A cooperative control method for trusted interaction of intelligent connected vehicle groups under a zero-trust architecture

Abstract

This invention relates to the field of intelligent connected vehicle technology, addressing the shortcomings of existing intelligent connected vehicle communication network trust assessment and consistency coordination. Specifically, it relates to a collaborative control method for trusted interaction among intelligent connected vehicle groups under a zero-trust architecture. By fusing direct and indirect trust functions to obtain a dynamic comprehensive trust assessment function, it achieves real-time fusion of the global communication network and the comprehensive trust calculation model, accurately quantifying the degree of interaction between vehicles and significantly improving the safety performance of ICV group interaction. A distributed collaborative control algorithm based on state feedback adapts to differences in vehicle trust levels, achieving the goal of consistent coordination among vehicle queues and ensuring the stability of collaborative control in dynamic communication scenarios for the ICV group. The integrated design of zero-trust security protection and vehicle communication network allows the security of communication relationships in the ICV group system to be updated in real time, adapting to the communication topology changes under dynamic vehicle movement.

Description

Technical Field

[0001] This invention relates to the field of intelligent connected vehicle technology, and in particular to a collaborative control method for trusted interaction of intelligent connected vehicle groups under a zero-trust architecture, which is applied to the automotive electronics and vehicle networking industries related to collaborative control of intelligent connected vehicle groups. Background Technology

[0002] With the rapid development of road traffic, communication technology, and the intelligent vehicle industry chain, intelligent connected vehicles (ICVs) have emerged. ICVs can exchange information with surrounding vehicles or intelligent road infrastructure. Through reasonable control algorithms and technologies, they can effectively reduce road congestion and traffic accidents, possessing significant traffic optimization value and thus becoming an important direction for industry development.

[0003] However, intelligent connected vehicles face severe security challenges during communication and interaction. Information and data are vulnerable to tampering, leakage, or malicious attacks. Furthermore, the dynamic nature of vehicle-to-vehicle communication, characterized by unpredictable interruptions, connections, and free networking, necessitates a zero-trust architecture that adheres to the principle of "verification at all times, never trusting." However, current research on ICV swarm queue collaborative control under a zero-trust architecture is still in its early stages, exhibiting key shortcomings: existing studies often only incorporate trust levels into vehicle mathematical models or control algorithm design, neglecting the impact of trust values ​​on the communication network. This prevents optimization of the communication interaction topology through trust level adjustment, thus overlooking the overall security performance of queue communication. While some studies focus on trust assessment or threat mitigation, they fail to consider physical factors such as vehicle spacing, making it difficult to achieve a balance between security and collaborative control. Consequently, it cannot guarantee that ICV swarms meet both interaction security requirements and achieve stable queue collaborative control goals in dynamic communication scenarios. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a collaborative control method for trusted interaction among intelligent connected vehicle groups under a zero-trust architecture. This method solves the technical problems of insufficient trust assessment and consistency coordination in existing intelligent connected vehicle communication networks, such as the lack of an effective trust assessment mechanism, the single factor considered in the trust assessment function, and the unreliability of consistency control parameters.

[0005] Based on the above background description, the technical problem to be solved by the present invention is: How to construct a trust calculation model that can quantify the degree of interaction between vehicles under a zero-trust architecture, and realize the real-time integration of this model with the ICV swarm communication network, so as to solve the problem of insufficient overall queue communication security performance caused by ignoring the impact of trust value on the communication network in existing technologies; How to design a collaborative control algorithm that adapts to dynamic communication scenarios and addresses the differences in trust levels between vehicles, thereby solving the problem that existing technologies cannot balance communication security and queue collaborative control stability, and thus cannot achieve the goal of consistent collaboration among ICV group queues.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a collaborative control method for trusted interaction among intelligent connected vehicle groups under a zero-trust architecture, comprising the following steps: Step 1: Construct a trust assessment function framework. Clarify that the trust assessment between nodes consists of direct trust functions and indirect trust functions. Limit the assessment scope to nodes that can directly send information, excluding the target vehicle. Exclude trust considerations for nodes without direct connectivity to avoid unnecessary communication burden. Simultaneously, determine the set of vehicle trust identification frameworks under the zero-trust framework, laying the foundation for subsequent trust calculations.

