Vehicle platoon control method based on encrypted transmission of state information in zero-trust environment

Abstract

The present invention discloses a vehicle queue control method based on encrypted transmission of state information in a zero-trust environment, including: each following vehicle in the vehicle queue receives the state information sent by its preceding vehicle; wherein the state information is encrypted and decrypted and verified by digital envelope technology when the vehicles send and receive the state information; each following vehicle in the vehicle queue performs the following operations: using a model prediction algorithm to calculate and predict the state information of the current vehicle at Np moments in the future; using a state prediction model to calculate the expected state information of the current vehicle at Np moments in the future; using an MPC objective function to calculate and generate the optimal control input of the current vehicle; and finally controlling its operating state based on the optimal control input of the current vehicle. The present invention introduces symmetric encryption algorithms and asymmetric encryption algorithms under a zero-trust framework, and at the same time predicts the state and expected state at future moments through the state information of the vehicle and its preceding vehicle to generate control inputs that meet the vehicle queue control requirements.

Description

Technical Field

[0001] The present invention relates to the field of Internet big data and zero-trust architecture, and in particular to a vehicle queue control method based on encrypted transmission of state information in a zero-trust environment. Background Art

[0002] As the number of cars increases year by year, social problems such as road safety, traffic congestion and air pollution have also increased significantly. In order to effectively solve these problems, intelligent transportation systems (ITS) have attracted much attention in recent years, and vehicle platoon control has become one of the important applications in ITS. A platoon consists of a group of vehicles, including a leader and several follower vehicles, which travel in coordination, usually at the same speed and a short interval. Compared with traditional driving modes, platoon driving has the advantages of increasing the throughput of road vehicles, improving traffic efficiency, enhancing road safety and reducing fuel consumption. Because of the use of the leader-follower mechanism, on the one hand, the distance between vehicles is effectively reduced, which to a certain extent increases road capacity and alleviates traffic congestion. On the other hand, platoon driving reduces air resistance, thereby reducing fuel consumption and air pollution, making a positive contribution to the construction of a sustainable transportation system.

[0003] Based on the driving mode of vehicle platoons, V2V communication in VANETs is used to realize information exchange between platoon vehicles. V2V communication enables vehicles to share current status information such as position, speed, acceleration, heading angle, etc., quickly respond to changes in the behavior of the vehicle in front, improve the fault tolerance of the system, and ensure the rapidity and accuracy of decision-making. In the cooperative control of vehicle platoons, secure communication is crucial, especially considering that communication usually occurs in open wireless channels. However, when the status information between vehicles in the platoon is transmitted through the network, it faces security and privacy issues such as network-induced delays, data packet loss caused by intruders or malicious nodes in the network, information leakage, and information tampering. These problems caused by the communication network will inevitably affect the communication between vehicles and undermine the performance of the entire control system.

[0004] In response to the above problems, the zero-trust architecture provides a solution that effectively responds to security threats by strictly authenticating and encrypting each network request. The core concept of the zero-trust architecture is to not trust any network node, whether it is located in the internal network or the external network. By implementing the principle of least privilege, dynamic authorization, and continuous monitoring, the zero-trust architecture ensures the authenticity, integrity, and confidentiality of the Internet of Vehicles (IoV) data. In the network control system, the zero-trust architecture can select appropriate transmission strategies to respond to potential network attacks and ensure the security consensus of the vehicle fleet.

[0005] However, although the zero-trust architecture has provided a strong authentication and access control mechanism, there is still the problem of state information being stolen or tampered when transmitting state information between vehicles, which makes it difficult to ensure the security and integrity of state information transmission. Therefore, under the zero-trust architecture, it is a technical problem that needs to be solved urgently to achieve the trusted interaction of intelligent networked fleets and the safe and stable operation of vehicle queue control based on encrypted communication technology of state information. Summary of the invention

[0006] In view of the deficiencies of the above-mentioned prior art, the technical problem to be solved by the present invention is: how to provide a vehicle queue control method based on encrypted transmission of state information in a zero-trust environment, introduce symmetric encryption algorithms and asymmetric encryption algorithms under the zero-trust architecture to realize encryption and decryption as well as identity authentication in the process of state information transmission, and improve the security and integrity of state information transmission; at the same time, predict the state and expected state at future moments through the state information of the vehicle and its preceding vehicle, so as to generate control inputs that meet the requirements of vehicle queue control, thereby improving the stability and accuracy of vehicle queue control.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] The vehicle queue control method based on encrypted transmission of state information in a zero-trust environment includes:

[0009] S1: Construct the communication topology model of the vehicle platoon;

[0010] S2: construct a longitudinal vehicle dynamics model for each vehicle in the vehicle platoon to calculate its own state information;

[0011] S3: Each following vehicle in the vehicle queue receives the status information sent by the vehicle in front of it; when the status information is sent and received between vehicles, it is encrypted, decrypted and verified through digital envelope technology;

[0012] S4: Each following vehicle in the vehicle queue performs the following operations:

[0013] S401: The current vehicle calculates and predicts the state information of the current vehicle Np moments in the future based on its own state information at the current moment using a model prediction algorithm;

[0014] S402: The current vehicle inputs the state information of the vehicle ahead of it into the state prediction model to predict the expected state information of the current vehicle at Np moments in the future;

[0015] S403: The current vehicle generates an optimal control input based on the state information and expected state information of the next Np moments in time in combination with the objective function of the MPC;

[0016] S404: Controlling the operating state of the vehicle based on the optimal control input of the current vehicle.

