CN114578847A - Unmanned aerial vehicle collaborative data verification system and method based on automatic driving vehicle networking - Google Patents
Unmanned aerial vehicle collaborative data verification system and method based on automatic driving vehicle networking Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle-assisted data verification system and method in an automatic driving vehicle networking network, wherein the system comprises an unmanned aerial vehicle sensing unit, a road side unit, a sensing data verification unit and a data storage unit; the unmanned aerial vehicle sensing unit is connected with the road side unit in an Air to ground (A2G) mode; the sensing data verification unit is connected with the road side unit in an A2G mode; the data storage unit is connected with the road side unit. The method comprises the steps of recruiting a corresponding number of verification unmanned aerial vehicles to verify perception data according to the credit of the perception unmanned aerial vehicles; establishing credibility of the verification unmanned aerial vehicle according to verification time, cooperation evaluation and verification results; and performing consensus according to the proposed trust interest certificate of credit enhancement in the block chain of the alliance, finishing the storage of perception data, credit degree and verification information, and realizing data traceability.
Description
Technical Field
The invention belongs to the field of Automatic Vehicle Networking (AVNs), and particularly relates to an unmanned aerial vehicle-assisted data verification system and method in an automatic vehicle networking.
Background
As one of the most vigorous and vigorous fields in the Internet of things (IoT), the automated driving vehicle networking network will play an important role in future Intelligent Transportation Systems (ITSs) and has a great potential to change the human social aspect. The automatic driving car networking has a series of advantages of reducing traffic accidents, shortening average commuting time, reducing carbon dioxide emission, improving travel experience and the like. Therefore, the prospect of the automatic driving vehicle networking is widely seen. In order to realize practical application of Autonomous Vehicles (AVs), the driving safety of each autonomous vehicle is receiving much attention. In particular, reasonable utilization and analysis of a large amount of surrounding traffic data (such as road congestion, the number of pedestrians, the traveling speed of vehicles in front of and behind, and the like) can ensure the safety of an autonomous automobile. However, due to the limited sensing range of the vehicle-mounted sensor, it is difficult for the automatic driving system to collect traffic data beyond the sensed distance in time or in advance. In addition, deployment of sensing infrastructure (e.g., wayside monitoring units) is costly and requires significant resources (e.g., bandwidth, computing, and energy). In recent years, Unmanned Aerial Vehicles (UAVs) have been widely used to monitor ground traffic due to their advantages of low cost and easy deployment. The unmanned aerial vehicle can be flexibly deployed along with the change of traffic environment, and can realize real-time and large-scale traffic perception. Therefore, it is very promising to apply drone-assisted traffic awareness in an autonomous vehicle networking network.
However, the large-scale application of drone-assisted traffic awareness in autonomous vehicle networking faces some challenges. In particular, the quality and behavior perceived by drones greatly influence the decision-making for autonomous driving. On the one hand, as the unmanned aerial vehicles have different perception abilities, the unmanned aerial vehicles with poor perception abilities can generate and provide low-quality traffic perception data, so that the accuracy of judging the traffic conditions by the unmanned aerial vehicles can be greatly reduced. On the other hand, any unmanned aerial vehicle can participate in data perception, and some malicious unmanned aerial vehicles can conduct dishonest perception behaviors to forge and generate false perception data. As a result, the autonomous automobile may be damaged, make an erroneous decision in autonomous driving, and even cause a traffic accident (e.g., a rear-end collision due to incorrect vehicle distance information). Therefore, validation and authenticity of the sensory data is necessary before it is transmitted to the autonomous vehicle.
Disclosure of Invention
Aiming at the defects of the existing unmanned aerial vehicle cooperative sensing system based on the automatic driving vehicle networking network, the invention aims to reduce the risk caused by the application of bad data sensed by the unmanned aerial vehicle, improve the safety of the automatic driving vehicle networking network and realize more accurate automatic driving.