[0007] Step 2: Calculate direct trust and indirect trust. For direct trust calculation, the basic probability allocation under direct trust is determined based on the number of vehicle-to-vehicle communications. An uncertainty range is obtained through the trust function and likelihood function. A trust decay function is introduced to offset the side effects of historical data. The specific direct trust value is obtained by taking the mean of the endpoints of the uncertainty range and combining it with the trust decay function. In terms of indirect trust calculation, a set of trust information about the target vehicle from third-party nodes is collected. The node conflict coefficient between third-party nodes is calculated based on the Dempster-Shafer evidence theory. The trust assessment results of third-party nodes are integrated according to the node conflict coefficient, and then the specific indirect trust value is obtained by using the same calculation method as the direct trust value.

[0008] Step 3: Integrate and generate comprehensive trust score and time-varying communication weights. Combining the calculation results of direct and indirect trust scores, a dynamic comprehensive trust evaluation function is constructed by introducing weight factors. A trust score threshold is set based on the comprehensive trust score, and time-varying communication weights are generated based on the real-time calculation value of the comprehensive trust score. Then, a time-varying adjacency weight matrix and a time-varying Laplacian matrix are constructed, and the comprehensive trust score is integrated into the communication links between nodes.

[0009] Step 4: Design a queue control algorithm under a zero-trust architecture. When the overall trust level between nodes does not reach the trust threshold, the corresponding information transmission link is disconnected. At the same time, to prevent vehicles from moving out of the queue and affecting the formation, a queue control objective is designed with the distance between the following vehicle and the leader vehicle as the core. Based on the vehicle dynamics model and the queue control objective, a corresponding queue control algorithm is generated to ensure the normal operation and safety of the queue.

[0010] This invention obtains a dynamic comprehensive trust evaluation function by fusing direct trust functions and indirect trust functions, thereby achieving real-time fusion of the global communication network and the comprehensive trust degree calculation model, accurately quantifying the degree of interaction between vehicles, and significantly improving the safety performance of ICV group interaction. This invention provides a distributed cooperative control algorithm based on state feedback that adapts to differences in vehicle trust levels. It can achieve a consistent cooperative goal for vehicle queues, ensure that vehicle queues reach an asymptotically stable and chordally stable state, and guarantee the cooperative control stability of ICV groups in dynamic communication scenarios. This invention adopts a design that integrates zero-trust security protection with vehicle communication networks, enabling the security of communication relationships in the ICV group system to be updated in real time and adapting to the communication topology changes under dynamic vehicle movement.

[0011] Furthermore, the direct trust function includes a trust decay function to offset the negative impact of historical data on the current direct trust assessment result, and takes the mean of the endpoint values ​​of the uncertainty range as the direct trust value. The indirect trust function processes third-party node trust information based on Dempster-Shafer evidence theory. It judges and integrates the trust evaluation results of different third-party nodes through node conflict coefficients, and obtains the indirect trust value using the same method as the calculation of the direct trust value.

[0012] Furthermore, the method for calculating the direct trust value includes: The basic probability allocation under direct trust is determined based on the number of inter-vehicle communications. The uncertainty range is obtained through the trust function and the likelihood function. A trust decay function is introduced to offset the side effects of historical data. The specific direct trust value is obtained by taking the mean of the endpoints of the uncertainty range and combining it with the trust decay function.

