A safety state estimation method for connected autonomous driving vehicles under network attacks

Abstract

The present invention provides a method for estimating the safety state of a networked autonomous driving vehicle under a network attack, which relates to the field of safety state estimation of autonomous driving vehicles. By fusing the information measured on the target vehicle and the information of other vehicles within the communication range, combined with a proximity algorithm, a risk isolation scheme is designed to achieve more accurate state estimation performance. The method includes: fusing vehicle data within the vehicle network area and establishing a networked autonomous driving vehicle model; proposing an iterative clustering algorithm to identify abnormal vehicle behavior and issue an early warning, thereby eliminating abnormal vehicle data and updating the state estimation data set; defining a proximity function and state estimation weight parameters to estimate the safety state of vehicles within the vehicle network area. The present invention can effectively deal with abnormal vehicle behavior within the vehicle network area, and help improve the safety and adaptability of autonomous driving of vehicles.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving, and in particular to a method for estimating the safety state of a networked autonomous driving vehicle under a network attack. Background Art

[0002] With the continuous advancement of science and technology, autonomous driving technology has become increasingly mature and has been applied in transportation. However, as a driver, faced with various situations, we still cannot completely hand over the responsibility to the autonomous driving system. Therefore, how to ensure the safety of autonomous vehicles has become a critical issue. One solution is to perform vehicle state estimation. Autonomous driving state estimation is a core technology of autonomous driving. It uses sensors such as radar, cameras, and lidar to collect information about the vehicle's surroundings, further identify road lines, obstacles, pedestrians, and other objects on the road, and determine information such as the vehicle's position, posture, and speed, thereby enabling vehicle control and planning.

[0003] Autonomous vehicles can provide real-time feedback on their current safety status estimates, giving drivers a clearer understanding of the vehicle's current safety status. Numerous studies have demonstrated that analyzing vehicle operating data can yield a relatively accurate state estimation model. This real-time vehicle status data can help drivers better understand the entire system, enabling them to make better decisions while ensuring vehicle safety and reliability. However, the reliability of autonomous driving state estimates can be threatened by cyberattacks. Hackers can tamper with sensor data, causing errors in vehicle control and planning, potentially leading to accidents.

[0004] Therefore, despite extensive research on state estimation algorithms, it remains a challenging problem if the network is attacked by cyberattacks. To ensure the safety and reliability of autonomous vehicles, it is necessary to pay attention to the threat of cyberattacks to autonomous driving state estimation, strengthen security testing and evaluation, identify security vulnerabilities, and promptly resolve and patch them. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for estimating the safety state of a connected autonomous driving vehicle under a network attack, so as to solve the problems raised by the above background technology.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] Integrate vehicle data within the connected vehicle area and build a connected autonomous driving vehicle model;

[0008] Design an iterative clustering algorithm, propose a malicious vehicle warning solution, and identify abnormal vehicle behavior;

[0009] Eliminate abnormal vehicle data and update the state estimation dataset;

[0010] Define the proximity function and state estimation weight parameters;

[0011] Provide real-time feedback on the current security status estimate and estimate the status at the next moment.

[0012] Furthermore, the vehicle data within the connected vehicle area is integrated and a connected autonomous driving vehicle model is established, including:

[0013] In the IoV area, each vehicle can collect and transmit data related to the vehicle and its surrounding environment through onboard equipment, such as vehicle speed, driving path, lane traffic conditions, etc. This data can be collected and used to build a connected autonomous driving vehicle model. At time k, the n vehicles in the IoV communication area are numbered and added to the set N. It is assumed that the number of vehicles communicating with the IoV remains unchanged during the K time interval, and that all vehicles are fully connected, that is, any two vehicle communication nodes are connected. The connected vehicle not only needs to transmit its own sensor measurement information y i (k) Upload to the vehicle cloud and share other vehicle sensor information y between the vehicle and neighboring vehicles i|j (k) Upload to the vehicle cloud to achieve vehicle information redundancy. This paper assumes that the sensor dimensions are the same.