[0017] Preferably, in step S1, a communication topology model is modeled for the vehicle queue based on a directed graph;

[0018] The vehicle platoon consists of a leading vehicle and N following vehicles. The status information between the N following vehicles is transmitted through a directed graph Indicates that one vehicle is regarded as a vehicle node; V N ={b1,b2,…,b N} represents a set of N vehicle nodes; Represents the set of edges from vehicle node to vehicle node, directed edges (b i ,b j ) indicates that vehicle node i can obtain the status information of vehicle node j.

[0019] Preferably, in step S2, the longitudinal vehicle dynamics model of each vehicle in the vehicle queue is expressed as:

[0020]

[0021] F g,i =m i gf i ;

[0022]

[0023] Where: p i (t) represents the position of vehicle i; v i (t) represents the speed of vehicle i; F T,i represents the driving force of vehicle i; m i represents the mass of vehicle i; F C,i represents the air resistance of vehicle i; F g,i represents the rolling resistance of vehicle i; τ represents the time lag constant of the longitudinal dynamic system; T i (t) represents the actual driving force torque of vehicle i; T des,i (t) represents the desired driving force torque of vehicle i; η i represents the mechanical efficiency of the transmission system of vehicle i; r i represents the tire radius of vehicle i; g represents the acceleration due to gravity; f i represents the rolling resistance coefficient of vehicle i; ρ represents the air density; A i represents the cross-sectional area of ​​vehicle i; u i represents the control input of vehicle i; C i (t) represents the air resistance coefficient of vehicle i; i∈{0,1,2,…,N} represents the vehicle number.

[0024] Preferably, in step S3, when status information is sent and received between vehicles, encryption and decryption of the status information and identity authentication of the vehicle are achieved by combining the AES symmetric encryption algorithm and the RSA asymmetric encryption algorithm.

[0025] Preferably, in step S3, the sending and receiving of status information between vehicles is achieved through the following steps:

[0026] S301: The sender, i.e. the preceding vehicle, calculates its own state information through a hash function to generate a message digest; at the same time, the sender signs the message digest with its own private key to generate a digital signature;

[0027] S302: The sender randomly generates a symmetric key, and encrypts its own state information, digital signature and public key using the symmetric key to obtain data ciphertext;

[0028] S303: The sender encrypts its symmetric key using the public key of the receiver, i.e., the following vehicle, to obtain a symmetric key ciphertext;

[0029] S304: The sender sends the data ciphertext and the symmetric key ciphertext to the receiver;

[0030] S305: The receiver decrypts the symmetric key ciphertext with its own private key to obtain the sender's symmetric key; then decrypts the data ciphertext with the sender's symmetric key to obtain the sender's status information, digital signature and public key;

[0031] S306: The receiver decrypts the digital signature using the sender's public key to obtain the sender's message digest, which is used to verify the sender's identity.

[0032] S307: The receiver calculates the sender's state information through a hash function to generate a real-time information summary; then compares the real-time information summary with the sender's information summary to verify the integrity and authenticity of the state information.

[0033] S308: After the identity authentication and integrity and authenticity verification are passed, the receiver receives the status information of the sender.

[0034] Preferably, 1) the logic of generating the public key and the private key is as follows:

[0035] Randomly select two larger prime numbers p and q, and calculate the public modulus n = pq of the public key and private key;

[0036] Computes Euler's function modulo n

[0037] Randomly select a public key exponent e, e and Mutually prime, and satisfy: Calculate the private key exponent d, satisfying ed = 1 (mod r);

[0038] The calculation results show that the public key is (n,e) and the private key is (n,d);

[0039] 2) The logic of using public key encryption is as follows:

[0040] The information to be encrypted is represented as an integer m, and m is encrypted using the public key (n, e). The ciphertext c = (m e )mod n;

[0041] 3) The logic of decryption using the private key is as follows:

[0042] The encrypted ciphertext information is decrypted with the private key (n, d), and the plaintext information m = (c d )mo dn.

[0043] Preferably, in step S401, the calculation formula of the model prediction algorithm is expressed as:

[0044] x t+1 =Ax t +Bu t ;

[0045] y t+1 =Cx t+1 ;

[0046]

[0047] Where: x t+1 、x t Respectively represent the state information at time t+1 and time t; u t represents the control input at time t; y t+1 represents the system output; T represents the sampling time.

[0048] Preferably, in step S501, the state information of the current vehicle at Np moments in the future is calculated by the following formula:

[0049]

[0050] Yi i =φX i ;

[0051]

[0052] Where: X i It represents the state information of vehicle i at Np moments in the future; Np represents the prediction time domain; Nc represents the control time domain.

[0053] Preferably, in step S402, the expected state information of the current vehicle at Np moments in the future is calculated by the following formula:

[0054]

[0055] Where: X i,ref represents the expected state information of vehicle i in the next Np moments; r t+1 represents the expected state information of vehicle i at time t+1; p j represents the position of vehicle j; v j represents the speed of vehicle j; a j represents the acceleration of vehicle j; (ij)d des represents the expected distance between vehicle i and vehicle j; T represents the sampling time.

