Distributed event trigger control method for intelligent automobile aiming at attacked sensor
By employing a distributed event-triggered control method, the problem of communication resource waste and security issues caused by sensor attacks on intelligent vehicles is solved, achieving resource-saving and safe and reliable motion control.
Patent Information
- Application Number
- CN202511194031.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-25
- Publication Date
- 2025-12-12
AI Technical Summary
Sensors in smart cars are vulnerable to attacks, which can cause controller modules to receive inaccurate measurement data, waste communication resources, and potentially cause network congestion, thus affecting vehicle safety and reliability.
A distributed event-triggered control method is adopted. Through an initialization module, a state detection module, a distributed event triggering module, a data caching module, and a controller module, a dynamic adaptive threshold function and a distributed event triggering condition are designed to determine whether the sensor state is updated to the controller, avoiding unnecessary communication. The front wheel steering angle control command is calculated using an intelligent vehicle time-delay control model and a state feedback time-delay controller.
It effectively saves communication resources, improves the safety and reliability of intelligent vehicles in motion control, reduces performance loss caused by sensor attacks, and avoids communication congestion.
Smart Images
Figure CN121125188A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of intelligent vehicle control, in particular to a distributed event-triggered control method for an intelligent vehicle under sensor attack. BACKGROUND
[0002] With the increasing interaction between the vehicle communication network and the intelligent vehicle, the network security threat faced by the intelligent vehicle is also significantly increasing. Malicious attackers can easily initiate attack signals against sensors between the vehicle sensors and the controller module, which will cause the controller module of the intelligent vehicle to receive inaccurate sensor measurement states, and in severe cases, cause the intelligent vehicle to lose control, and even threaten life and property safety. In addition, the communication bandwidth of the vehicle communication network is limited, which means that the communication resources must be limited. The traditional time-triggered intelligent vehicle control system updates and transmits the sensor measurement state to the controller module at each sampling time, which will waste a large amount of unnecessary communication resources, and in severe cases, will cause the communication network between the vehicle sensors and the controller module to be congested, so that the sensor measurement state of the intelligent vehicle cannot be normally transmitted to the controller module, and even cause a traffic accident. The sensor measurement state includes lateral speed, yaw angular velocity, lateral error, heading angle error, and speed. The vehicle sensor is composed of a plurality of sensors. SUMMARY
[0003] To solve the above problems existing in the prior art, the purpose of the present application is to provide a distributed event-triggered control method for an intelligent vehicle under sensor attack, so as to effectively save the communication resources of the vehicle communication network and reduce the tracking performance loss of the intelligent vehicle caused by the attack on the sensor, and make the intelligent vehicle run safely and reliably.
[0004] In order to achieve the above-mentioned purpose, the technical scheme of the present application is as follows: The distributed event-triggered control method for an intelligent vehicle under sensor attack uses an intelligent vehicle distributed event-triggered control system for control, which includes an initialization module, a state and signal detection module when the sensor is attacked, a distributed event-triggered module, a data cache module, a controller module, and an actuator module.
[0005] The initialization module is used to check whether the sensor, the planning module, and the actuator module signal transceiver are normal; check whether the state and signal detection module and the data cache module are normally running when the sensor is attacked; check whether the loading of the reference path information is normal, the reference path information including the reference position, the reference road curvature, and the reference heading angle; load the parameters of the intelligent vehicle time delay control model; load the parameters of the distributed event-triggered module; load the parameters of the intelligent vehicle state feedback time delay controller.