[0014] The vehicle dynamics system model is expressed as:

[0015] x i (k) = A i x i (k-1)+ω i (k-1)

[0016] Where A is the system state matrix, ω represents the system noise and its covariance matrix is ​​represented by Q;

[0017] Step S1.3, the vehicle sensor measurement equation can be expressed as:

[0018] y i (k)=H i x i (k)+v i (k)

[0019] Where H is the sensor measurement matrix, v represents the measurement noise and its covariance matrix is ​​represented by R;

[0020] Step S1.4: The vehicle sensor measurement model shared between vehicles can be expressed as:

[0021] y j|i (k) = y i (k)

[0022] where y j|i represents the measurement value shared by vehicle i received by vehicle j;

[0023] Step S1.5: The information about the i-th vehicle received by the vehicle cloud can be expressed as:

[0024] z i (k) = y i (k)+a i (k)

[0025] z j|i (k) = y j|i (k)+a j (k)

[0026] It should be noted that in order to simplify the expression of the formula and improve readability, the time parameter k is omitted in some descriptions.

[0027] where z i Represents vehicle i sending its own measurement value to the vehicle cloud, z j|i On behalf of vehicle j, the measurement value shared by vehicle i is sent to the vehicle cloud; a i and a j is an item of maliciously manipulated information. Assuming that the communication link between the sensor of vehicle i and the vehicle cloud is working normally, then a i =0; otherwise, a i ≠0.

[0028] Furthermore, we designed an iterative clustering algorithm and proposed a malicious vehicle warning scheme to identify abnormal vehicle behavior, including:

[0029] By designing an iterative clustering algorithm, the vehicle cloud can quickly mark and cluster the vehicle's status information and evaluate the vehicle's abnormal behavior. If abnormal vehicle data is found, an alarm will be issued.

[0030] The iterative clustering algorithm includes parameter initialization, allocation step and update step. The goal is to classify n sensor measurements into two categories and select the category with higher credibility for state estimation. The specific steps are as follows:

[0031] At time k, the vehicle cloud receives n sensor measurements of vehicle i, including the self-measurement value z sent by sensor node i. i and the measurement values ​​z sent by all neighboring nodes j|i , after fusion, {z il (k), l∈N}, where the set N includes all neighboring nodes of sensor node i and itself, and card(N)=n.

[0032] Initialize and define the category set {G ig|i=1,2,...,n.;g=1,2.}, where i is the sensor node label and g is the category label.

[0033] Initialization, set the mean vector m (g) , and assign a random initial value.

[0034] Initialization, define the distance function between two points:

[0035]

[0036] Initialize and define the matrix represents the clustering result of each iteration,

[0037]

[0038] Where, define u gl Represents the lth test sample z il (k) Is it assigned to category g? If so, u gl =1; otherwise u gl =0.

[0039] Assign step, measure z il (k) Assignment to Category G requires the following conditions to be met:

[0040]

[0041] In the update step, in order to match the sample measurement data in the current category, the following mean updates are required:

[0042]

[0043] Repeat the assignment and update steps until the matrix U does not change.

[0044] Let k (c) is the number of data points belonging to class c, defined as follows:

[0045]

[0046] Select the trusted data set of sensor node i as follows:

[0047]

[0048] The g i The data within the class is placed into the confidence set G i In the set G, there is only i The data points in the set are considered to be credible data, and the number of elements in the set is k. (gi) ;Put the rest of the data into the collection At this time, the collection It is considered a suspicious set, and the number of elements in the set is nk (gi) .

[0049] The iterative clustering algorithm first integrates sensor measurement data and uses iterative allocation and updating to divide all data into two groups. The group with higher confidence is selected for subsequent state estimation, while the data in the other group with lower confidence is considered to be potentially risky. Each data point in the group has a unique label, which can be used to determine which vehicle the data was measured from and which vehicle sensor sent it to the vehicle cloud.