[0056] Preferably, in step S503, if the vehicles in the queue exchange information using the LPF communication topology, the objective function of MPC in the zero-trust environment is expressed as:

[0057]

[0058] s.tu min ≤u i ≤u max ;

[0059] Where: Q, R and W are weight matrices; x t+j represents the state of vehicle i at time t+j; r F,t+j represents the expected state information of vehicle i at time t+j calculated based on the state information of the preceding vehicle; r L,t+j Tr represents the expected state information of vehicle i at time t+j calculated based on the state information of the pilot vehicle; F and Tr L are the trustworthiness of the leading car and the pilot car respectively; u t+j Represents the control amount of vehicle i at time t+j.

[0060] Compared with the prior art, the vehicle queue control method based on encrypted transmission of state information in a zero-trust environment of the present invention has the following beneficial effects:

[0061] Under the zero-trust framework, the present invention further realizes the encryption and decryption of state information and the identity authentication of vehicles by combining the AES symmetric encryption algorithm and the RSA asymmetric encryption algorithm. First, the AES symmetric encryption algorithm provides efficient encryption and decryption speed, ensuring that the transmission encryption process of state information between vehicles is highly real-time and efficient; and the RSA asymmetric encryption algorithm is used to safely exchange keys or verify identities, ensuring the security of key transmission, and then ensuring the security of encrypted information. Therefore, the combination of the AES symmetric encryption algorithm and the RSA asymmetric encryption algorithm can improve the security of state information transmission in the vehicle queue. Secondly, the RSA asymmetric encryption algorithm ensures the secure transmission of symmetric keys, and only the receiver (with a private key) can decrypt and obtain the sender's symmetric key, and then decrypt the encrypted data. At the same time, the sender uses the private key to digitally sign the state information, and the receiver uses the sender's public key to verify the signature to ensure the integrity of the information and the identity of the sender, effectively preventing identity fraud and the injection of forged information, and realizing identity authentication between vehicles. Finally, the encrypted transmission process not only protects the confidentiality of the information, but also can be combined with hash functions and digital signatures to verify the integrity and authenticity of the status information, thereby ensuring that the status information has not been tampered with during transmission.

[0062] The vehicle of the present invention calculates and predicts the state information at the future time based on its own state information at the current time using a model prediction algorithm. On the one hand, by predicting the state information at the future time, the vehicle can adjust its control strategy in advance to cope with possible external interference or environmental changes, thereby improving the stability of vehicle queue control. On the other hand, when each vehicle can predict its own future state, the vehicle queue can work better with other vehicles and reduce control conflicts caused by mutual interference between vehicles.

[0063] The present invention predicts the expected state information of the vehicle at a future time based on the state information of the vehicle in front of it. On the one hand, by considering the state information of the vehicle in front, the current vehicle can predict the expected state at a future time, which helps to achieve coordinated control in the vehicle queue, making the driving of the entire queue smoother and more orderly, thereby improving the coordination of vehicle queue control. On the other hand, combined with the state information of the vehicle in front, the current vehicle can more accurately predict its own future state, thereby more accurately calculating the optimal control input, thereby improving the accuracy of vehicle queue control.

[0064] Based on the prediction of the state information and expected state information of the vehicle at the future moment, the present invention further combines the objective function of MPC (model predictive control) to calculate the optimal control input. First, the MPC algorithm takes into account the state and expected state at the future moment, so that the control input of the vehicle is more accurate, which can better meet the driving needs and the stability requirements of the vehicle queue, and further improve the accuracy of the vehicle queue control. Secondly, combined with the coordinated control and state prediction between vehicles, the optimal control input that makes the entire queue stable and well coordinated can be calculated according to the optimization objective function of MPC, which further improves the overall performance of the vehicle queue. Finally, the MPC algorithm can handle constraints and take into account the dynamic characteristics of the system, so that the vehicle has a stronger dynamic response capability when facing complex environments and uncertainties. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to make the purpose, technical solution and advantages of the invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0066] Figure 1 A system diagram of vehicle platoon control based on encrypted transmission of state information in a zero-trust environment;

[0067] Figure 2 A flowchart of vehicle platoon control based on encrypted transmission of state information in a zero-trust environment;

[0068] Figure 3 A logical block diagram for encrypted transmission of state information;

[0069] Figure 4 This is the overall framework diagram of the vehicle queue control method based on encrypted transmission of state information in a zero-trust environment. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but only represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0071] It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In the description of the present invention, it should be noted that the orientation or position relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, or the orientation or position relationship in which the invention product is usually placed when used, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance. In addition, the terms "horizontal", "vertical", etc. do not mean that the components are required to be absolutely horizontal or suspended, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted. In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0072] The following is a further detailed description through specific implementation methods:

[0073] Example:

[0074] This embodiment discloses a vehicle queue control method based on encrypted transmission of state information in a zero-trust environment.