[0013] Furthermore, the basic probability allocation under direct trust is expressed as follows: ; In the formula, This represents the total number of communications from vehicle j to vehicle i within a given period of time. This indicates the number of times vehicle j has gained communication trust from vehicle i; This indicates the number of times vehicle j has received untrusted communication from vehicle i; This represents the probability that vehicle i considers vehicle j to be trustworthy under direct trust. This represents the probability that vehicle i considers vehicle j to be untrustworthy under direct trust. This represents the probability that the trust level of vehicle i to vehicle j is uncertain under direct trust. It should be a positive number to avoid the denominator being 0; The uncertainty range of the direct trust level is: ; The trust function is expressed as: The likelihood function is expressed as: ; The trust decay function The expression is:

[0014] In the formula, Indicates the weighting coefficient; Indicates the first Next communication time The attenuation; Indicates the first Next communication time The attenuation; the attenuation coefficient satisfies ;

[0015] The expression for the direct trust function is:

[0016] In the formula, Represents a node With nodes Direct trust value between them; To adjust the parameters.

[0017] Furthermore, the method for calculating the indirect trust value includes: Collect a set of trust information about the target vehicle from third-party nodes, calculate the node conflict coefficient between third-party nodes based on the Dempster-Shafer evidence theory, integrate the trust assessment results of third-party nodes based on the node conflict coefficient, and then use the calculation method consistent with the direct trust value to obtain the specific indirect trust value.

[0018] Furthermore, the basic probability allocation under indirect trust is expressed as follows:

[0019] in, This indicates that vehicle j obtains the vehicle set node. The number of times communication trust is established; This indicates that vehicle j obtains the vehicle set node. The number of times communication was not trusted; This indicates that vehicle j moves towards the vehicle set node within a certain period of time. Total number of communications; This represents the probability that the indirect node m1 considers vehicle j to be trustworthy under indirect trust. This represents the probability that the indirect node m1 considers vehicle j to be untrustworthy under indirect trust. This represents the probability that the trust level of indirect node m1 towards vehicle j is uncertain under indirect trust. The formula for calculating the conflict coefficient is as follows:

[0020] In the formula, Represents a node With nodes The conflict coefficient between them; Indicates the number of vehicle nodes; subscript Indicates an indirect node; This represents the probability that indirect node p considers vehicle j to be untrustworthy; This represents the probability that indirect node p considers vehicle j to be trustworthy; When the conflict coefficient If the conflict between third-party nodes is considered to be an incomplete conflict, then the fusion result is expressed as follows:

[0021] When the conflict coefficient If it is believed that conflict is inevitable, then the vehicles will be assembled at this time. Third-party node to node When transmitting information, it is an untrusted node; The formula for calculating the indirect trust value is as follows:

[0022] In the formula, Indicates third-party nodes and nodes Indirect trust value between them; The likelihood function representing indirect trust; The trust function represents indirect trust.

[0023] Furthermore, the dynamic comprehensive trust evaluation function The expression is:

[0024] in, This represents the weighting factor between the direct trust function and the indirect trust function. Represents a node With nodes Direct trust value between them; Indicates third-party nodes and nodes Indirect trust value between them.

[0025] Furthermore, the expression for the time-varying communication weight is:

[0026] In the formula, This refers to the time-varying communication weight value; This indicates the trust threshold.

[0027] Furthermore, the expression for the queue control target is:

[0028] in, Indicates the position of vehicle i; This represents the distance between vehicle i and the leader's vehicle, and is a constant value. This represents the acceleration of vehicle i; Indicates the speed of vehicle i; Indicates the location of the leader's vehicle; Indicates the acceleration of the leader's vehicle; Indicates the speed of the leader's vehicle.

[0029] By employing the above technical solution, the present invention provides a collaborative control method for trusted interaction among intelligent connected vehicle groups under a zero-trust architecture, which has at least the following beneficial effects: This invention reduces the communication burden between vehicles and improves communication efficiency. It limits the scope of interaction nodes for trust assessment to nodes that can directly send information, excluding the target vehicle. This avoids the cumbersome process of collecting trust information from all nodes or establishing a central server, as required by traditional trust assessment methods. It reduces unnecessary communication interactions, making the communication relationships between vehicles more aligned with actual needs and effectively reducing the communication burden.