[0050] At each moment, a total of n vehicles upload their own sensor measurement information and the sensor information of n-1 vehicles shared with other vehicles to the cloud. That is, the vehicle cloud will receive n measurement values ​​of sensor i at a time node, and a total of n 2 At each time point, the cloud will determine the suspicious set corresponding to each vehicle sensor based on the iterative clustering algorithm. Count the sensors in the n suspicious sets within the K time interval, select sensors with a number greater than K / 2, and put them into the set Count the sensors in the n suspicious sets, select sensors with a number greater than n / 2, and put them into the set By prompting technicians that the vehicle may be under cyber attack, it is recommended that the driver intervene in the operating system as soon as possible to make a manual judgment on the current vehicle status.

[0051] Furthermore, abnormal vehicle data is excluded and the state estimation dataset is updated, including:

[0052] Malicious network attacks may cause vehicle cloud and multi-sensor data to be tampered with or injected with false information, which will seriously affect the accuracy and credibility of state estimation. It is necessary to develop an effective abnormal data identification mechanism to filter out abnormal data. Once an anomaly is detected, the data needs to be excluded to reduce its impact on state estimation. At the same time, it is also necessary to continuously update the state estimation dataset so that autonomous driving vehicles can make more accurate road condition judgments. In the K time interval, by adopting a malicious vehicle early warning scheme, the data is detected to see if it is suspicious, and the safety sensor set Φ is obtained. At this time, card(Φ) = φ, the abnormal sensor data is removed from the dataset, and the remaining data is added to the safety dataset Ψ. At this time, This can effectively prevent abnormal data from misleading autonomous vehicles and improve the safety of autonomous driving systems.

[0053] Furthermore, the proximity function and state estimation weight parameters are defined, including:

[0054] In a vehicle-cloud and multi-sensor environment, different data sources (vehicle sensors) may provide different measurement data, which can lead to deviations and issues in the state estimation results. Therefore, defining the proximity function and state estimation weight parameters is a crucial step. By comparing and analyzing the measurement data provided by different data sources, we calculate similarity metrics between them. Based on these similarity metrics, we can effectively determine the relative reliability of different data sources and thus perform weighted combinations of the measurement data.

[0055] Define the proximity function:

[0056]

[0057] At time k, assuming that there are φ sensor measurements of vehicle i in the security dataset Ψ obtained by vehicle cloud screening, z iβ (k), (β=1,2,...,φ) means that the two-by-two combinations are calculated The closeness matrix between the measurements is as follows:

[0058]

[0059] The βth measurement value z of vehicle i iβ and any other measurement value z in the security dataset ip The consistency measure of closeness between (p=1,2,...,φ) can be expressed as:

[0060]

[0061] Among them, E iβ Reflects the measured value z iβ How close it is to any other sensor measurement.

[0062] In order to obtain a more reasonable weight value, the consistency measure of the entire interval is considered. In the K time interval, the βth measurement value z iβ The mean agreement with any other measurement in the safety dataset is:

[0063]

[0064] In the K time interval, the βth measurement value z iβ The variance of the agreement with any other measurement is:

[0065]

[0066] At this time, if the mean μ iβ Large, and variance S iβis smaller, it means that the measured value z iβ It has higher reliability and should have higher weight in the data fusion process. Therefore, this design introduces the intermediate variable α iβ as follows:

[0067]

[0068] In the K time interval, define the sensor measurement value z iβ The weight parameter is ξ iβ as follows:

[0069]

[0070] Then, at time k, the credible measurement value of the i-th vehicle is as follows:

[0071]

[0072] Furthermore, the current security status estimate is fed back in real time, and the next security status estimate is estimated, including:

[0073] Providing real-time feedback on the current safety state estimate and estimating the state at the next moment is an important step in achieving stable operation and accurate decision-making for autonomous driving systems. This can be achieved using the Kalman filter algorithm. Kalman filtering is a commonly used state estimation method that accurately estimates the future state based on current sensor measurement data and prediction models. Knowing the current safety state estimate allows timely action to protect vehicle safety. Estimating the state at the next moment also helps us anticipate risks and provide reliable predictions for future safety. State estimation is performed using the Kalman filter algorithm and vehicle model based on current measurements, as described below.

