323 results about "Platoon" patented technology

The invention relates to a formation driving coordination method based on vehicle-road cooperation, and the method comprises the steps: a road side unit RSU is used for releasing a traffic constraint weight value containing the information of passing restriction, path guidance, risk distribution and the like into a formation driving region, and the weight value forms a traffic environment guidance domain with a potential gradient at a vehicle end; the vehicle autonomously completes formation aggregation and position arrangement based on the own state and the weight change trend in the local sensing environment; after the formation is completed, the vehicles dynamically form a multi-level influence circle structure according to relative positions, historical cooperative behaviors and influence factors provided by the RSU, a current leading vehicle exists in each layer of influence circle, the leading vehicle has local scheduling guidance capability, and when the state of the leading vehicle is unstable or the behavior is abnormal, the control right is transferred to the auxiliary control vehicle; according to the method, the traffic environment guidance domain with the potential gradient is constructed through the traffic constraint weight value issued by the RSU, and the vehicles can autonomously perform formation aggregation and array sorting based on the local weight change trend.

The invention relates to a neural network-based adaptive control method for a vehicle formation system. Compared with the prior art, the method solves the defects that a complete target trajectory cannot be known in advance due to the influence of an actual environment and the control direction is unknown due to the unstable state of a vehicle engine. The method comprises the following steps: acquiring tracking data of a vehicle formation system; establishing a dynamic model of the vehicle formation system; establishing an error equation; designing a nusturb type function; designing a radial basis function neural network architecture; designing a GRNN training set and an evaluation index for trajectory reconstruction; designing an event trigger function of an actual controller of the system; carrying out self-adaptive control on the vehicle formation; and controlling the vehicle formation with an unknown target trajectory and an unknown control direction. According to the method, a neural network (NN)-based adaptive control architecture is adopted for a vehicle formation system (VPSs), a real-time trajectory can be predicted online by using a GRNN based on historical data of a target trajectory, and an unknown nonlinear term in the system is compensated, so that the position of a vehicle tracks the predicted target trajectory.

The invention relates to the technical field of intelligent traffic control, and discloses a self-balancing two-wheeled vehicle queue formation control system which comprises a cloud scheduling platform, a formation control unit and a vehicle end execution unit. And the cloud scheduling platform generates a global avoidance path based on the accumulated gravitational potential energy change and the pavement flatness coefficient, and executes dynamic position complementing. The formation control unit constructs a communication topology and executes formation switching based on environmental perception; the cooperative control module adopts an attitude position coupling algorithm, an expected acceleration is corrected through a gain function attenuating along with an attitude angle index, and the attitude balance is maintained preferentially when a tracking error is eliminated. The vehicle end execution unit is provided with an environment sensing and composite actuating module, and a hub motor and a control moment gyroscope are used for responding to longitudinal driving and transverse balance instructions respectively, so that dynamic decoupling is achieved. According to the invention, the control conflict between two-wheeled vehicle formation trajectory tracking and attitude stability is effectively solved, and the driving safety of the system is improved.

The embodiment of the invention relates to the technical field of intelligent driving, and discloses a motorcade passing control method, device and equipment based on global planning, and the method comprises the steps: obtaining the driving related information of vehicles in a motorcade; the motorcade comprises a plurality of vehicles, and the driving related information comprises at least one of road condition information, vehicle driving information and map navigation information; according to the driving related information of at least one vehicle in the motorcade, performing global planning by taking the vehicles in the motorcade not to fall behind or the fallen vehicles to get up to the motorcade as a control target, and determining a target state of a target vehicle in the motorcade; the target state indicates driving information needing to be executed by a target vehicle, and the target vehicle is any vehicle in a motorcade; and sending the target state of the target vehicle to the target vehicle to control the target vehicle to be switched from the current state to the target state. By applying the technical scheme of the invention, the current problems that the formation vehicles are easy to fall behind and are difficult to recover because of being cut off in the complex urban road can be solved.