[0030] To improve the real-time performance and accuracy of trust assessment, a trust decay function is introduced into the direct trust value calculation. By reasonably setting the trust decay coefficient, the decay rate of historical trust values ​​in the distant future is greater than that of recent trust values, thus offsetting the negative impact of historical data on the current assessment results and making the trust value more consistent with the real-time interaction status. The calculation of indirect trust value is based on the Dempster-Shafer evidence theory to process the trust information of third-party nodes. By judging and integrating the trust assessment results of different third-party nodes through the conflict coefficient, the problem of inconsistent third-party judgments is solved, further ensuring the accuracy of trust assessment.

[0031] To ensure the stability and safety of vehicle platooning, a control objective and corresponding platooning control algorithm are designed based on the distance between following vehicles and the leader vehicle, addressing scenarios where the overall trust level is below the trust threshold. This eliminates the need to directly remove low-trust vehicles from the platoon, avoiding platooning disorder or rear-end collision risks. Furthermore, following vehicles directly receive information from the leader vehicle, independent of neighboring vehicle status trust, ensuring the platoon can continue operating normally even if the trust level of some nodes decreases, thus improving platooning stability and safety redundancy. Attached Figure Description

[0032] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is an example diagram of the ICV group cooperative control process under the zero-trust architecture in this invention; Figure 2 This is a schematic diagram of the ICV group cooperative control architecture under the zero-trust architecture in this invention; Figure 3 This is a diagram of the zero-trust intelligent connected vehicle interaction network in this invention; Figure 4 This is a flowchart of the trust assessment process in this invention. Detailed Implementation

[0033] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This will allow for a full understanding of how the present application uses technical means to solve technical problems and achieve technical effects, and to facilitate its implementation.

[0034] Existing research on zero-trust vehicle queuing control often assumes that the communication relationship between vehicles is constant. The main approach is to introduce a constructed trust evaluation function into the controller gain parameter. Although this method can effectively adjust the controller gain according to the trust strength to achieve queuing control, it ignores the impact of trust on the communication connection relationship. This makes the neighbor node information that each node can obtain remain constant, thus causing new security challenges for the vehicle queuing.

[0035] Therefore, to address communication security and vehicle platooning control issues in zero-trust environments, this embodiment proposes a cooperative control method for trusted interaction of intelligent connected vehicle groups under a zero-trust architecture, aiming to improve the cooperative robustness of connected autonomous vehicle groups under a zero-trust architecture. Figure 1 As shown, the basic process of queue collaborative control in an ICV swarm system under a zero-trust architecture is illustrated. A real-time dynamic comprehensive trust evaluation function can quantify the interaction relationships between nodes, thereby constructing a communication interaction network with trusted nodes. Implementing queue collaborative control in an ICV swarm system can effectively reduce congestion and traffic accidents. Introducing a zero-trust architecture can improve the security of communication interactions in the ICV system. The method includes the following steps: S1. Under the zero-trust architecture, establish an evaluation scope for trust assessment between nodes. The evaluation scope includes nodes that can directly send information except for the target vehicle, excluding trust considerations for nodes without direct connection relationships, avoiding unnecessary communication burdens. At the same time, determine the identification framework set for vehicle trust under the zero-trust framework, laying the foundation for subsequent trust calculation.

[0036] In a zero-trust security architecture, frequent security verifications are performed between vehicles, typically involving nodes. For nodes ( Trust assessment can be composed of direct trust functions and indirect trust functions. The core of the direct trust function is the node. With nodes Historical trust records are generated through direct interaction. The core of the indirect trust function, however, relies on other interaction nodes. ( In relation to nodes The trust information set obtained during the interaction is then passed to the node. However, it is clear that if all other nodes need to send data to the node... Send to node This requires establishing a central server to collect trust information, or having other nodes independently send the trust information to the nodes. This results in unrestricted communication between vehicles, increasing the communication burden and hindering the previously established communication relationships. Therefore, the remaining interactive nodes mentioned in this embodiment... This represents the node excluding the node. Other than that, it can be directed to the node. For nodes that send information and have no direct connection, the level of trust between them does not need to be considered.