[0074] Initialize system status and the covariance matrix of the state

[0075] The time update steps are as follows to predict the system status in advance:

[0076]

[0077] Ahead forecast error covariance:

[0078]

[0079] The measurement update steps are as follows to calculate the Kalman gain coefficient:

[0080]

[0081] pass Update estimate status:

[0082]

[0083] Update error covariance:

[0084]

[0085] The current state estimate is fed back to the vehicle operating system in real time, and the state at the next moment is predicted in advance for reference.

[0086] Compared with the prior art, the present invention has the following beneficial effects:

[0087] This method is based on the safety state estimation of networked autonomous vehicles and malicious vehicle warning. By utilizing multi-sensor data and vehicle cloud data redundancy, it improves the ability to respond to network attacks; an iterative clustering algorithm is designed to screen malicious sensors and improve the accuracy and timeliness of malicious vehicle warnings; at the same time, a proximity function is designed to assign state estimation parameter weights, and the Kalman filter algorithm is referenced to achieve higher accuracy and stability in the state estimation of autonomous vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 Schematic diagram of the vehicle network of the present invention

[0089] Figure 2 Flowchart of the present invention DETAILED DESCRIPTION

[0090] The following specific implementation examples are used to illustrate and explain the present invention, but are not intended to limit the scope of the present invention.

[0091] The present invention provides a method for estimating the safety state of a connected autonomous driving vehicle under a network attack, such as Figure 2 As shown, the following steps are included:

[0092] Step 1: Collect the vehicle's own driving data and send its own data to other vehicles in its communication area. Also send the collected data of other vehicles and its own data to the vehicle cloud. Number the five vehicles in the vehicle network communication area and add them to the set N. Assume that the number of vehicles communicating with the vehicle network remains unchanged within the K time interval, and that all vehicles are fully connected, that is, any two vehicle communication nodes are connected. Vehicle 1 not only uploads its own sensor measurement information y1(k) to the vehicle cloud, but also shares other vehicle sensor information y1(k) between vehicles. 1|2 (k), y 1|3 (k), y 1|4 (k) and y 1|5(k) Upload to the vehicle cloud to achieve vehicle information redundancy and integrate the data of the five vehicles in the connected vehicle area. Build a connected autonomous driving vehicle model. This paper assumes that the sensor dimensions are the same.

[0093] Build a simulation scenario and express the vehicle dynamics system model as:

[0094] x i (k) = A i x i (k-1)+ω i (k-1)

[0095] Where A is the system state matrix, ω represents the system noise and its covariance matrix is ​​represented by Q. The parameters are set as follows:

[0096]

[0097] The system state is set to the position of the vehicle in the x-direction and y-direction.

[0098] The vehicle sensor measurement equation can be expressed as:

[0099] y i (k)=H i x i (k)+v i (k)

[0100] Where H is the sensor measurement matrix, v represents the measurement noise and its covariance matrix is ​​represented by R. The parameters are set as follows:

[0101]

[0102] The vehicle sensor measurement model shared between vehicles can be expressed as:

[0103] y j|i (k) = y i (k)

[0104] where y j|i represents the measurement value shared by vehicle i received by vehicle j;

[0105] The information about the i-th vehicle received by the vehicle cloud can be expressed as:

[0106] z i (k) = y i (k)+a i (k)

[0107] z j|i (k) = y j|i (k)+a j (k)