The invention discloses a vehicle queue control method based on an improved gap strategy and a depth deterministic strategy gradient algorithm, and the method comprises the following steps: building a vehicle-to-vehicle gap strategy considering the difference between the speed of a front vehicle and the speed of a vehicle, constructing an MPC frame through the strategy, and adding the minimum safety distance between vehicles into a constraint condition of the MPC frame; adjusting parameters in the MPC framework in real time by using a trained depth deterministic strategy gradient algorithm model to obtain an optimal parameter combination in each state, and then substituting the optimal parameter combination into the MPC framework to calculate and obtain an optimal control sequence at each moment; and finally, longitudinally controlling the vehicles in the vehicle queue according to the specified control quantity in the optimal control sequence of each control period. Compared with a traditional fixed parameter MPC method, the method has the advantages that the motorcade gap error can be remarkably reduced, the stability and safety of motorcade longitudinal control are improved, and efficient and safe automatic driving motorcade management is achieved.

The invention provides a heterogeneous motorcade prediction control method based on a path information protection mechanism, and relates to the technical field of network connection unmanned vehicle cooperative control and intelligent traffic. Firstly, a path information protection mechanism is provided for preventing an alien invading vehicle from tracking a motorcade and ensuring the safety of motorcade path information. In addition, a predictor model is established to improve the estimation performance of the adaptive law, and a variable gain nonlinear expansion state observer is designed to approach and compensate the total disturbance. Then, by introducing a variable-gain hyperbolic tangent tracking differentiator, the problem of'complexity explosion 'caused by repeated derivation of a virtual controller in a backstepping method is avoided; and finally, a distributed prediction cooperative controller is constructed, and stable and safe driving of the whole heterogeneous network connection fleet is ensured.

The invention discloses an intelligent electric vehicle queue optimization method based on congestion prediction and DRL. The method comprises the following steps: (1) modeling a queue position optimization problem of an electric vehicle queue into a mathematical model which takes energy balance as a target and is constrained by a traffic environment; and (2) solving the mathematical model in the step (1) by adopting a DRL method based on a TRPO algorithm to obtain an optimal queue adjustment strategy of the electric vehicle queue. In the step (2), congestion state information in an external traffic environment is obtained through an LSTM traffic prediction and FCM method, and the congestion state information, the remaining electric quantity of each vehicle in the motorcade and the accumulated driving distance are jointly used as state input of a TRPO algorithm; according to the TRPO algorithm, a strategy network and a value network are adopted to respectively obtain a strategy for adjusting the queue position, and the return expectation in the current state is evaluated. The TRPO algorithm dynamically adjusts the updating step length of the strategy through the KL divergence updated by the constraint strategy, and guarantees the stability and convergence in the strategy updating process.

The invention discloses an unmanned vehicle queue control method based on physical information reinforcement learning, and the method comprises the steps: building a reinforcement learning environment of an unmanned vehicle queue, and enabling a queue following vehicle to plan the acceleration according to the acceleration change of a leading vehicle; state information of each vehicle is obtained, a TD3 algorithm is used for carrying out training iteration on the model, in the training iteration process, the expected acceleration obtained through calculation of the CACC model is embedded into a loss function of a strategy network Actor to serve as physical information constraint, and a strategy is guided to generate an action conforming to vehicle dynamics; and after training iteration is finished, deploying a control strategy of the strategy network Actor in an unmanned motorcade simulation environment to realize cooperative control of the motorcade. According to the method, physical information constraints are introduced, so that the model training speed is increased, the stability and interpretability of a reinforcement learning control strategy are improved, and the queue operation efficiency and stability are enhanced.

The invention relates to the technical field of vehicle control, in particular to a vehicle queue optimal control method and system based on a function deviation triggering mechanism, and the method comprises the steps: 1, constructing a piloting dynamics model of a piloting vehicle and a following dynamics model of a following vehicle; 2, defining a local tracking error of the ith following vehicle, defining control input of the following dynamical model as a function of the local tracking error according to an optimal control target, further defining an optimal value function, and determining optimal control input according to the optimal value function; 3, obtaining an optimal value function estimation value through an adaptive commentator neural network; 4, designing a function deviation triggering mechanism according to the optimal value function estimation value and the local tracking error so as to dynamically adjust the optimal control input of the following dynamical model; and 5, setting a communication association relationship and an initial state of the leading vehicle and the following vehicle to obtain a queue control result. According to the invention, efficient cooperative driving in a complex dynamic environment is realized.