[0037] S2. Based on the aforementioned evaluation scope, construct a dynamic comprehensive trust evaluation function that includes a direct trust function and an indirect trust function to obtain a comprehensive trust level used to quantify the degree of interaction between nodes. For example... Figure 4 As shown, this embodiment uses DS evidence theory to construct a comprehensive trust calculation model based on direct and indirect communication trust between vehicles, achieving accurate quantification of vehicle interaction levels. Simultaneously, a trust-driven communication interaction topology is constructed based on the comprehensive trust, enabling real-time dynamic fusion of the global communication network and the dynamic comprehensive trust evaluation function, allowing for real-time updates to the security of vehicle group communication relationships.

[0038] The direct trust function includes a trust decay function to offset the negative impact of historical data on the current direct trust assessment result, and takes the average of the endpoints of the uncertainty range as the direct trust value. Because in a zero-trust framework, nodes... For nodes Trust levels can only be categorized as trust and distrust Therefore, its identification framework set can be represented as , Pick express proper subsets of, i.e. , , Let the total number of communications from vehicle j to vehicle i satisfy the following condition: , then for a period of time Within this framework, the Basic Probability Assignment (BPA) under direct trust can be expressed as:

[0039] In the formula, This represents the total number of communications from vehicle j to vehicle i within a given period of time. This indicates the number of times vehicle j has gained communication trust from vehicle i; This indicates the number of times vehicle j has received untrusted communication from vehicle i; This represents the probability that vehicle i considers vehicle j to be trustworthy under direct trust. This represents the probability that vehicle i considers vehicle j to be untrustworthy under direct trust. This represents the probability that the trust level of vehicle i to vehicle j is uncertain under direct trust. It should be a positive number to avoid the denominator being 0.

[0040] Then, the trust function based on direct trust and likelihood function The uncertain range of its direct trust level can be obtained, that is: The trust function is expressed as: The likelihood function is expressed as: .

[0041] To obtain a specific direct trust value, and considering that historical direct trust values ​​gradually decrease over time and their impact on the current direct trust evaluation result, thus making the node's trust value more real-time, this embodiment introduces a trust decay function to offset the side effects of historical data on the direct trust value. Trust Decay Function It has the following form:

[0042] In the formula, Indicates the weighting coefficient; Indicates the first Next communication time The attenuation; Indicates the first Next communication time The decay of long-term trust values ​​relative to short-term trust values ​​should be greater than that of short-term trust values; therefore, its decay coefficient satisfies the following condition. .

[0043] Based on this, in order to obtain a specific direct trust value, this embodiment takes the average of the endpoints of the uncertainty range of the direct trust degree as the direct trust value, combined with the proposed trust decay function. The expression for the direct trust function is:

[0044] In the formula, Represents a node With nodes Direct trust value between them; To adjust the parameters.

[0045] The indirect trust function processes third-party node trust information based on Dempster-Shafer evidence theory. It determines and merges the trust assessment results of different third-party nodes through node conflict coefficients, and obtains the indirect trust value using a method consistent with the calculation of the direct trust value. The indirect trust degree involves the fusion of third-party trust degrees, and the third-party nodes... ( In obtaining information about nodes After collecting the trust information, it is passed to the node. Assuming except for node In addition, it can send to nodes The nodes that send information are ( Let's temporarily represent these nodes as a set of vehicles. With third-party nodes For example, vehicle collection Basic probability assignment of remaining nodes and vehicle set nodes If they are the same, then the nodes Through vehicle aggregation node Get Nodes The basic probability can be expressed as:

[0046] in, This indicates that vehicle j obtains the vehicle set node. The number of times communication trust is established; This indicates that vehicle j obtains the vehicle set node. The number of times communication was not trusted; This indicates that vehicle j moves towards the vehicle set node within a certain period of time. Total number of communications; This represents the probability that the indirect node m1 considers vehicle j to be trustworthy under indirect trust. This represents the probability that the indirect node m1 considers vehicle j to be untrustworthy under indirect trust. This represents the probability that the trust level of indirect node m1 towards vehicle j is uncertain under indirect trust.