[0075] like Figure 1 and Figure 2 As shown, the vehicle queue control method based on encrypted transmission of state information in a zero-trust environment includes:

[0076] S1: Construct the communication topology model of the vehicle platoon;

[0077] S2: construct a longitudinal vehicle dynamics model for each vehicle in the vehicle platoon to calculate its own state information;

[0078] S3: Each following vehicle in the vehicle queue receives the (real-time) status information sent by the preceding vehicle; when the status information is sent and received between vehicles, it is encrypted, decrypted and verified through the digital envelope technology (DET);

[0079] S4: Each following vehicle in the vehicle queue performs the following operations:

[0080] S401: The current vehicle calculates and predicts the state information of the current vehicle Np moments in the future based on its own state information at the current moment using a model prediction algorithm;

[0081] S402: The current vehicle inputs the state information of the vehicle ahead of it into the state prediction model to predict the expected state information of the current vehicle at Np moments in the future;

[0082] S403: The current vehicle generates an optimal control input based on the state information and expected state information of the next Np moments in time in combination with the objective function of its MPC (model predictive control);

[0083] S404: Controlling the operating state of the vehicle based on the optimal control input of the current vehicle.

[0084] Under the zero-trust framework, the present invention further realizes the encryption and decryption of state information and the identity authentication of vehicles by combining the AES symmetric encryption algorithm and the RSA asymmetric encryption algorithm. First, the AES symmetric encryption algorithm provides efficient encryption and decryption speed, ensuring that the transmission encryption process of state information between vehicles is highly real-time and efficient; and the RSA asymmetric encryption algorithm is used to safely exchange keys or verify identities, ensuring the security of key transmission, and then ensuring the security of encrypted information. Therefore, the combination of the AES symmetric encryption algorithm and the RSA asymmetric encryption algorithm can improve the security of state information transmission in the vehicle queue. Secondly, the RSA asymmetric encryption algorithm ensures the secure transmission of symmetric keys, and only the receiver (with a private key) can decrypt and obtain the sender's symmetric key, and then decrypt the encrypted data. At the same time, the sender uses the private key to digitally sign the state information, and the receiver uses the sender's public key to verify the signature to ensure the integrity of the information and the identity of the sender, effectively preventing identity fraud and the injection of forged information, and realizing identity authentication between vehicles. Finally, the encrypted transmission process not only protects the confidentiality of the information, but also can be combined with hash functions and digital signatures to verify the integrity and authenticity of the status information, thereby ensuring that the status information has not been tampered with during transmission.

[0085] The vehicle of the present invention calculates and predicts the state information at the future time based on its own state information at the current time using a model prediction algorithm. On the one hand, by predicting the state information at the future time, the vehicle can adjust its control strategy in advance to cope with possible external interference or environmental changes, thereby improving the stability of vehicle queue control. On the other hand, when each vehicle can predict its own future state, the vehicle queue can work better with other vehicles and reduce control conflicts caused by mutual interference between vehicles.

[0086] The present invention predicts the expected state information of the vehicle at a future time based on the state information of the vehicle in front of it. On the one hand, by considering the state information of the vehicle in front, the current vehicle can predict the expected state at a future time, which helps to achieve coordinated control in the vehicle queue, making the driving of the entire queue smoother and more orderly, thereby improving the coordination of vehicle queue control. On the other hand, combined with the state information of the vehicle in front, the current vehicle can more accurately predict its own future state, thereby more accurately calculating the optimal control input, thereby improving the accuracy of vehicle queue control.

[0087] Based on the prediction of the state information and expected state information of the vehicle at the future moment, the present invention further combines the objective function of MPC (model predictive control) to calculate the optimal control input. First, the MPC algorithm takes into account the state and expected state at the future moment, so that the control input of the vehicle is more accurate, which can better meet the driving needs and the stability requirements of the vehicle queue, and further improve the accuracy of the vehicle queue control. Secondly, combined with the coordinated control and state prediction between vehicles, the optimal control input that makes the entire queue stable and well coordinated can be calculated according to the optimization objective function of MPC, which further improves the overall performance of the vehicle queue. Finally, the MPC algorithm can handle constraints and take into account the dynamic characteristics of the system, so that the vehicle has a stronger dynamic response capability when facing complex environments and uncertainties.

[0088] In practice, V2V communication is a short-range communication technology that enables vehicles to share their status information. In a convoy, the communication topology plays a key role, which determines how the information is exchanged between the member vehicles in the convoy. Fleet control usually uses a distributed controller, which calculates the control law based on limited neighbor status information. The topology determines the following strategy of the vehicles in the convoy.

[0089] The present invention models the communication topology model of a vehicle queue based on a directed graph; the vehicle queue includes a pilot vehicle and N following vehicles, and the state information between the N following vehicles is transmitted through a directed graph. Indicates that one vehicle is regarded as a vehicle node; V N ={b1,b2,…,b N} represents a set of N vehicle nodes; Represents the set of edges from vehicle node to vehicle node, directed edges (b i ,b j ) indicates that vehicle node i can obtain the status information of vehicle node j.

[0090] At the same time, this embodiment introduces four matrices, namely, an adjacency matrix, an in-degree matrix, a Laplace matrix and a traction matrix, to model the vehicle platoon communication topology.

[0091] 1) Adjacency Matrix

[0092]

[0093] where i,j∈{1,2,…,N}, b ij =1 means that vehicles i and j can communicate, that is, vehicle i can obtain the status information of vehicle j, b ij = 0 means that vehicles i and j cannot communicate, that is, vehicle i cannot obtain the status information of vehicle j, especially b ii =0, that is, not considering communication with oneself.