The invention provides a multi-pilot vehicle formation control method based on formation graph connectivity judgment, and the method comprises the steps: determining the motion state of each target vehicle in a vehicle formation when the formation operation mode of the vehicle formation is a single-pilot mode; if it is determined that a target vehicle in the vehicle formation is abnormal according to the motion state, splitting the vehicle formation into a plurality of sub-formations in a multi-navigation mode; and when each sub-formation satisfies a preset formation fusion condition, recombining the sub-formations into a vehicle formation in the single navigation mode. The method is used for overcoming the defect that in the prior art, the whole formation loses efficacy when the vehicles cannot continue to follow stably in the vehicle formation in a single-pilot mode.

The invention relates to the technical field of intelligent vehicles, in particular to an intelligent vehicle automatic driving formation obstacle avoidance control method and device, and the method comprises the steps: constructing an intelligent vehicle formation controller according to the destination data and obstacle data of a target intelligent vehicle formation, so as to control the target intelligent vehicle formation to move towards the destination; establishing an intelligent vehicle formation space-time transformation strategy based on the queue expansion coefficient according to the queue expansion coefficient of the target intelligent vehicle formation and the driving safety boundary; performing dynamic optimization on the intelligent vehicle formation controller by adopting a Levy flight random search logic based on a cuckoo algorithm to obtain an optimized intelligent vehicle formation controller; and controlling the target intelligent vehicle formation to avoid obstacles by using the optimized intelligent vehicle formation controller and the intelligent vehicle formation space-time transformation strategy based on the queue expansion coefficient. Therefore, the problems that when intelligent vehicle formation obstacle avoidance is carried out through a traditional artificial potential field method, local minimum value and obstacle avoidance track jittering are likely to happen, and complex dynamic environments cannot be rapidly adapted are solved.

The invention discloses a fleet safety reinforcement learning robust formation control method fusing deep heterogeneous features. The method comprises the following steps: step 1, establishing a heterogeneous motorcade dynamic model; step 2, isomerism feature extraction based on LSTM (Long Short Term Memory); step 3, designing a Lyapunov safety reinforcement learning vehicle following strategy; and step 4, carrying out Lyapunov-PPO training optimization and convergence guarantee. According to the invention, the dynamic time-varying features of each vehicle in the heterogeneous motorcade are accurately extracted and tracked through the LSTM network, and adaptive control of different types of vehicles is realized; a Lyapunov safety constraint mechanism is introduced, collision-free guarantee is embedded in the reinforcement learning decision process, and the driving safety is improved; robustness of the system to external disturbance and internal dynamic change is enhanced, and it is ensured that a fleet can still keep stable formation in a complex driving environment.

The invention discloses an intelligent truck formation transportation control system based on cloud side-end cooperation, and the system achieves the cooperative control through a three-stage architecture of a cloud large model cognition center, an edge-side intelligent control center and an intelligent terminal with a body. Comprising a cloud large model cognition center, an edge side intelligent control center and an emergency response module driven by integrated fuzzy logic of an intelligent terminal system with a body. The invention also discloses an intelligent truck formation transportation control method based on cloud side-end cooperation, and the method comprises the steps: firstly deploying a large model at a cloud end, achieving the domain adaptation through the LoRA fine tuning, analyzing task semantics, and outputting feature parameters; secondly, a vehicle end needs to integrate a multi-mode sensor and a fuzzy controller, a sensing-control local closed loop is established, and it is guaranteed that when communication is interrupted, local safety takeover is achieved through the fuzzy controller; and finally, cloud-edge-end strategy alignment is realized, and autonomous operation in a network disconnection scene is supported. The problems of cognitive decision separation, high communication dependency, insufficient security redundancy and the like in a traditional method are solved.

The invention relates to an airport snow sweeper cooperative formation control method, electronic equipment and a medium, and the method comprises the steps: building a vehicle transverse and longitudinal coupling kinematic model in an airport snow removal environment based on environmental parameters, the vehicle transverse and longitudinal coupling kinematic model comprises transverse and longitudinal physical quantity cross terms, enabling the longitudinal speed to affect the transverse posture change, and enabling the longitudinal speed to affect the transverse posture change; the transverse path characteristics and the control input restrict the longitudinal motion; constructing a formation constraint model based on the vehicle transverse and longitudinal coupling kinematics model; constructing a distributed communication topology model based on a directed graph and an adjacent matrix; constructing a single vehicle local optimization control problem model; and iteratively solving the single vehicle local optimization control problem model based on a path guiding type distributed model predictive control algorithm to obtain the optimal control input of each vehicle. According to the method, control input can be optimized while accurate path tracking of the snow sweeper is ensured, energy consumption and cost are reduced, and collaboration, safety and efficiency of snow removal operation of an airport can be improved.