[0047] Due to the vehicle assembly If the nodes in the network have inconsistent evaluations of vehicle j, it will lead to differences in their trust levels. Based on the Dempster-Shafer evidence theory, the conflict coefficient between third-party nodes can be calculated first, and the calculation formula is as follows:

[0048] In the formula, Represents a node With nodes The conflict coefficient between them; Indicates the number of vehicle nodes; subscript Indicates an indirect node; This represents the probability that indirect node p considers vehicle j to be untrustworthy; This represents the probability that indirect node p considers vehicle j to be trustworthy.

[0049] When the conflict coefficient When the conflict between third-party nodes is considered to be an incomplete conflict, the fusion result can be expressed as:

[0050] When the conflict coefficient If it is believed that conflict is inevitable, then the vehicles will be assembled at this time. Third-party node to node When transmitting information, it is a non-trusted node.

[0051] Using the trust calculation method in direct trust, the formula for calculating indirect trust value can be obtained as follows:

[0052] In the formula, Indicates third-party nodes and nodes Indirect trust value between them; The likelihood function representing indirect trust; The trust function represents indirect trust.

[0053] This embodiment introduces a weighting factor and combines the direct trust function with the indirect trust function to obtain a dynamic comprehensive trust evaluation function. as follows:

[0054] in, This represents the weighting factor between the direct trust function and the indirect trust function.

[0055] S3. Based on the comprehensive trust level, a trust level threshold is set, and time-varying communication weights are generated according to the real-time calculation value of the comprehensive trust level. Then, a time-varying adjacency weight matrix and a time-varying Laplacian matrix are constructed to integrate the comprehensive trust level into the communication links between nodes. This achieves real-time dynamic integration of the global communication network and the comprehensive trust level calculation model, enabling the security of vehicle group communication relationships to be updated in real time.

[0056] Dynamic integrated trust evaluation function When integrated into communication links, time-varying communication weights can be established based on trust levels, as follows:

[0057] In the formula, This refers to the time-varying communication weight value; This indicates the trust threshold.

[0058] Therefore, the time-varying adjacency weight matrix It can be represented as: ,

[0059] Time-varying Laplacian matrix Represented as:

[0060] in, express A real matrix of dimension 1; Representation degree matrix.

[0061] S4. When the overall trust level between nodes does not reach the trust threshold, disconnect the corresponding information transmission link, and at the same time establish a queue control target with the distance between the following vehicle and the leader vehicle as the core, in order to achieve consistent and coordinated control of the vehicle queue.

[0062] In a zero-trust architecture, when node j's overall trust in node i fails to reach the trust threshold, it will disconnect from node i. At this point, node j's security performance is low, but the vehicle queue will not immediately remove the vehicle from the queue; otherwise, it would affect the overall queue formation and potentially cause a rear-end collision. To maintain the normal operation and control of the vehicle queue, the following queue control objectives are designed:

[0063] in, Indicates the position of vehicle i; This represents the distance between vehicle i and the leader's vehicle, and is a constant value. This represents the acceleration of vehicle i; Indicates the speed of vehicle i; Indicates the position of the leader node; Indicates the acceleration of the leader node; This indicates the speed of the leader node.

[0064] This embodiment establishes a control target directly with the leader vehicle, so that following vehicles do not rely on the state of neighboring vehicles for trust. Therefore, when the trust level decreases, the node can still receive information from the leader vehicle, thereby ensuring the normal operation and safety of the queue.