[0094] 2) In-degree matrix

[0095]

[0096] in It represents the degree of the Nth vehicle node, that is, the number of vehicle nodes whose status information the vehicle can receive.

[0097] 3) Laplacian Matrix

[0098] L = DB;

[0099] The state convergence of vehicles is affected by the inter-vehicle communication topology, and the properties of the graph can be studied by analyzing the eigenvalues ​​of the Laplace matrix.

[0100] 4) Traction Matrix

[0101]

[0102] where i∈{1,2,…,N}, P i ∈{0,1}, when P i =1, indicating that vehicle i and the pilot vehicle can communicate, that is, vehicle i can obtain the status information of the pilot vehicle. i =0, indicating that vehicle i and the pilot vehicle cannot communicate, that is, vehicle i cannot obtain the status information of the pilot vehicle.

[0103] Common communication topologies include front-to-back following (PF), bidirectional following (BF), leading vehicle following (LPF), bidirectional leader following (LBF), bidirectional leading vehicle following (TPF), and leading bidirectional leading vehicle following (LTPF). Using digital envelope technology, the status information is encrypted by the leading vehicle i and transmitted to the following vehicle j through the wireless network in a zero-trust environment. The following vehicle obtains the complete status information of the leading vehicle after verifying the consistency of the information through identity authentication and private key decryption.

[0104] In the specific implementation process, the present invention studies the vehicle platoon control problem on a straight road, so only the longitudinal dynamics are considered. Consider N+1 vehicles, where the lead vehicle is numbered 0 and the follower vehicles are numbered 1 to N. Therefore, the longitudinal vehicle dynamics model of each vehicle in the vehicle platoon is expressed as:

[0105]

[0106] F g,i =m i gf i ;

[0107]

[0108] In order to facilitate the analysis of the control system, feedback linearization technology is used, that is, the feedback strategy is expressed as:

[0109]

[0110] Where: p i (t) represents the position of vehicle i; v i (t) represents the speed of vehicle i; F T,i represents the driving force of vehicle i; m i represents the mass of vehicle i; F C,i represents the air resistance of vehicle i; F g,i represents the rolling resistance of vehicle i; τ represents the time lag constant of the longitudinal dynamic system; T i (t) represents the actual driving force torque of vehicle i; T des,i (t) represents the desired driving force torque of vehicle i; η i represents the mechanical efficiency of the transmission system of vehicle i; r i represents the tire radius of vehicle i; g represents the acceleration due to gravity; f i represents the rolling resistance coefficient of vehicle i; ρ represents the air density; A i represents the cross-sectional area of ​​vehicle i; u i represents the control input of vehicle i; C i (t) represents the air resistance coefficient of vehicle i; i∈{0,1,2,…,N} represents the vehicle number.

[0111] The vehicle state equation is:

[0112]

[0113] In the specific implementation process, in the ITS environment of intelligent connected vehicles, a successful authentication scheme is the key to system defense against intrusion. It is critical to ensure the confidentiality of state information to prevent unauthorized tampering or manipulation of vehicle systems. The zero-trust architecture requires strict identity authentication and data encryption for all vehicle nodes. Digital envelope technology is applied to the encrypted transmission of vehicle state information under this architecture, providing double protection. By verifying the hash value of the vehicle identity information and the information summary generated by the digital signature, it effectively prevents unauthorized nodes from obtaining information and leaking vehicle privacy, while ensuring the integrity of state information transmission. State information encryption is closely related to control decisions. The encrypted information can be used to formulate decision-making strategies and achieve the collaborative control goals of vehicle queues, thereby improving system reliability, safety and overall performance. Digital envelope technology provides strong security protection for vehicle communication and decision-making links.

[0114] Commonly used data encryption algorithms are divided into two categories: symmetric encryption and asymmetric encryption. Symmetric encryption is simple and fast, while asymmetric encryption is more secure and suitable for digital signatures. Digital envelope technology combines symmetric encryption for efficient data transmission, while using asymmetric encryption for key exchange and digital signatures. This fully utilizes the advantages of symmetric and asymmetric encryption algorithms, improves the security and integrity of information in the transmission of vehicle queue status information, and enhances the overall security of the system.

[0115] 1) AES symmetric encryption algorithm

[0116] AES has become a widely used encryption standard with its strong scalability, high security and good compatibility. It can effectively defend against attacks by selecting the appropriate key length according to security requirements and has been widely used in various systems and applications. AES is a block encryption algorithm that uses an iterative structure to perform multiple rounds of encryption and decryption operations on block data using the same key. The four core operations of AES are as follows:

[0117] ① Round key addition: In each round, the input data of the current round is bitwise XORed with the round key.

[0118] ② Byte replacement: Non-linear byte replacement is performed through a fixed byte replacement table.

[0119] ③ Row shift: Circularly shift each row of the input matrix to the left to achieve data replacement within the row, thereby increasing the diffusibility of the data encryption algorithm.

[0120] ④ Column confusion: Perform confusion operation on each column of the input matrix and replace bytes through a combination of addition and multiplication over a finite field.

[0121] These four layers of operations together constitute a round of AES operations. During the entire AES encryption process, this round of operations will be repeated multiple times, and each round uses a different round key to increase the complexity and security of the algorithm.