Platoons of vehicles are operated remotely by a teleoperator. A lead vehicle of a platoon establishes communications with a teleoperator station, which receives drive state data from the lead vehicle and generates reference instructions for the lead vehicle. When a lead vehicle is under control of a teleoperator, and a follower vehicle joins a platoon with the lead vehicle, the follower vehicle receives drive state data from the lead vehicle and reference instructions for the lead vehicle from the teleoperator station. The follower vehicle captures data regarding its own drive state, and generates instructions based on the drive state data of the lead vehicle and the reference instructions for the lead vehicle. Drive state data of a lead vehicle of a platoon may be estimated, augmented or refined for accuracy based on drive state data of other vehicles of the platoon.

The invention discloses an automobile queue control method based on pipeline distributed model predictive control, and belongs to the field of intelligent automobiles and intelligent traffic. The method comprises the following steps: constructing a vehicle discrete dynamic model; establishing a queue cooperative control framework through vehicle-vehicle communication; introducing a wheel slip rate into the queue performance index function, and setting a longitudinal slip rate as a constraint; on the basis of nominal model predictive control, a nominal optimal control input sequence and an optimal state track are generated through decision making; and then the state of the disturbed vehicle is corrected in real time through an auxiliary control law, so that the disturbed vehicle is restrained near the optimal track. In the queue car-following control process, an interference suppression mechanism and tire nonlinear modeling are introduced into the system, and the limitation that the node vehicle stability and the full-queue control performance are not sufficiently considered in a traditional method is broken through. Even under the limiting working conditions of high-speed driving, low adhesion and the like, the stability and the good all-working-condition control performance of the automobile queue can be guaranteed.

The invention provides a method for determining a motorcade driving strategy and electronic equipment, and relates to the technical field of path planning, and the method comprises the steps: firstly, determining a comprehensive operation index of a motorcade based on cooperation data and a basic operation index of the motorcade; secondly, the collaborative data and interaction data and prior data of the motorcade are fused to obtain fused data, the interaction data are data of the current running state of the vehicle and the road condition of the environment where the vehicle is located, and the prior data are used for representing historical driving data matched with the current scene; and finally, based on the fusion data, the comprehensive operation index and the path planning data of the motorcade, determining a target driving strategy of the motorcade, the path planning data being a constraint for supporting path planning. The technical problem that in the prior art, cooperative requirements of distance control, path conflict avoidance and the like of the motorcade in an off-road environment are not considered, so that the overall passing efficiency and safety of the motorcade are insufficient is solved.

The invention provides a motorcade active-disturbance-rejection control method and system with a variable time interval strategy. The method comprises the following steps: acquiring state information of each vehicle in a vehicle queue system; the leader vehicle and the following vehicle carry out information transmission; under the condition of considering external disturbance, a vehicle dynamics model referring to the Newton second law is established, and a variable time interval strategy is constructed; compensating unmodeled dynamic and external disturbances in the vehicle dynamics model by using a time-varying gain expansion state observer in an active disturbance rejection control technology, and constructing a virtual controller and an actual controller based on a backstepping method; and according to the constructed virtual controller and the actual controller, realizing position and speed synchronization of the following vehicle and the leading vehicle in the queue under the condition of considering unmodeled dynamics and external disturbance. According to the method, the non-smooth problem at the saturation threshold is solved, the fluctuation influence can be effectively reduced, the safety is guaranteed, and the passing efficiency is improved; and the problem of gain attenuation is solved while the high gain characteristic is reserved.

The invention provides a forward collision risk processing method and device, equipment and a medium, and relates to the technical field of auxiliary driving, and the method comprises the steps: forming a forward target sequence through synchronously obtaining the speeds, accelerations and vehicle distances of a vehicle and a plurality of front vehicles, and constructing a relative speed, relative distance and longitudinal acceleration time sequence according to a time dimension; calculating nominal collision time and a time margin based on the motion relation between the two vehicles, and introducing a motorcade compression index and a deceleration wave intensity index to correct the nominal collision time so as to obtain effective collision time; collision hazards are quantified according to a collision relative speed, a compression index and a deceleration wave intensity index, a controllability score is constructed in combination with effective collision time and a time margin, then a severity score and the controllability score are mapped into discrete risk levels, and differential active safety measures such as transverse avoidance are triggered according to different risk levels. The method can simultaneously consider the longitudinal compression trend of the motorcade and the deceleration wave cumulative effect to trigger the evasion strategy.