In order to achieve the purpose, the invention adopts the following technical scheme:
an unmanned aerial vehicle-assisted data verification system in an automatic driving vehicle networking network comprises an unmanned aerial vehicle sensing Unit, a Road Side Unit (RSU), a sensing data verification Unit and a data storage Unit. The unmanned aerial vehicle sensing unit is connected with the road side unit in an Air to ground (A2G) mode; the sensing data verification unit is connected with the road side unit in an A2G mode; the data storage unit is connected with the road side unit. The unmanned aerial vehicle sensing unit is responsible for network sensing data of the automatic driving vehicle network, and the network sensing data comprise road condition information, abnormal vehicle information and the like. Here, before the perception data of unmanned aerial vehicle perception unit is uploaded to the internet of vehicles, verification needs to be carried out. In order to realize perception data verification, an unmanned aerial vehicle perception unit needs to transmit perception data to be verified to a road side unit through an A2G channel and simultaneously issue a verification task; the roadside units are uniformly deployed at two sides of a road, sensing data and verification tasks submitted by the unmanned aerial vehicle sensing unit are collected through an A2G channel, and verification unmanned aerial vehicles are recruited to complete the verification tasks; the perception data verification unit consists of a verification unmanned aerial vehicle and is responsible for completing a verification task so as to judge whether perception data is correct or not; the data storage unit is used for storing sensing data, verification information and the like, and is maintained by the road side unit.
An unmanned aerial vehicle-assisted data verification method in an automatic driving vehicle networking network comprises the following steps:
s100, sensing road condition information and abnormal vehicle information in an unmanned aerial vehicle sensing automatic driving vehicle network, and issuing corresponding verification tasks on a road side unit according to complexity and importance of the sensed information;
s200, the road side unit receives the verification task, recruits and verifies that the unmanned aerial vehicle completes the verification task in a cooperative mode, and judges whether the unmanned aerial vehicle sensing data are correct or not;
s300, after the verification unmanned aerial vehicle receives the verification task, firstly verifying the format of the unmanned aerial vehicle, and then verifying the perception data according to a verification algorithm;
and S400, the road side unit receives and summarizes the verification result fed back by the verification unmanned aerial vehicle, and analyzes the verification result to obtain the final verification result of the perception data of the unmanned aerial vehicle. Feeding back the final verification result to the perception unmanned aerial vehicle through an A2G channel;
and S500, the road side unit is responsible for uploading information such as sensing data and verification results to the data storage unit.
The specific steps of step S200 are:
s210, the road side unit receives the verification task and adds the verification task to a task panel;
s220, real-time communication is carried out between road side units to synchronously update a task panel for verification unmanned aerial vehicles at all places to look up;
s230, verifying that the unmanned aerial vehicle refers to a task panel and requests a verification task from a road side unit;
s240, after confirming the identity of the unmanned aerial vehicle and the state of the verification task, the road side unit issues the verification task to the unmanned aerial vehicle requesting the task;
the specific steps of step S500 are:
s510, the road side unit packs information such as sensing data and verification results into a verification task packet and broadcasts the verification task packet to other road side units;
and S520, taking all legally registered road side units and one road side unit selected by the unmanned aerial vehicle voting as a super node to be responsible for data integration and storage work within a period of time.
S530, the super node collects and arranges verification task packages broadcast by each road side unit, calculates and updates the credit of the unmanned aerial vehicle and the credit of the road side units, integrates and packages the verification task packages, the credit and the credit into a block to be verified, and broadcasts the block to all the road side units for verification.
And S540, after receiving the road side unit recognition that the road side unit recognition exceeds 2/3, the super node adds the block to the tail end of the block chain and links the previous block to finish data storage.
Compared with the prior art, the invention has the following prominent substantive characteristics and obvious advantages:
(1) an unmanned aerial vehicle is introduced to participate in data perception of an automatic driving vehicle networking network, and the unique high-altitude visual angle of the unmanned aerial vehicle can be used for rapidly perceiving the states of roads and surrounding vehicles in a large range so as to realize safe automatic driving.