[0122] 2)RSA asymmetric encryption algorithm

[0123] RSA has become one of the most widely used encryption standards due to its asymmetric nature, easy key distribution, and digital signature capabilities. Based on the large prime number factorization problem, public and private keys are used for encryption and decryption respectively. Symmetric encryption cannot achieve signatures, while RSA can achieve signatures through private key encryption to ensure the security of information.

[0124] In the present invention, when status information is sent and received between vehicles, encryption and decryption of the status information and identity authentication of the vehicle are achieved by combining the AES symmetric encryption algorithm and the RSA asymmetric encryption algorithm.

[0125] Combination Figure 3 As shown, the present invention implements the sending and receiving of status information between vehicles through the following steps:

[0126] S301: The sender, i.e. the preceding vehicle, calculates its own state information through a hash function to generate a message digest; at the same time, the sender signs the message digest with its own private key to generate a digital signature;

[0127] S302: The sender randomly generates a symmetric key, and encrypts its own state information, digital signature and public key using the symmetric key to obtain data ciphertext;

[0128] S303: The sender encrypts its symmetric key using the public key of the receiver, i.e., the following vehicle, to obtain a symmetric key ciphertext;

[0129] S304: The sender sends the data ciphertext and the symmetric key ciphertext to the receiver;

[0130] S305: The receiver decrypts the symmetric key ciphertext with its own private key to obtain the sender's symmetric key; then decrypts the data ciphertext with the sender's symmetric key to obtain the sender's status information, digital signature and public key;

[0131] S306: The receiver decrypts the digital signature using the sender's public key to obtain the sender's message digest, which is used to verify the sender's identity.

[0132] S307: The receiver calculates the sender's state information through a hash function to generate a real-time information summary; then compares the real-time information summary with the sender's information summary to verify the integrity and authenticity of the state information.

[0133] S308: After the identity authentication and integrity and authenticity verification are passed, the receiver receives the status information of the sender.

[0134] Specific:

[0135] 1) The logic for generating public and private keys is as follows:

[0136] Randomly select two larger prime numbers p and q, and calculate the public modulus n = pq of the public key and private key;

[0137] Computes Euler's function modulo n

[0138] Randomly select a public key exponent e, e and Mutually prime, and satisfy: Calculate the private key exponent d, satisfying ed = 1 (mod r);

[0139] The calculation results show that the public key is (n,e) and the private key is (n,d);

[0140] 2) The logic of using public key encryption is as follows:

[0141] The information to be encrypted is represented as an integer m, and m is encrypted using the public key (n, e). The ciphertext c = (m e )mod n;

[0142] The mathematical model of the RSA encryption function is expressed as:

[0143] E(m,e,n)=(m e )mod n;

[0144] 3) The logic of decryption using the private key is as follows:

[0145] The encrypted ciphertext information is decrypted with the private key (n, d), and the plaintext information m = (c d )mod n;

[0146] The mathematical model of the RSA decryption function is:

[0147] D(c,d,n)=(c d )mod n.

[0148] In the specific implementation process, the present invention adopts model predictive control as the method of vehicle platoon control. The goal of vehicle platoon control is to keep the speed of the following vehicle consistent with the speed of the leading vehicle, while keeping the distance between adjacent vehicles in the platoon to the desired value. Here, a constant distance strategy is adopted, that is, d i-1,i =d des , where d i-1,irepresents the expected distance between the preceding vehicle i-1 and vehicle i. Since a constant distance strategy is adopted, d des is a constant greater than 0.

[0149] The vehicle queue control objectives are:

[0150]

[0151] According to the kinematic formula, the state of the vehicle queue can be expressed as follows:

[0152]

[0153] The state space established by the above formula is:

[0154]

[0155] Among them, the system state quantity The control quantity is u t =a i,t ,

[0156] By discretizing the above formula, we have:

[0157]

[0158] x t+1 =(I+TA1)x t +TB1u t ;

[0159] Therefore, the calculation formula of the model prediction algorithm of the present invention is expressed as:

[0160] x t+1 =Ax t +Bu t ;

[0161] y t+1 =Cx t+1 ;

[0162]

[0163] Where: x t+1 、x t Respectively represent the state information at time t+1 and time t; u t represents the control input at time t; y t+1 represents the system output; T represents the sampling time.

[0164] The future state of the vehicle predicted by the above formula is:

[0165]

[0166] The status output is:

[0167]

[0168] Therefore, the state information of the current vehicle at the next Np moments can be calculated by the following formula:

[0169]

[0170] Y i =φX i ;

[0171]

[0172] Where: X i It represents the state information of vehicle i at Np moments in the future; Np represents the prediction time domain; Nc represents the control time domain.

[0173] In the specific implementation process, the expected state information of the current vehicle in the next Np moments is calculated by the following formula:

[0174]

[0175] Where: X i,ref represents the expected state information of vehicle i in the next Np moments; r t+1 represents the expected state information of vehicle i at time t+1; p j represents the position of vehicle j; v j represents the speed of vehicle j; a j represents the acceleration of vehicle j; (ij)d des represents the expected distance between vehicle i and vehicle j; T represents the sampling time.