[0108] where zi Represents vehicle i sending its own measurement value to the vehicle cloud, z j|i On behalf of vehicle j, the measurement value shared by vehicle i is sent to the vehicle cloud; a i and a j is an item of maliciously manipulated information. When the communication link between the vehicle's sensor and the vehicle cloud is working properly, a i = 0, assuming that the communication link of vehicle 5 is maliciously manipulated, assign

[0109] Step 2: Identify abnormal vehicle behavior based on the vehicle warning plan. Divide the five sensor measurements into two categories and select the more reliable category for state estimation. The specific steps are as follows:

[0110] The vehicle cloud receives five sensor measurements of vehicle i, including the self-measured value z sent by sensor node i. i and the measurement values ​​z sent by all neighboring nodes j|i , after fusion, {z il (k),l∈N}, where the set N includes all neighboring nodes of sensor node i and itself, and card(N)=5.

[0111] Initialize and define the category set {G ig |i=1,2,3,4,5.;g=1,2.}, where i is the sensor node number and g is the category number. Set the mean vector m (g) , and assign a random initial value.

[0112] Define the distance function between two points:

[0113]

[0114] Define the matrix represents the clustering result of each iteration,

[0115]

[0116] Where, define u gl Represents the lth test sample z il (k) Is it allocated to category g? If yes, then u gl =1; otherwise u gl =0.

[0117] Assign step, measure z il (k) Assignment to Category G requires the following conditions to be met:

[0118]

[0119] In the update step, in order to match the sample measurement data in the current category, the following mean updates are required:

[0120]

[0121] Repeat the assignment and update steps until the matrix U does not change.

[0122] Let k (c) is the number of data points belonging to class c, defined as follows:

[0123]

[0124] Select the trusted data set of sensor node i as follows:

[0125]

[0126] The g i The data within the class is placed into the confidence set G i In the set G, there is only i The data points in the set are considered to be credible data, and the number of elements in the set is k. (gi) ;Put the rest of the data into the collection At this time, the collection It is considered a suspicious set, and the number of elements in the set is 5-k (gi) .

[0127] At the k time node, the suspicious set corresponding to each vehicle sensor can be determined Count the sensors in the five suspicious sets within the K time interval, select sensors with a number greater than 5 / 2, and put them into the set Count the sensors in these 5 suspicious sets, select the sensors whose number is greater than 5 / 2, and put them into the set By prompting technicians that the vehicle may be under cyber attack, it is recommended that the driver intervene in the operating system as soon as possible to make a manual judgment on the current vehicle status.

[0128] Step 3: Eliminate abnormal vehicle data and update the state estimation dataset. Specifically: During the process of autonomous driving, once abnormal vehicle data is detected, it is necessary to exclude the data to maintain the accuracy of the dataset. At the same time, it is also necessary to continuously update the state estimation dataset so that the autonomous driving vehicle can make more accurate judgments on the road conditions. In the K time interval, by adopting the malicious vehicle early warning scheme, detect whether the data is suspicious and obtain the safety sensor set Φ. At this time, card(Φ)=φ=4, remove the abnormal sensor data from the dataset, and add the remaining data to the safety dataset Ψ. At this time This can effectively prevent abnormal data from misleading autonomous vehicles and improve the safety of autonomous driving systems.

[0129] Step 4: Define the proximity function and state estimation weight parameters, specifically: Define the proximity function

[0130]

[0131] At time k, assuming that there are 4 sensor measurements of vehicle i in the security dataset Ψ obtained by vehicle cloud screening, z iβ (k), (β=1,2,3,4.) means, then the two-by-two combinations are calculated The closeness matrix between the measurements is as follows:

[0132]

[0133] The βth measurement value z of vehicle i iβ and any other measurement value z in the security dataset ip The consistency measure of closeness between (p = 1, 2, 3, 4.) can be expressed as:

[0134]

[0135] Among them, E iβ Reflects the measured value z iβ How close it is to any other sensor measurement.