(2) In consideration of the occurrence of a sensing error phenomenon and a malicious sensing unmanned aerial vehicle, an unmanned aerial vehicle-assisted data verification method is designed for an unmanned aerial vehicle sensing automatic driving Internet of vehicles network system, so that the sensing error rate is reduced, and the behavior of the malicious unmanned aerial vehicle is inhibited.
(3) The block chain is used for storing information such as the sensing data and the verification result, malicious tampering in the data storing and forwarding process is prevented, the data storing efficiency is effectively improved, and the sensing data can be traced back by related departments.
(4) By combining the past behaviors of the unmanned aerial vehicle and the drive test unit and designing a credit and credit mechanism, the unmanned aerial vehicle is effectively stimulated to participate in data verification, and the drive test unit participates in verification task management.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of an unmanned aerial vehicle-assisted data verification system in an autonomous driving vehicle network according to the present invention.
Fig. 2 is a schematic diagram of an unmanned aerial vehicle-assisted data verification method in an autonomous driving vehicle network according to the present invention.
Fig. 3 is a detailed flowchart of step S200 in fig. 2.
Fig. 4 is a detailed flowchart of step S500 in fig. 2.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
An unmanned aerial vehicle-assisted data verification system in an automatic driving vehicle networking network comprises an unmanned aerial vehicle sensing unit 100, a road side unit 200, a sensing data verification unit 300 and a data storage unit 400; the unmanned aerial vehicle sensing unit 100 is connected with the road side unit 200 in an air-to-ground communication mode; the sensing data verification unit 300 is connected with the road side unit 200 through an air-to-ground communication mode; the data storage unit 400 is connected with the road side unit 200; the unmanned aerial vehicle sensing unit 100 is used for sensing road condition information and abnormal vehicle information; the road side unit 200 is used for collecting and managing verification tasks and bearing data storage tasks; the perception data verifying unit 300 is used for verifying perception information; the data storage unit 400 is configured to store verification information corresponding to the sensing data, and judge and verify reputation and credit values of behaviors of the unmanned aerial vehicle sensing unit 100 and the roadside unit 200.
This embodiment system reduces the risk that the bad data of using the unmanned aerial vehicle perception brought, improves the security of automatic driving vehicle networking, realizes more accurate autopilot nature, can realize more accurate autopilot.
Example two
As a specific embodiment of the present invention, as shown in fig. 1, an unmanned aerial vehicle-assisted data verification system in an autonomous driving vehicle network includes an unmanned aerial vehicle sensing unit 100, a roadside unit 200, a sensing data verification unit 300, and a data storage unit 400; the unmanned aerial vehicle sensing unit 100 is connected with the road side unit 200 in an air-to-ground communication mode; the sensing data verification unit 300 is connected with the road side unit 200 through A2G; the data storage unit 400 is connected to the roadside unit 200.
The unmanned aerial vehicle sensing unit 100 is used for sensing road condition information, abnormal vehicle information and the like. And the perception unmanned aerial vehicle registered by the perception main body in the AVNs. After the information is perceived, the drones need to perform data validation first in order to share the perception data. Data verification requires the employment of a certain number of verification drones to complete, thus sensing that a drone needs to first issue a verification task to an adjacent rsu 200.
The roadside unit 200 is used to collect and manage verification tasks. After receiving perception unmanned aerial vehicle verification task, roadside unit 200 can add it to the task panel in, and the roadside unit can be real-time synchronization update task panel simultaneously between to ensure that each verifies that unmanned aerial vehicle can both receive the verification task of renewal through roadside unit 200 that closes on. The roadside unit 200 also needs to recruit the verification unmanned aerial vehicle to complete the verification task, and pay a reward corresponding to the verification unmanned aerial vehicle. After the verification unmanned aerial vehicle completes the verification task, the roadside unit 200 collects the verification results of the verification unmanned aerial vehicles and feeds back the verification results to the perception unmanned aerial vehicle to authorize sharing. In addition, after the above work is completed, the road side unit needs to package, encrypt and broadcast the sensing data and the verification result, and the road side unit selected as the super node also needs to undertake a data storage task.