[0176] In the specific implementation process, if the vehicles in the queue exchange information using the LPF communication topology, the objective function of MPC in the zero-trust environment is expressed as:

[0177]

[0178] s.tu min ≤u i ≤u max ;

[0179] Where: Q, R and W are weight matrices; x t+j represents the state of vehicle i at time t+j; r F,t+j represents the expected state information of vehicle i at time t+j calculated based on the state information of the preceding vehicle; r L,t+j Tr represents the expected state information of vehicle i at time t+j calculated based on the state information of the pilot vehicle; F and Tr Lare the trustworthiness of the leading car and the pilot car respectively; u t+j Represents the control amount of vehicle i at time t+j.

[0180] In the objective optimization function, the first and second terms represent the weighted sum of state errors, which minimizes the state error between each following vehicle in the vehicle queue and its preceding vehicle and the leading vehicle. By introducing trust weights, the influence of information from vehicles with different trust levels on the control decision can be dynamically adjusted. The second term represents the weighted sum of inputs, which minimizes the change in control quantity, thereby reducing emergency braking and sudden acceleration and improving comfort. The final constraint ensures the upper and lower limits of control. The output of this optimization problem is a series of optimal control inputs u t ,u t+1 ,u t+NC-1 . Only the first input u t It is applied to the system as the current acceleration, and the whole process is repeated in the next time step t+1. In this way, the control effect and safety of vehicle platoons can be improved by dynamically adjusting the trust of vehicles in a zero-trust environment.

[0181] Combination Figure 4 As shown, the present invention further performs vehicle node trust modeling and converts the trust of vehicle node i into r The objective function of MPC is constructed as the trust weight. The calculation logic of trust is as follows:

[0182] Speed ​​and acceleration are basic parameters of vehicle movement, which directly reflect the vehicle's operating status and behavior patterns. The encrypted transmission of vehicle status information ensures its integrity and the security of the transmission process, but sometimes a vehicle may suddenly fail and behave abnormally (such as sudden acceleration and deceleration, frequent acceleration and deceleration), which may threaten the safety and stability of the entire fleet. By evaluating the status and behavior of the vehicle in real time, the trust of the vehicle is calculated, and the trust of vehicles with abnormal behavior is reduced to prevent them from affecting the normal operation of other vehicles and the fleet.

[0183] In a zero-trust environment, consider calculating the trust of vehicle nodes by analyzing the changes in vehicle speed and acceleration, and use a fuzzy membership function to calculate. Fuzzy membership function of speed change T r,a :

[0184]

[0185] T r,a represents the impact of speed change on trust weight, a represents vehicle acceleration, and a mid Expected acceleration, a min Minimum acceleration limit, a maxThe maximum acceleration limit, that is, the closer the speed change is to the expected acceleration, the greater the trust weight of the vehicle node.

[0186] Fuzzy membership function T of acceleration change r,β :

[0187]

[0188] Similarly, the impact of acceleration changes on trust weight can be expressed as T r,β , where β represents the speed of change of acceleration (also called jerk), β mid Desired jerk, β min Minimum jerk limit, β max The maximum jerk limit, that is, the closer the acceleration change is to the expected jerk, the greater the trust weight of the vehicle node.

[0189] The trust degree of vehicle node i is calculated as T r :

[0190] Tr=k1T r,a +k2T r,β ;

[0191] Among them, k1 and k2 are weight coefficients used to adjust the impact of speed change and acceleration change on the trust of vehicle nodes. The trust of vehicle nodes is dynamically adjusted according to the changes in vehicle node behavior. The changes in vehicle speed and acceleration are evaluated through fuzzy membership functions to update the trust of each vehicle node in real time, thereby reducing the impact of abnormal vehicles on the control of the platoon system.

[0192] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A vehicle queue control method based on encrypted transmission of state information in a zero-trust environment, characterized in that: include: S1: Construct the communication topology model of the vehicle platoon; S2: construct a longitudinal vehicle dynamics model for each vehicle in the vehicle platoon to calculate its own state information; In step S2, the longitudinal vehicle dynamics model of each vehicle in the vehicle queue is expressed as: F g,i =m i gf i ; Where: p i (t) represents the position of vehicle i; v i (t) represents the speed of vehicle i; F T,i represents the driving force of vehicle i; m i represents the mass of vehicle i; F C,i represents the air resistance of vehicle i; F g,i represents the rolling resistance of vehicle i; τ represents the time lag constant of the longitudinal dynamic system; T i (t) represents the actual driving force torque of vehicle i; T des,i (t) represents the desired driving force torque of vehicle i; η i represents the mechanical efficiency of the transmission system of vehicle i; r i represents the tire radius of vehicle i; g represents the acceleration due to gravity; f i represents the rolling resistance coefficient of vehicle i; ρ represents the air density; A i represents the cross-sectional area of ​​vehicle i; u i represents the control input of vehicle i; C i (t) represents the air resistance coefficient of vehicle i; i∈{0,1,2,…,N} represents the vehicle number; S3: Each following vehicle in the vehicle queue receives the status information sent by the vehicle in front of it; when the status information is sent and received between vehicles, it is encrypted, decrypted and verified through digital envelope technology; S4: Each following vehicle in the vehicle queue performs the following operations: S401: The current vehicle calculates and predicts the state information of the current vehicle Np moments in the future based on its own state information at the current moment using a model prediction algorithm; S402: The current vehicle inputs the state information of the vehicle ahead of it into the state prediction model to predict the expected state information of the current vehicle at Np moments in the future; S403: The current vehicle generates an optimal control input based on the state information and expected state information of the next Np moments in time in combination with the objective function of the MPC; S404: Controlling the operating state of the vehicle based on the optimal control input of the current vehicle.