[0006] The state and signal detection module determines the security state of the vehicle communication network and obtains the signal of the malicious attacker attacking the sensor by monitoring whether the data stream transmission of the vehicle communication network is distorted in real time. The security state is an attack state or a non-attack state. The distributed event triggering module is composed of a distributed event triggering condition including a plurality of triggering sub-conditions. The distributed event triggering condition is designed according to a dynamic adaptive threshold function and a state signal of the intelligent vehicle time delay control model. The state signal of the intelligent vehicle time delay control model is designed according to the sensor measurement state. The dynamic adaptive threshold function is designed according to the security state of the vehicle communication network and the state signal of the intelligent vehicle time delay control model. The distributed event triggering module determines whether the state signal of the intelligent vehicle time delay control model is updated into the controller module. The elements of the state signal of the intelligent vehicle time delay control model include lateral velocity, yaw angular velocity, lateral error and heading angle error. The lateral error refers to the difference between the current position of the intelligent vehicle and the reference position. The heading angle error refers to the difference between the current heading angle of the intelligent vehicle and the reference heading angle. The current state information of the intelligent vehicle includes position information, heading angle, longitudinal velocity, lateral velocity and yaw angular velocity. The data buffer module is used to buffer the state signal of the intelligent vehicle time delay control model triggered at the last time when the distributed event triggering condition is not met.
[0007] The intelligent vehicle time delay control model is obtained by mathematical calculation and modeling in the form of mathematical equation based on the current state information of the intelligent vehicle and the reference path information. The controller module is composed of an intelligent vehicle state feedback time delay controller. The intelligent vehicle state feedback time delay controller is designed based on the intelligent vehicle time delay control model. The intelligent vehicle state feedback time delay controller uses the state signal of the intelligent vehicle time delay control model to solve and obtain the front wheel steering angle control instruction.
[0008] The distributed event triggering control method includes the following steps: Step 1: Initialization The initialization module checks whether the sensor, planning module and actuator module signal transmission are normal. It checks whether the state and signal detection module and the data buffer module are normally running when the sensor is attacked. It checks whether the loading of the reference path information is normal. The sensor information, planning module information and actuator module signal refer to the environmental obstacle information and the current state information of the intelligent vehicle, the reference path information and the actuator module signal. The intelligent vehicle actuator module signal includes the actual front wheel steering angle executed by the actuator.
[0009] The initialization module loads parameters of the intelligent vehicle time delay control model; loads parameters of the distributed event-triggered module; and loads parameters of the intelligent vehicle state feedback time delay controller. The parameters of the intelligent vehicle time delay control model include the mass of the intelligent vehicle, the distance from the front and rear axle centers to the center of mass, the lateral stiffness and rotational inertia of the front and rear tires; the parameters of the distributed event-triggered module include the weight matrix of the distributed event-triggered and the different constant values preset in the dynamic adaptive threshold function; and the parameters of the intelligent vehicle state feedback time delay controller include the constraint maximum value of the control instruction, the preview distance, the actuator time delay, the maximum and minimum values of the network-induced time delay, and the sampling period.
[0010] Step 2: Establishing the intelligent vehicle time delay control model According to the real-time acquisition of the current state information and the reference path information of the intelligent vehicle in step 1, mathematical calculation is performed and the following intelligent vehicle time delay control model is established in the form of a mathematical equation:
[0011] In the formula: denotes the intelligent vehicle time delay control model running in the continuous time according to the sampling period ; is the state signal of the intelligent vehicle time delay control model, ; is the first-order differential of ; is the front wheel steering angle control instruction actually executed by the intelligent vehicle; is the lateral velocity of the intelligent vehicle; is the yaw rate of the intelligent vehicle; denotes the lateral error, denotes the heading angle error, and the first-order differential form of
[0012] In the formula: is the preview distance; is the speed of the intelligent vehicle; is the reference road curvature, and are the uncertainty disturbances affecting the lateral error and the heading angle error, respectively; is the front wheel steering angle control instruction calculated by the intelligent vehicle state feedback time delay controller; is the coefficient matrix of ; is the coefficient matrix of ; is regarded as the disturbance of the intelligent vehicle time delay control model; and the calculation formula is as follows:
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[0020]
[0021] In the formula: For the quality of intelligent vehicles; The moment of inertia of an intelligent vehicle; and These are the distances from the front and rear axles to the center of gravity, respectively. and These are the lateral stiffness of the front and rear axle tires, respectively. This refers to the actuator time delay.