The perception data verifying unit 300 is used to verify perception information. The subject of the authentication is the authenticated drones registered in the AVNs. The verification unmanned aerial vehicle can request the verification task from the road side unit 200, and the road side unit issues the verification task to the verification unmanned aerial vehicle after the verification by the road side unit. Authenticating a drone requires proper completion of the authentication task as this can have an impact on its reputation and reward.
The data storage unit 400 is used for storing verification information corresponding to the sensing data, judging and verifying reputation and credit value of behaviors of the unmanned aerial vehicle and the road side unit, and the like. The body of the data storage unit 400 is a blockchain, which is maintained by the road side unit.
AutomaticUnmanned aerial vehicles in the driving vehicle networking network comprise two types, namely sensing unmanned aerial vehicles and verifying unmanned aerial vehicles. The set of drones is denoted by M {1, …, M, …, M }, S {1, …, S, …, S } and V {1, …, V, …, V } denote a set of verified drones, which satisfy S ≡ V ═ M. The roadside unit 200, i.e., the set of roadside units, is represented by K ═ 1, …, K, …, K, and the A2G range covered by the drone m has a radius of one
The circle of (c).
In order to effectively manage the unmanned aerial vehicle and the road side unit, the unmanned aerial vehicle and the road side unit register real identities thereof, such as a unique vehicle identification number issued by a traffic supervision center, on a Certificate Authority (CA) before participating in a sensing task or a verification task. Then, each drone M ∈ M and road side unit K ∈ K obtain their asymmetric private/public key pair { PKm，SKm} and { PKk，SKkAnd get the certificate. The certificate can ensure that the drone can exchange data securely on the network and verify the identity of the other party. The drone certificate is represented as:
Certm＝{IDm，PKm，SigCA(·)}
wherein, IDmIs the unique identification, PK, of unmanned aerial vehicle mmIs the public key, Sig, of drone mCA(. is a signature of a CA certificate, consisting of
Derived from the following. Each registered drone or road side unit then broadcasts the authentication to other entities in the autonomous in-vehicle network. Each entity receives the public keys of the other entities and completes the public key verification by decrypting the signature using the public key of the CA.
Based on the unmanned aerial vehicle-assisted data verification system, a unmanned aerial vehicle-assisted data verification method in an automatic driving vehicle networking network is provided, and as shown in fig. 2, the specific steps are as follows:
s100, sensing road condition information and abnormal vehicle information in an automatic driving vehicle networking network by the unmanned aerial vehicle sensing unit 100, and issuing corresponding verification tasks on the road side unit 200 according to the complexity and importance of the sensed information;
s200, the road side unit 200 receives the verification task, recruits and verifies that the unmanned aerial vehicle completes the verification task in a cooperative manner, and judges whether the sensing data of the unmanned aerial vehicle is correct or not;
s300, in a perception data verification unit, after the unmanned aerial vehicle is verified to receive a verification task, firstly, the format of the unmanned aerial vehicle is verified, and then perception data are verified according to a verification algorithm;
and S400, the road side unit 200 receives and summarizes the verification result fed back by the verification unmanned aerial vehicle, and analyzes the verification result to obtain the final verification result of the perception data of the unmanned aerial vehicle. Feeding back the final verification result to the perception unmanned aerial vehicle through an A2G channel;
s500, in the data storage unit 400, the road side unit 200 is responsible for uploading information such as sensing data and verification results to the data storage unit 400.
Further, with reference to fig. 3, step S200 includes the following steps:
s210, the road side unit 200 receives the verification task and adds the verification task to a task panel;
the tasks in the task panel contain four states, completed, in-process, pending and reconfirmed.