2. The vehicle queue control method based on encrypted transmission of state information in a zero-trust environment according to claim 1, characterized in that: In step S1, a communication topology model is modeled for the vehicle queue based on a directed graph; The vehicle platoon consists of a leading vehicle and N following vehicles. The status information between the N following vehicles is transmitted through a directed graph Indicates that one vehicle is regarded as a vehicle node; V N ={b1,b2,…,b N } represents a set of N vehicle nodes; Represents the set of edges from vehicle node to vehicle node, directed edges (b i ,b j ) indicates that vehicle node i can obtain the status information of vehicle node j.

3. The vehicle queue control method based on encrypted transmission of state information in a zero-trust environment according to claim 1, characterized in that: In step S3, when status information is sent and received between vehicles, the encryption and decryption of the status information and the identity authentication of the vehicle are achieved by combining the AES symmetric encryption algorithm and the RSA asymmetric encryption algorithm.

4. The vehicle queue control method based on encrypted transmission of state information in a zero-trust environment as claimed in claim 3 is characterized in that: In step S3, the sending and receiving of status information between vehicles is achieved through the following steps: S301: The sender, i.e. the preceding vehicle, calculates its own state information through a hash function to generate a message digest; at the same time, the sender signs the message digest with its own private key to generate a digital signature; S302: The sender randomly generates a symmetric key, and encrypts its own state information, digital signature and public key using the symmetric key to obtain data ciphertext; S303: The sender encrypts its symmetric key using the public key of the receiver, i.e., the following vehicle, to obtain a symmetric key ciphertext; S304: The sender sends the data ciphertext and the symmetric key ciphertext to the receiver; S305: The receiver decrypts the symmetric key ciphertext with its own private key to obtain the sender's symmetric key; then decrypts the data ciphertext with the sender's symmetric key to obtain the sender's status information, digital signature and public key; S306: The receiver decrypts the digital signature using the sender's public key to obtain the sender's message digest, which is used to verify the sender's identity. S307: The receiver calculates the state information of the sender through a hash function to generate a real-time information summary; then compares the real-time information summary with the information summary of the sender to verify the integrity and authenticity of the state information; S308: After the identity authentication and integrity and authenticity verification are passed, the receiver receives the status information of the sender.

5. The vehicle queue control method based on encrypted transmission of state information in a zero-trust environment according to claim 4, characterized in that: 1) The logic for generating public and private keys is as follows: Randomly select two different prime numbers p and q, and calculate the public modulus n = pq of the public key and private key; Computes Euler's function modulo n Randomly select a public key exponent e, e and Mutually prime, and satisfy: Calculate the private key exponent d, satisfying ed = 1 (mod r); The calculation results show that the public key is (n,e) and the private key is (n,d); 2) The logic of using public key encryption is as follows: The information to be encrypted is represented as an integer m, and m is encrypted using the public key (n, e). The ciphertext c = (m e )mod n; 3) The logic of decryption using the private key is as follows: The encrypted ciphertext information is decrypted with the private key (n, d), and the plaintext information m = (c d )modn.

6. The vehicle queue control method based on encrypted transmission of state information in a zero-trust environment according to claim 1, characterized in that: In step S401, the calculation formula of the model prediction algorithm is expressed as: x t+1 =Ax t +Bu t 4 y t+1 =Cx t+1 ; Where: x t+1 、x t Respectively represent the state information at time t+1 and time t; u t represents the control input at time t; y t+1 represents the system output; T represents the sampling time.

7. The vehicle queue control method based on encrypted transmission of state information in a zero-trust environment according to claim 6, characterized in that: In step S501, the state information of the current vehicle at the next Np moments is calculated by the following formula; Y i =φX i ; Where: X i It represents the state information of vehicle i at Np moments in the future; Np represents the prediction time domain; Nc represents the control time domain.

8. The vehicle queue control method based on encrypted transmission of state information in a zero-trust environment according to claim 1, characterized in that: In step S402, the expected state information of the current vehicle at Np moments in the future is calculated by the following formula: Where: X i,ref represents the expected state information of vehicle i in the next Np moments; r t+1 represents the expected state information of vehicle i at time t+1; p j represents the position of vehicle j; v j represents the speed of vehicle j; a j represents the acceleration of vehicle j; (ij)d des represents the expected distance between vehicle i and vehicle j; T represents the sampling time.

9. The vehicle queue control method based on encrypted transmission of state information in a zero-trust environment according to claim 1, characterized in that: In step S503, if the vehicles in the queue exchange information using the LPF communication topology, the objective function of MPC in the zero-trust environment is expressed as: s.t u min ≤u i ≤u max ; Where: Q, R and W are weight matrices; x t+j represents the state of vehicle i at time t+j; r F,t+j represents the expected state information of vehicle i at time t+j calculated based on the state information of the preceding vehicle; r L,t+j Tr represents the expected state information of vehicle t at time t+j calculated based on the state information of the pilot vehicle; F and Tr L are the trustworthiness of the leading car and the pilot car respectively; u t+j Represents the control amount of vehicle i at time t+j.

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