[0065] This embodiment considers the differences in trust levels among following vehicles and designs a distributed cooperative control algorithm based on state feedback to achieve the goal of consistent cooperative control of the vehicle platoon. Therefore, based on the description of the platoon control goal, a platoon control algorithm with the following form is designed:

[0066] in, This represents the control input for the follower node; This indicates the inertial delay of the leader's vehicle. Indicates the controller gain coefficient; This represents the time-varying communication weight value of the leader node relative to node i; This represents the inertial delay of vehicle i; Indicates the position of vehicle j; This indicates the distance between vehicle j and the leader's vehicle; Indicates the speed of vehicle j; This represents the acceleration of vehicle j.

[0067] Based on the above analysis, the key steps for building trusted interaction among intelligent connected vehicle groups under a zero-trust architecture are as follows: Figure 2 As shown. First, the formation spacing can be set using radar equipment mounted on the intelligent connected vehicle system; simultaneously, vehicle dynamics model analysis is conducted and integrated with a more targeted mathematical model of the longitudinal system of intelligent connected vehicles. Second, inter-vehicle communication interaction rules are defined based on graph theory methods to construct an inter-vehicle communication interaction network; based on the improved DS evidence discrimination theory, a comprehensive trust function is constructed by integrating direct trust and indirect trust, and a threshold is introduced to achieve dynamic linkage between trust value and communication relationship weight.

[0068] The core features of the collaborative control method proposed in this embodiment are as follows: The DS evidence discrimination method is improved, and a dynamic access trust assessment method is proposed. This method integrates historical direct trust and indirect trust between nodes to achieve accurate assessment of the dynamic trust level of nodes and improve the security performance of the interaction environment. A vehicle-group communication and interaction network is constructed based on a dynamic comprehensive trust evaluation function, and the dynamic trust weights and edge weights are integrated into the cooperative control algorithm, such as... Figure 3As shown, unlike traditional zero-trust control methods that directly introduce trust values, this approach can dynamically characterize the impact of trust levels on communication networks, thereby improving network security performance.

[0069] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. Since the above embodiments are substantially similar to the method embodiments, they are described relatively simply; relevant parts can be referred to the descriptions of the method embodiments.

[0071] The above embodiments provide a detailed description of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A collaborative control method for trusted interaction among intelligent connected vehicle groups under a zero-trust architecture, characterized in that, The method includes the following steps: In a zero-trust architecture, an evaluation scope is established for assessing trust between nodes. This evaluation scope includes nodes that can directly send information, excluding the target vehicle, and excludes trust considerations for nodes with no direct connection relationship. Based on the aforementioned evaluation scope, a dynamic comprehensive trust evaluation function, including a direct trust function and an indirect trust function, is constructed to obtain a comprehensive trust level used to quantify the degree of interaction between nodes. A trust threshold is set based on the comprehensive trust level, and a time-varying communication weight is generated based on the real-time calculation value of the comprehensive trust level, so as to integrate the comprehensive trust level into the communication link between nodes. When the overall trust level between nodes does not reach the trust threshold, the corresponding information transmission link is disconnected. At the same time, a queue control target is established with the distance between the following vehicle and the leader vehicle as the core, in order to achieve consistent and coordinated control of the vehicle queue.

2. The collaborative control method according to claim 1, characterized in that, The direct trust function includes a trust decay function to offset the negative impact of historical data on the current direct trust assessment result, and takes the mean of the endpoint values ​​of the uncertainty range as the direct trust value. The indirect trust function processes third-party node trust information based on Dempster-Shafer evidence theory, judges and integrates the trust evaluation results of different third-party nodes through node conflict coefficients, and obtains the indirect trust value using the same method as the calculation of the direct trust value.

3. The cooperative control method according to claim 2, characterized in that, The method for calculating the direct trust value includes: The basic probability allocation under direct trust is determined based on the number of inter-vehicle communications. The uncertainty range is obtained through the trust function and the likelihood function. A trust decay function is introduced to offset the side effects of historical data. The specific direct trust value is obtained by taking the mean of the endpoints of the uncertainty range and combining it with the trust decay function.