S220, the road side units 200 communicate in real time to synchronously update task panels for verification unmanned aerial vehicles at all places to look up;
the task panel of each roadside unit is identical. However, only the roadside unit that receives the verification task uploaded by the perception drone actually manages and takes charge of the verification task.
S230, verifying that the unmanned aerial vehicle refers to a task panel and requests a verification task from a road side unit;
the verification drone requests the verification task from the task panel according to the verification task type, the verification reward, and a verification time-to-live (TTL). In particular, the verification drone may request, by a nearby road side unit, a verification task managed by a distant road side unit.
And S240, after confirming the identity of the unmanned aerial vehicle and the state of the verification task, the road side unit 200 issues the verification task to the unmanned aerial vehicle requesting the task.
The roadside unit 200 first confirms the identity of the verification drones and then checks whether their reputations reach a prescribed threshold, and drones whose reputations do not reach the threshold will not be able to receive the verification task. Then, the roadside unit checks the state of the verification task to be received by the verification drone, and if the state is pending or reconfirmed, allows the verification drone to join the verification process of the verification task. Thereafter, the road side unit will connect to the verification drone and send the encrypted verification task package to the verification drone. The format of the verification task package is as follows:
wherein, H (INFO)sensor) Is the result of hash operation on the unique identifier of the data packet, INFOsensorRepresenting perceived information, PayvA reward indicating that the verifying drone completed a task.
In order to facilitate verification of unmanned aerial vehicles, verification tasks matched with the unmanned aerial vehicles are selected, and the difficulty of the verification tasks is divided into different levels in consideration of different calculation requirements and importance of the verification tasks. For the verification task u, the verification difficulty thetauSatisfies thetauE (0, 1). The reputation of the unmanned aerial vehicle is verified and evaluated mainly in an objective and subjective way.
From the perspective of objective evaluation, the smaller verification time not only ensures timeliness, but also improves the transmission rate. For the verification of drone v at time slot t, its average verification time
Can be expressed as:
wherein N isvIs the number of times theta that the unmanned aerial vehicle v participates in the verificationiIs the difficulty of verification of the ith verification task, TiIs the verification time of the ith verification task. To incentivize the verification drone to select a verification task that is compliant with its computational power, the ability to verify the drone is measured by the ratio of the verification time of the verification drone to the total verification mean time:
wherein eta isv(t) e (0, 1), V is the total number of verified drones. The equation shows that if the less time it takes to verify that the drone is completing a task, ηvThe larger (t) will be.
From the perspective of subjective evaluation, the behavior of the unmanned aerial vehicle and the verification result in the task verification process are mainly considered. For verification task v, there is NcrEach verification drone participates in the verification. The verification drones in the same group can evaluate each other according to the behavior of the group members. Thus, the cooperation evaluation score CR of the verification drone vvCan be expressed as:
wherein, CRjE {0, 1}, representing the behavior evaluation, CR, of the verified drone j to the verified drone vjDenotes that verifying drone j approves verifying drone v's behavior, CRjThe value of 0 represents that the verification unmanned plane j considers that the verification unmanned plane v has collusion attack in the task verification process, or maliciously increases verification time and other behaviors. The verification result of the verification unmanned aerial vehicle is evaluated by a road side unit managing the verification task:
wherein, RUkE {0, 1}, tableAnd showing the evaluation score of the road side unit k on the verification result. RUk1 denotes that the verification is correct, and RUk0 indicates a verification error. Therefore, considering the verification behavior of the drone throughout the verification process, both objectively and subjectively, the final reputation value of the verification drone v at the time slot t can be expressed as:
Rv(t)＝αηv+β·(χCRv+ωRUv)
wherein α and β are subjective adjustment factors, and α + β ═ 1 is satisfied. χ and ω are evaluation weights, satisfying χ + ω 1. Reputation affects the reward of verifying a drone, and too low reputation can be assessed by the system as a malicious verifying drone, rejecting its request for a verification task. Therefore, the verification that the unmanned aerial vehicle should do positive and effective behaviors to improve the reputation of the unmanned aerial vehicle as much as possible. The number of verification unmanned aerial vehicles which need to be recruited for sensing the verification tasks issued by the unmanned aerial vehicles is related to the credit of the unmanned aerial vehicles, and the requirement of meeting the requirement
Wherein R isvIs to verify the reputation of drone v,
is the reputation of perception unmanned aerial vehicle s, satisfies:
wherein f issGreater than 0, indicating the importance of perceiving the verification task issued by the drone,
is TTL for perceiving the verification task issued by the unmanned aerial vehicle s, and epsilon is an adjusting factor.