[0022] Step 3: Design a dynamic adaptive threshold function Based on the security status of the vehicle communication network and the state signal of the intelligent vehicle time-delay control model obtained in step 2, a dynamic adaptive threshold function is designed, and its mathematical equation expression is as follows:
[0023] In the formula: , The first condition for triggering a distributed event One triggering sub-condition; For the first A dynamic adaptive threshold function for each triggering sub-condition; All are preset, different constant values and satisfy... ; It is an exponential function; The sign for calculating the norm; It is the hyperbolic tangent function; For the security status of the communication network between the sensor and the controller module, Refers to a state of no attack. It indicates an attack state; For the first Each sensor at the current sampling time The status signal; To induce network delay, For the first The triggering sub-condition in the first Status signals that are triggered at any time; For the first The error induced by each triggering subcondition and satisfying , For the first The triggering sub-conditions at the current sampling time and the previous trigger time The weighted average of the state signals is expressed mathematically as follows:
[0024] In the formula: , The length of the sampling period. Refers to the moment of triggering Start of The next sampling time; For the first Each trigger sub-condition at the trigger time The status signal, For the first Each trigger sub-condition at the trigger time The status signal, For the first Each trigger sub-condition at the trigger time The status signal.
[0025] Step 4: Determine if the distributed event triggering conditions are met. The distributed event triggering module consists of distributed event triggering conditions. These conditions are designed based on the dynamic adaptive threshold function in step 3 and the state signals of the intelligent vehicle time-delay control model calculated in step 2. Their mathematical expression is:
[0026] In the formula: Pi; Refers to the first k Each event triggers the sampling time. Refers to the first k +1 event trigger sampling time. For the first The weight matrix of each triggering subcondition is positive definite.
[0027] When the sensor is attacked and the signal detection module detects that the security status of the communication network between the sensor and the controller module is under attack, then... , the distributed event-triggered condition is updated as follows:
[0028] The updated event-triggered condition is easier to be satisfied when the sensor is attacked, which helps the intelligent vehicle distributed event control system trigger more effective state signals to resist the impact of sensor attacks.
[0029] When the state and signal detection module detects that the security state of the communication network between the sensor and the controller module is an attack-free state when the sensor is attacked, then , the distributed event-triggered condition is updated as follows:
[0030] The updated event-triggered condition helps the intelligent vehicle distributed event control system filter out a large number of unnecessary state signals, effectively saving the resources of the communication network.
[0031] The intelligent vehicle state feedback time delay controller judges whether the distributed event-triggered condition is met before calculating the control instruction each time, and if it is met, the state signal of the intelligent vehicle time delay control model is updated to the intelligent vehicle state feedback time delay controller to calculate the front wheel steering angle control instruction; if it is not met, it is not updated, and the front wheel steering angle control instruction triggered at the last time is saved in the data buffer module.
[0032] Step 5: Solve the front wheel steering angle control instruction According to the state signal of the intelligent vehicle time delay control model triggered in step 4, the control instruction of the intelligent vehicle state feedback time delay controller is calculated as follows:
[0033] In the formula: K is the gain matrix of the intelligent vehicle state feedback time delay controller, which is based on the coefficient matrix in the intelligent vehicle time delay control model A and B are designed; denotes the signal of the malicious attacker attacking the sensor at the time, and satisfies the following formula:
[0034] In the formula: is a positive diagonal matrix with moderate dimension.
[0035] Step 6: Distributed event-triggered control The control instruction determined in step 5 is sent to the actuator module to realize the distributed event triggered control of the intelligent automobile; further, it is judged whether the intelligent automobile reaches the destination, if yes, the control task is completed, otherwise, step 1 is turned.
[0036] The present application has the following beneficial effects: 1、The present application fully considers the actuator time delay, network induced time delay, limited communication resources and sensor attack problems existing in the intelligent automobile time delay control system, designs a distributed event triggered condition to determine whether the state signal of the intelligent automobile time delay control model is updated to the intelligent automobile state feedback time delay controller, avoids the communication congestion problem, and can save a large amount of communication resources.