According to the method for evaluating the urban trunk traffic signal control effect based on the single-vehicle theory provided by the invention, the obtained gate data is used for deducing to obtain a time theoretical value sequence for all vehicles on a certain lane to arrive at a downstream intersection within a certain time period needing phase positioning; and further obtaining a green light phase arrival proportion and an upstream intersection signal control phase evaluation index. According to the evaluation method provided by the invention, the Platon Ratio index calculation model which is more suitable for the actual situation and is based on the bicycle identification data is provided; the theoretical arrival time of the vehicle arriving at the parking line at the intersection is deduced by using the vehicle matching characteristics of the single vehicle identification data, and the new model has practical guiding significance; the TAD trunk line coordination evaluation index based on the single vehicle identification data is provided, the PCD index for evaluating the trunk line coordination control effect provided in the prior art and a calculation method are improved, the limitation of the PCD index in domestic application is improved, and the method has more practical guiding significance in China.

The invention relates to the technical field of vehicle control, in particular to a motorcade collaborative automatic driving method and system based on distributed consensus decision. The invention provides a motorcade collaborative automatic driving method based on a distributed consensus decision, and the method comprises the steps: carrying out the dynamic networking and data broadcasting, enabling vehicles in a motorcade to automatically discover and carry out the networking through a V2V communication module, and forming a logic cluster; triggering a cooperative task and generating a proposal, and when a local system of any vehicle in the cluster detects a scene needing a motorcade cooperative response, the vehicle serves as a proposal node and generates a cooperative action proposal based on the vehicle and received shared information; and based on BFT distributed consensus decision, vehicles in the cluster independently verify security and rationality to achieve a consensus process after receiving a proposal. After all the vehicles reach the consensus, a time synchronization mechanism is kept, and it is ensured that all the vehicles execute the instructions on the same standard. The technical problem that in the prior art, an RSU-based motorcade collaborative automatic driving method is needed is solved.

The invention provides a wireless communication method based on a cellular Internet of Vehicles technology, a communication terminal and a vehicle, and relates to the technical field of vehicle communication. The method comprises the following steps: adding a first vehicle-mounted communication terminal and a second vehicle-mounted communication terminal of a target following vehicle in preset formation vehicles to a communication network of a target multicast group, and generating a target multicast routing table, so that a leading vehicle adopts a multicast communication mode, the multicast data can be sent to the second vehicle-mounted communication terminal of each target following vehicle in a targeted manner through the first vehicle-mounted communication terminal, so that the data is prevented from being sent to other vehicles. Due to the adoption of the multicast communication mode, the vehicle-mounted communication terminal of any vehicle in the target multicast group sends the multicast data, and the vehicle-mounted communication terminals of the other vehicles can receive the multicast data, so that unnecessary repeated transmission is avoided, the risk of network congestion is remarkably reduced, the information spreading efficiency is improved, and the user experience is improved. And meanwhile, all target following vehicles can receive the latest instruction in time, and synchronization and stability of a motorcade are maintained.

The invention discloses a networked vehicle queue safe cruise method for coping with hybrid network attacks, the vehicle queue comprises a head vehicle and a following vehicle, the following vehicle collects the running state data of the vehicle, obtains the running state data of other vehicles through a communication network, continuously detects the state of the communication network, and sends the detected state to the vehicle queue. Detecting whether a network attack is currently suffered or not and the type of the suffered attack, and calculating the minimum characteristic value of the topological matrix in a normal communication state, a spoofing attack state or a replay attack state according to the identified attack type; and then solving the constructed linear matrix inequality which enables the vehicle closed-loop system to be stable to obtain a feedback gain, and generating a control input signal according to the feedback gain obtained by calculation to realize accurate control of the vehicle. The feedback gain is dynamically adjusted according to the real-time state of the vehicle, so that the safety cruise control of the vehicle queue system is more effectively realized.