[0136] In order to obtain a more reasonable weight value, the consistency measure of the entire interval is considered. In the K time interval, the βth measurement value z iβ The mean agreement with any other measurement in the safety dataset is:

[0137]

[0138] In the K time interval, the βth measurement value z iβ The variance of the agreement with any other measurement is:

[0139]

[0140] At this time, if the mean μ iβ Large, and variance S iβ is smaller, it means that the measured value z iβ It has higher reliability and should have higher weight in the data fusion process. Therefore, this design introduces the intermediate variable α iβ as follows:

[0141]

[0142] In the K time interval, define the sensor measurement value z iβThe weight parameter is ξ iβ as follows:

[0143]

[0144] Then, at time k, the credible measurement value of the i-th vehicle is as follows:

[0145]

[0146] Step 5: Update the state estimate, specifically: based on the current measurement value, perform state estimation by using the Kalman filter algorithm and the vehicle model.

[0147] Initialize system status and the covariance matrix of the state

[0148] Time update, predict system status in advance:

[0149] Forecast error covariance:

[0150] Measurement update, calculation of Kalman gain coefficient:

[0151] pass Update estimate status:

[0152] Update error covariance:

[0153] The current state estimate is fed back to the vehicle operating system in real time, and the state at the next moment is predicted in advance for reference.

[0154] After introducing the iterative clustering algorithm, abnormal vehicle behavior can be effectively identified. Furthermore, the configuration of the proximity function and estimation weight parameters improves the accuracy of state estimation. These findings demonstrate the effectiveness of this safety state estimation method for connected autonomous vehicles.

[0155] The examples described above are intended solely to illustrate the technical solutions of the present invention and are not intended to limit them. Although the examples have been described in detail, those skilled in the art will appreciate that the technical solutions described in the examples may be modified or equivalently substituted for some or all of the technical features. Such modifications or substitutions do not detract from the essence of the corresponding technical solutions beyond the scope of the present invention.

Claims

1. A method for estimating the safety state of a connected autonomous driving vehicle under a network attack, characterized in that: The following steps are involved: Step S1: Integrate vehicle data within the Internet of Vehicles area and establish a connected autonomous driving vehicle model; Step S2: Design an iterative clustering algorithm, propose a malicious vehicle warning scheme, and identify abnormal vehicle behavior; Step S3: exclude abnormal vehicle data and update the state estimation data set; Step S4: defining a closeness function and state estimation weight parameters; Step S5: Feedback the estimated value of the safety status at the current moment in real time and estimate the status at the next moment; The specific steps of integrating vehicle data in the Internet of Vehicles area in step S1 include: Step S1.1: At time k, The vehicles are numbered and added to the collection In the K time interval, if the number of vehicles communicating with the Internet of Vehicles remains unchanged and all vehicles are fully connected, that is, any two vehicle communication nodes are connected; the connected vehicle must not only transmit its own sensor measurement information Upload to the vehicle cloud and share other vehicle sensor information between vehicles Upload to the vehicle cloud to achieve vehicle information redundancy; described The sensor measurement information of the i-th vehicle; described For the The sensor measurement information of the vehicle is shared by the Car upload.

2. The method for estimating the safety state of a connected autonomous driving vehicle under a network attack according to claim 1, characterized in that: The specific steps of establishing the networked autonomous driving vehicle model in step S1 include: Step S1.2: Express the vehicle dynamics system model as: in is the system state matrix, represents the system noise and its covariance matrix is ​​given by express; Step S1.3, the vehicle sensor measurement equation can be expressed as: in is the sensor measurement matrix, represents the measurement noise and its covariance matrix is ​​given by express; Step S1.4: The vehicle sensor measurement model shared between vehicles can be expressed as: in Representative vehicle Receive the vehicle shared measurements; Step S1.5: Vehicle Cloud receives the information about The information of a car can be expressed as: in Representative vehicle Send its own measurement values ​​to the vehicle cloud, Representative vehicle The vehicle The shared measurements are sent to the vehicle cloud; and It is an item that maliciously manipulates information. If the vehicle When the communication link between the sensor and the vehicle cloud is working properly, then ;otherwise, .