[0037] 2、The distributed event triggered condition designed for the intelligent automobile network communication security problem caused by the sensor attack fully considers the influence of the sensor attack, has strong self-adaptive ability, and improves the safety of the intelligent automobile in the motion control process. BRIEF DESCRIPTION OF DRAWINGS
[0038] Figure 1 is the schematic diagram of the present application.
[0039] Figure 2 is the structure schematic diagram of the intelligent automobile distributed event control system of the present application. DETAILED DESCRIPTION
[0040] The following refers to the accompanying Figures 1-2 The present application is further described.
[0041] Figure 1 The schematic diagram of the present application is shown, the safety control and communication resource saving of the intelligent automobile are realized through the intelligent automobile state feedback time delay controller and the distributed event triggered module. The distributed event triggered module judges whether the distributed event triggered condition is satisfied according to the dynamic self-adaptive threshold function and the state signal of the intelligent automobile time delay control model, and then determines whether to update the state signal; the intelligent automobile state feedback time delay controller calculates the front wheel steering angle control instruction according to the state signal triggered by the distributed event triggered module and the gain matrix of the intelligent automobile state feedback time delay controller.
[0042] The intelligent automobile state feedback time delay controller needs to judge whether the distributed event triggered condition in step 4 is satisfied before calculating the front wheel steering angle control instruction each time, if satisfied, the state signal of the intelligent automobile time delay control model is updated to the intelligent automobile state feedback time delay controller to calculate the front wheel steering angle control instruction; if not satisfied, it is not updated, and the control instruction of the last time is saved in the data buffer module.
[0043] The present application utilizesFigure 2 The intelligent vehicle distributed event-triggered control system shown controls in the following specific steps: Step 1: initialization; Step 2: establishing an intelligent vehicle time-delay control model; Step 3: designing a dynamic self-adaptive threshold function; Step 4: judging whether the distributed event-triggered condition is satisfied; Step 5: solving the front wheel steering angle control instruction; Step 6: performing distributed event-triggered control.
[0044] The content of each step of the present embodiment is consistent with the steps of the summary of the invention.
[0045] The present application is not limited to the details of the above-described embodiments, and any equivalent concept or change within the technical scope disclosed in the present application is included in the protection scope of the present application.
Claims
1. A distributed event-triggered control method for intelligent vehicles under sensor attack, characterized in that: Controlling the vehicle using a distributed event-triggered control system includes the following steps: Step 1: Initialization; Step 2: Establish a time-delay control model for intelligent vehicles; Step 3: Design a dynamic adaptive threshold function; Step 4: Determine if the conditions for triggering the distributed event are met; Step 5: Solve for the front wheel steering angle control command; Step 6: Implement distributed event triggering control; The control command determined in step 5 is sent to the actuator module to realize the distributed event-triggered control of the intelligent vehicle; further, it is determined whether the intelligent vehicle has reached its destination. If so, the control task is completed; otherwise, proceed to step 1.