In order to realize reputation management and perception data traceability, a storage mechanism of block chain enabling is introduced. Data perceived by the unmanned aerial vehicle have the characteristic of confidentiality, and if the sensing data are maliciously utilized or stolen, the problems of privacy data disclosure and data abuse occur. Thus, blockchains are only targeted to a limited number of participants, i.e., federation blockchain types. Meanwhile, in order to save computing resources, Credit-enhanced trusted delayed proof of behaviour (CeD) alliance block chains are used for Credit management. The credit enhanced delegation rights certification consensus federation chain is maintained and managed by road side units registered with the certificate authority. When the road side unit registers in a certificate authority, the road side unit needs to pay a deposit for an account under the supervision of the public to become a legal entity in the block chain.
Further, with reference to fig. 4, step S500 further includes the following steps:
s510, the road side unit packs information such as sensing data and verification results into a verification task packet and broadcasts the verification task packet to other road side units;
and S520, voting all legally registered road side units and the unmanned aerial vehicle to select one road side unit as a super node to be responsible for data integration and storage work within a period of time.
The behaviors of the road side unit are divided into honesty and malice, and are evaluated by the super node of the current time slot. In general, the impact of behavior at different times on the blockchain is different. Therefore, a variable t is introducedrecentTo distinguish between past and present behavior of the roadside unit. When t < trecent，Then these behaviors are past behaviors and otherwise are current behaviors. Thus, the behavior score for road side unit k may be defined as:
where ξ and σ are timeliness weights, and ξ + σ satisfy ξ + σ ═ 1.
The number of past honesty behaviors, the number of current honesty behaviors, the number of past malicious behaviors and the number of current malicious behaviors are respectively. Thus, it is possible to provideThe credit score of rsu k is:
where θ and τ are trust weights. Smaller theta means less tolerance to malicious behaviour in the credit system.
Each unmanned aerial vehicle (perception or verification unmanned aerial vehicle) can participate in the voting process of the super nodes, and selects l super nodes to perform block generation, broadcast, verification process and block management in sequence in l time slots. In addition, the super node will also complete the processes of reputation calculation, reputation update and consensus. The voting weight of a drone depends on its reputation value, with higher reputations for drones having greater voting weights. After the first 2l nodes are voted out by the unmanned plane, the super nodes in the previous round will evaluate and check the credit scores of the newly elected nodes. If their credit score satisfies credit < e, then these nodes will be removed and the remaining child nodes become new supernodes in voting ranking order. If the super node fails to pack the blocks in time or makes malicious behavior aiming at the block chain, the unmanned aerial vehicle can also vote for elimination. As a penalty, the deposit of the removed supernode will be confiscated by the blockchain system.
S530, the super node collects and arranges verification task packages broadcast by each road side unit, calculates and updates the credit of the unmanned aerial vehicle and the credit of the road side units, integrates and packages the verification task packages, the credit and the credit into a block to be verified, and broadcasts the block to all the road side units for verification.
The supernode packs the reputation values of all drones into one transaction, which acts as the first transaction in the block. Similarly, the credit score of the roadside unit will also be packaged into a transaction as the second transaction in the block. Finally, the validation task transactions in the transaction pool are added to the block. And when the block is successfully packed, the super node broadcasts to the whole network and waits for block verification.