3. The method for estimating the safety state of a connected autonomous driving vehicle under a network attack according to claim 1, characterized in that: The specific steps of designing the iterative clustering algorithm in step S2 include: The iterative clustering algorithm includes parameter initialization, allocation step and update step, the goal is to The sensor measurements are divided into two categories, and the one with higher credibility is selected for state estimation. The specific steps are as follows: Step S2.1: At time k, the vehicle cloud receives the vehicle The sensor measurements are , including sensor nodes Self-measured values ​​emitted and the measurements sent by all neighboring nodes , after fusion Indicates that the set Including sensor nodes All neighboring nodes and itself, at this time ; Step S2.2: Initialize and define the category set ,in is the sensor node number, is the category label; Step S2.3, initialization, setting the mean vector , and assign a random initial value; Step S2.4, initialization, define the distance function between two points: Step S2.5, initialization, definition matrix represents the clustering result of each iteration, Among them, the definition Representative Test samples Is it assigned to Class, if assigned ;otherwise ; Step S2.6, allocation step, measure the value Assigned to Class, the following conditions must be met: Step S2.7, update step, in order to match the sample measurement data in the current category, the following mean update is required: Step S2.8, repeat the allocation and update steps until the matrix No changes occur; Step S2.9, let For the The number of data points for a class is defined as follows: Step S2.10: Select sensor nodes The trusted data set is as follows: The first The data within the class is placed into the confidence set In the The data points in the set are considered to be credible data. At this time, the number of elements in the set is ;Put the rest of the data into the collection At this time, the collection is considered a suspicious set, and the number of elements in the set is .

4. The method for estimating the safety state of a connected autonomous driving vehicle under a network attack according to claim 1, characterized in that: The specific steps of defining the closeness function and the state estimation weight parameters in step S4 include: Step S4.1, define the closeness function: Among them, the closer A and B are, the greater the closeness; Step S4.2: At time k, if the security dataset obtained by vehicle cloud screening is Related vehicles The sensor measurements are , by Indicates that the pairwise calculations are The closeness matrix between the measurements is as follows: Step S4.3: Vehicles No. Measurements and any other measurements in the security dataset The consistency measure of closeness between can be expressed as: in, Reflects the measured value How close it is to any other sensor measurement; Step S4.4: In order to obtain a more reasonable weight value, consider the consistency measure of the entire interval; in the K time interval, the first Measurements The mean agreement with any other measurement in the safety dataset is: Step S4.5: In the K time interval, Measurements The variance of the agreement with any other measurement is: At this time, if the mean Large, and the variance Smaller, it means the measured value It has higher reliability and should have higher weight in the data fusion process; therefore, this design introduces intermediate variables as follows: Step S4.6: Define the sensor measurement value within the K time interval The weight parameter is as follows: Then, at time k, The trusted measurements for a car are as follows: 。 5. The method for estimating the safety state of a connected autonomous driving vehicle under a network attack according to claim 1, characterized in that: The specific steps of feeding back the estimated value of the safety status at the current moment and estimating the status at the next moment in real time in step S5 include: Real-time feedback of the current safety status estimate and the next-moment status estimate are essential. By knowing the current safety status estimate, appropriate measures can be taken in a timely manner to protect vehicle safety. At the same time, the next-moment status estimate can also help us predict risks in advance and provide reliable predictions for future safety assurance. Based on the current measurement value, the state is estimated by using the Kalman filter algorithm and the vehicle model; Step S5.1: Initialize system status and the covariance matrix of the state ; Step S5.2, time update steps are as follows, predicting the system state in advance: Ahead forecast error covariance: Step S5.3, measurement update step is as follows, calculate the Kalman gain coefficient: pass Update estimate status: Update error covariance: The current state estimate is fed back to the vehicle operating system in real time, and the state at the next moment is predicted in advance for reference.

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