2. The distributed event-triggered control method for intelligent vehicles under sensor attack as described in claim 1, characterized in that: The intelligent vehicle distributed event triggering control system includes an initialization module, a sensor status and signal detection module when the sensor is attacked, a distributed event triggering module, a data cache module, a controller module, and an actuator module. The initialization module is used to check whether the signal transmission and reception of the sensor, planning module, and actuator module are normal; check the state of the sensor when it is attacked and whether the signal detection module and data buffer module are operating normally; check whether the loading of reference path information is normal, the reference path information including reference position, reference road curvature, and reference heading angle; load the parameters of the intelligent vehicle time-delay control model; load the parameters of the distributed event triggering module; and load the parameters of the intelligent vehicle state feedback time-delay controller. The sensor attack status and signal detection module determines the security status of the vehicle communication network by real-time monitoring of whether the data stream transmission of the vehicle communication network is distorted, and obtains the signal of a malicious attacker attacking the sensor. The security status is either an attacked state or an unattacked state. The distributed event triggering module consists of distributed event triggering conditions including several triggering sub-conditions. The distributed event triggering conditions are designed based on a dynamic adaptive threshold function and the state signal of the intelligent vehicle time-delay control model. The state signal of the intelligent vehicle time-delay control model is designed based on the sensor measurement status. The dynamic adaptive threshold function is designed based on the security status of the vehicle communication network and the state signal of the intelligent vehicle time-delay control model. The distributed event triggering module determines whether the state signal of the intelligent vehicle time-delay control model is updated in the controller module. The elements of the state signal of the intelligent vehicle time-delay control model include lateral velocity, yaw rate, lateral error, and heading angle error. The lateral error refers to the difference between the current position and the reference position of the intelligent vehicle, and the heading angle error refers to the difference between the current heading angle and the reference heading angle of the intelligent vehicle. The current state information of the intelligent vehicle includes position information, heading angle, longitudinal velocity, lateral velocity, and yaw rate. The data caching module is used to cache the state signal of the intelligent vehicle time-delay control model triggered in the previous moment when the distributed event triggering conditions are not met. The intelligent vehicle time-delay control model is obtained by mathematical calculation of the current state information of the intelligent vehicle and the reference path information and modeling it in the form of mathematical equations. The controller module consists of an intelligent vehicle state feedback time delay controller, which is designed based on an intelligent vehicle time delay control model. The intelligent vehicle state feedback time delay controller uses the state signals of the intelligent vehicle time delay control model to solve for the front wheel steering angle control command.
3. The distributed event-triggered control method for intelligent vehicles under sensor attack as described in claim 1, characterized in that: The initialization method described in step 1 is as follows: The initialization module checks whether the signal transmission and reception of the sensors, planning module, and actuator module are normal; it checks the state of the sensors when attacked and whether the signal detection module and data buffer module are operating normally; it checks whether the loading of the reference path information is normal; the sensor information, planning module information, and actuator module signals respectively refer to environmental obstacle information and the current state information, reference path information, and actuator module signals of the intelligent vehicle; the actuator module signals of the intelligent vehicle include the front wheel steering angle actually executed by the actuator; The initialization module loads the parameters of the intelligent vehicle time-delay control model; loads the parameters of the distributed event triggering module; and loads the parameters of the intelligent vehicle state feedback time-delay controller. The parameters of the intelligent vehicle time-delay control model include the mass of the intelligent vehicle, the distance from the center of the front and rear axles to the center of mass, the lateral stiffness of the front and rear axle tires, and the moment of inertia. The parameters of the distributed event triggering module include the weight matrix of the distributed event triggering and different preset constants in the dynamic adaptive threshold function. The parameters of the intelligent vehicle state feedback time-delay controller include the maximum value of the control command constraint, the preview distance, the actuator time delay, the maximum and minimum values of the network-induced time delay, and the sampling period.
4. The distributed event-triggered control method for intelligent vehicles under sensor attack as described in claim 1, characterized in that: The method for establishing the intelligent vehicle time-delay control model in step 2 is as follows: Based on the current state information of the intelligent vehicle obtained in real time in step 1 and the reference path information, mathematical calculations are performed, and the following intelligent vehicle time-delay control model is established in the form of mathematical equations: In the formula: Refers to the time-delay control model of intelligent vehicles according to the sampling period The continuous time of execution; For the state signals of the time-delay control model of intelligent vehicles, ; for The first derivative; The actual front wheel steering angle control commands executed by the intelligent vehicle; The lateral speed of the intelligent vehicle; For the yaw rate of intelligent vehicles; Refers to lateral error. Refers to the error in heading angle. and The first-order differential form is as follows; In the formula: Preview distance; For the speed of intelligent vehicles; For reference road curvature, and These are the uncertainties affecting lateral error and heading angle error, respectively; The front wheel steering angle control command calculated by the intelligent vehicle state feedback time-delay controller; for The coefficient matrix, for The coefficient matrix, The disturbance is considered as part of the time-delay control model for intelligent vehicles; the calculation formula is as follows: In the formula: For the quality of intelligent vehicles; The moment of inertia of an intelligent vehicle; and These are the distances from the front and rear axles to the center of gravity, respectively. and These are the lateral stiffness of the front and rear axle tires, respectively. This refers to the actuator time delay.
