicles, including emissions, energy consumption, efficiency, and safety, are calculated. The data is then organized into a decision matrix. Finally, FLLS and GRA-TOPSIS methods are used for data analysis to calculate normalized weight values ​​and obtain the performance of the CAV platoon under different strategies.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the multi-performance evaluation method for intelligent connected vehicle platooning as described in any one of claims 1-6.

Images