Platoons of vehicles are operated remotely by a teleoperator. A lead vehicle of a platoon establishes communications with a teleoperator station, which receives drive state data from the lead vehicle and generates reference instructions for the lead vehicle. When a lead vehicle is under control of a teleoperator, and a follower vehicle joins a platoon with the lead vehicle, the follower vehicle receives drive state data from the lead vehicle and reference instructions for the lead vehicle from the teleoperator station. The follower vehicle captures data regarding its own drive state, and generates instructions based on the drive state data of the lead vehicle and the reference instructions for the lead vehicle. Drive state data of a lead vehicle of a platoon may be estimated, augmented or refined for accuracy based on drive state data of other vehicles of the platoon.

The application belongs to the technical field of distributed vehicle platoon control, and provides a vehicle platoon control method and system with a time-varying gain extended state observer, which comprises establishing a vehicle dynamics model; external disturbances are estimated in real time based on the time-varying gain extended state observer, and the initial differential peak value phenomenon is effectively inhibited; the stability analysis of the observer system is discussed in different cases, and it is verified that the time-varying gain extended state observer has a more rigorous and simple stability proof process; an extended state observer for compensating external disturbances and unknown nonlinear terms of the model is designed, a virtual controller and an actual controller are constructed by using the backstepping method based on the extended state observer and the fixed-time stability theory; by designing a fast fixed-time backstepping control method, the system state converges to the expected signal in a fixed time, and the system obtains good tracking performance.

The invention provides an automatic driving formation hardware-in-the-loop simulation platform based on a satellite position simulation platform and a medium, and relates to the technical field of automatic driving and car networking simulation. The platform comprises a satellite position simulation module, a traffic simulation platform, a hardware verification platform, a simulation bus and a clock synchronization module. The satellite position simulation module generates satellite data with a timestamp; the traffic simulation platform constructs a formation driving scene and operates a formation control algorithm; the hardware verification platform comprises a real phased-array antenna module and a PC5 communication device. The modules exchange data through a simulation bus, and the clock synchronization module unifies the time reference to form a closed-loop verification environment of simulation hardware simulation. The problems that space-based data and ground simulation are separated and real communication characteristics are difficult to quantitatively evaluate in the prior art are solved, the satellite-ground cooperation and formation control strategy can be efficiently and reliably verified in a laboratory environment, and the real vehicle test cost and risk are remarkably reduced.

This invention discloses a system and method for lane-changing and clustering of connected autonomous driving fleets in bottleneck areas. It generates corresponding lane-changing and clustering schemes based on the type of vehicle ahead, calculates the lateral offset of the vehicle ahead, and utilizes a cooperative search intelligent optimization algorithm to improve the accuracy of the lateral offset. The impact values ​​of collision factors and the safety thresholds of collision impact factors are input into a collision risk assessment model to determine whether there is a collision risk, thereby controlling the connected autonomous driving fleet. Finally, based on an information matrix, the types of lagging vehicles in the target lane are discussed to complete the lane-changing and clustering process. This invention can improve traffic efficiency in bottleneck areas while ensuring driving safety.

A method for automated vehicle driving control in a platoon includes obtaining first operational parameters of a first vehicle, including at least a first measured braking performance of the first vehicle, obtaining second operational parameters of a second vehicle, including at least a first measured braking performance of the second vehicle, determining a platoon sequence according to the operational parameters by assigning whichever of the first vehicle and the second vehicle that has a lesser measured braking performance as a lead vehicle in the platoon sequence, and assigning the other of the first vehicle and the second vehicle as a follower vehicle in the platoon sequence, initiating a platoon sequence driving route, and controlling automated driving of the follower vehicle according to tracking of the lead vehicle, wherein the automated driving includes automated steering control, automated acceleration and automated braking based on tracked movement of the lead vehicle.

The application discloses a kind of anti-network attack's multi-vehicle safety platoon control method and system, the method first establishes multi-vehicle queue dynamics model;Second, design fuzzy state observer to estimate vehicle state and unknown nonlinear dynamics;Then, introduce funnel function to build time-varying performance boundary, and the double constraints of vehicle anti-collision and keep communication are converted into funnel constraints of spacing tracking error;Then, based on backstepping method and funnel performance error, design virtual controller and adaptive law, and obtain adaptive safety controller to obtain control input;Finally, through Lyapunov stability analysis, the tolerance threshold condition that system can still maintain final consistent bounded stable after suffering replay attack is quantitatively obtained.The application simplifies design through funnel control, avoids complex barrier lyapunov function, and for the first time clearly provides quantified anti-interference ability to replay attack, enhances the network security and practicality of multi-vehicle queue system.