4. The collaborative control method according to claim 3, characterized in that, The basic probability allocation under direct trust is expressed as follows: ； In the formula, This represents the total number of communications from vehicle j to vehicle i within a given period of time. This indicates the number of times vehicle j has gained communication trust from vehicle i; This indicates the number of times vehicle j has received untrusted communication from vehicle i; This represents the probability that vehicle i considers vehicle j to be trustworthy under direct trust. This represents the probability that vehicle i considers vehicle j to be untrustworthy under direct trust. This represents the probability that the trust level of vehicle i to vehicle j is uncertain under direct trust. It should be a positive number to avoid the denominator being 0; The uncertainty range of the direct trust level is: ； The trust function is expressed as: The likelihood function is expressed as: ; The trust decay function The expression is: ； In the formula, Indicates the weighting coefficient; Indicates the first Next communication time The attenuation; Indicates the first Next communication time The attenuation; the attenuation coefficient satisfies ; The expression for the direct trust function is: ； In the formula, Represents a node With nodes Direct trust value between them; To adjust the parameters.

5. The cooperative control method according to claim 2, characterized in that, The method for calculating the indirect trust value includes: Collect a set of trust information about the target vehicle from third-party nodes, calculate the node conflict coefficient between third-party nodes based on the Dempster-Shafer evidence theory, integrate the trust assessment results of third-party nodes based on the node conflict coefficient, and then use the calculation method consistent with the direct trust value to obtain the specific indirect trust value.

6. The cooperative control method according to claim 5, characterized in that, The basic probability allocation under indirect trust is expressed as follows: ； in, This indicates that vehicle j obtains the vehicle set node. The number of times communication trust is established; This indicates that vehicle j obtains the vehicle set node. The number of times communication was not trusted; This indicates that vehicle j moves towards the vehicle set node within a certain period of time. Total number of communications; This represents the probability that the indirect node m1 considers vehicle j to be trustworthy under indirect trust. This represents the probability that the indirect node m1 considers vehicle j to be untrustworthy under indirect trust. This represents the probability that the trust level of indirect node m1 towards vehicle j is uncertain under indirect trust. The formula for calculating the conflict coefficient is as follows: ； In the formula, Represents a node With nodes The conflict coefficient between them; Indicates the number of vehicle nodes; subscript Indicates an indirect node; This represents the probability that indirect node p considers vehicle j to be untrustworthy; This represents the probability that indirect node p considers vehicle j to be trustworthy; When the conflict coefficient If the conflict between third-party nodes is considered to be an incomplete conflict, then the fusion result is expressed as follows: ； When the conflict coefficient If it is believed that conflict is inevitable, then the vehicles will be assembled at this time. Third-party node to node When transmitting information, it is an untrusted node; The formula for calculating the indirect trust value is as follows: ； In the formula, Indicates third-party nodes and nodes Indirect trust value between them; The likelihood function representing indirect trust; The trust function represents indirect trust.

7. The cooperative control method according to claim 1, characterized in that, The dynamic comprehensive trust evaluation function The expression is: ； in, This represents the weighting factor between the direct trust function and the indirect trust function. Represents a node With nodes Direct trust value between them; Indicates third-party nodes and nodes Indirect trust value between them.

8. The cooperative control method according to claim 1, characterized in that, The expression for the time-varying communication weight is: ； In the formula, This refers to the time-varying communication weight value; This indicates the trust threshold.

9. The cooperative control method according to claim 1, characterized in that, The expression for the queue control objective is: ； in, Indicates the position of vehicle i; This represents the distance between vehicle i and the leader's vehicle, and is a constant value. This represents the acceleration of vehicle i; Indicates the speed of vehicle i; Indicates the location of the leader's vehicle; Indicates the acceleration of the leader's vehicle; Indicates the speed of the leader's vehicle.

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