5. The distributed event-triggered control method for intelligent vehicles under sensor attack as described in claim 1, characterized in that: The method for designing the dynamic adaptive threshold function described in step 3 is as follows: Based on the security status of the vehicle communication network and the state signal of the intelligent vehicle time-delay control model obtained in step 2, a dynamic adaptive threshold function is designed, and its mathematical equation expression is as follows: In the formula: , The first condition for triggering a distributed event One triggering sub-condition; For the first A dynamic adaptive threshold function for each triggering sub-condition; All are preset, different constant values and satisfy... ; It is an exponential function; The sign for calculating the norm; It is the hyperbolic tangent function; For the security status of the communication network between the sensor and the controller module, Refers to a state of no attack. It indicates an attack state; For the first Each sensor at the current sampling time The status signal; To induce time delay in the network, For the first The triggering sub-condition in the first Status signals that are triggered at any time; For the first The error induced by each triggering subcondition and satisfying , For the first The triggering sub-conditions at the current sampling time and the previous trigger time The weighted average of the state signals is expressed mathematically as follows: In the formula: , The length of the sampling period. Refers to the moment of triggering Start of The next sampling time; For the first Each trigger sub-condition at the trigger time The status signal, For the first Each trigger sub-condition at the trigger time The status signal, For the first Each trigger sub-condition at the trigger time The status signal.
6. The distributed event-triggered control method for intelligent vehicles under sensor attack as described in claim 1, characterized in that: The method for determining whether the distributed event triggering conditions are met in step 4 is as follows: The distributed event triggering module consists of distributed event triggering conditions. These conditions are designed based on the dynamic adaptive threshold function in step 3 and the state signals of the intelligent vehicle time-delay control model calculated in step 2. Their mathematical expression is: In the formula: Pi; Refers to the first k Each event triggers the sampling time. Refers to the first k +1 event trigger sampling time; For the first The weight matrix of each triggering sub-condition is positive definite; When the sensor is attacked and the signal detection module detects that the security status of the communication network between the sensor and the controller module is under attack, then... The distributed event triggering conditions are updated as follows: The updated event triggering conditions are easier to meet under sensor attacks, which helps the intelligent vehicle distributed event control system to trigger more effective status signals to resist the impact of sensor attacks. When the sensor is attacked and the signal detection module detects that the communication network between the sensor and the controller module is in a non-attack state, then... The distributed event triggering conditions are updated as follows: The updated event triggering conditions help the intelligent vehicle distributed event control system filter out a large number of unnecessary status signals, thereby effectively saving communication network resources; Before each calculation of a control command, the intelligent vehicle state feedback time delay controller determines whether the distributed event triggering conditions are met. If the conditions are met, the state signal of the intelligent vehicle time delay control model is updated in the intelligent vehicle state feedback time delay controller to calculate the front wheel steering angle control command. If the conditions are not met, no update is made, and the front wheel steering angle control command triggered at the previous moment is saved in the data cache module.
7. The distributed event-triggered control method for intelligent vehicles under sensor attack as described in claim 1, characterized in that: The method for solving the front wheel steering angle control command in step 5 is as follows: Based on the state signal of the intelligent vehicle time-delay control model triggered in step 4, the control command of the intelligent vehicle state feedback time-delay controller is calculated as follows: In the formula: K The gain matrix of the state feedback time-delay controller for intelligent vehicles is based on the coefficient matrix in the time-delay control model of intelligent vehicles. A and B Design obtained; Refers to the malicious attacker in the The signal that attacks the sensor at any time satisfies the following formula: In the formula: It is a diagonal matrix with appropriate dimensions.