When the super node broadcasts the block to be verified, the road side unit receiving the block compares the block with the transactions in the transaction pool and deletes the repeated transactions. And the super node updates the credit of the unmanned aerial vehicle and the credit of the road side unit at the current time slot. Before updating, the super node needs to extract the reputation value and the credit score from the previous block. Thereafter, the super node collects the transactions broadcast by each rsu and adds them to its own transaction pool after validating its format. When the super node management block is turned to, the super node extracts all the transactions in the transaction pool, and then calculates and updates the reputation of the unmanned aerial vehicle, and the behavior of the road side unit is evaluated by the super node. Meanwhile, the super node completes the calculation and updating of credit score according to the credit calculation formula and the credit value before the road side unit.
And S540, after the super node receives the RSU approval exceeding 2/3, adding the block to the tail end of the block chain, and chaining the previous block to finish data storage.
The unmanned aerial vehicle-assisted data verification system and method in the automatic driving vehicle networking network of the embodiment comprise an unmanned aerial vehicle sensing unit, a road side unit, a sensing data verification unit and a data storage unit; the unmanned aerial vehicle sensing unit is connected with the road side unit in an Air to ground (A2G) mode; the sensing data verification unit is connected with the road side unit in an A2G mode; the data storage unit is connected with the road side unit. The method of the embodiment comprises the steps of recruiting a corresponding number of verification unmanned aerial vehicles to verify perception data according to the credit of the perception unmanned aerial vehicles; establishing credibility of the verification unmanned aerial vehicle according to verification time, cooperation evaluation and verification results; and performing consensus according to the proposed trust interest certificate of credit enhancement in the block chain of the alliance, finishing the storage of perception data, credit degree and verification information, and realizing data traceability.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. An unmanned aerial vehicle-assisted data verification system in an automatic driving vehicle networking network is characterized by comprising an unmanned aerial vehicle sensing unit (100), a road side unit (200), a sensing data verification unit (300) and a data storage unit (400); the unmanned aerial vehicle sensing unit (100) is connected with the road side unit (200) in an air-to-ground communication mode; the perception data verification unit (300) is connected with the road side unit (200) in an air-to-ground communication mode; the data storage unit (400) is connected with the road side unit (200); the unmanned aerial vehicle sensing unit (100) is used for sensing road condition information and abnormal vehicle information; the road side unit (200) is used for collecting and managing verification tasks and undertaking data storage tasks; the perception data verification unit (300) is used for verifying perception information; the data storage unit (400) is used for storing verification information corresponding to the perception data and evaluating and verifying the credit and credit values of behaviors of the unmanned aerial vehicle perception unit (100) and the road side unit (200).
2. The unmanned-aerial-vehicle-assisted data verification system in the autonomous-driving vehicle-networking network of claim 1, wherein the road side unit (200) is not only responsible for the management of verification tasks, but also serves as a mineworker in a block chain, maintaining the block chain.
3. The drone-assisted data verification system in an autonomous vehicle networking network according to claim 1, characterized in that the main body of the perception data verification unit (300) is verifying a drone; and the verification unmanned aerial vehicle collaborates to complete the verification task, and the honest verification behavior improves the credit and increases the verification reward.
4. An unmanned aerial vehicle-assisted data verification method in an automatic driving vehicle networking network is characterized by comprising the following steps:
s100, sensing road condition information and abnormal vehicle information in an automatic driving vehicle networking network by an unmanned aerial vehicle in an unmanned aerial vehicle sensing unit (100), and issuing a corresponding verification task on a road side unit (200) according to the complexity and importance of the sensed information;
s200, a road side unit (200) receives the verification task, recruits and verifies that the unmanned aerial vehicle completes the verification task in a cooperative manner, and judges whether the sensing data of the unmanned aerial vehicle is correct or not;
s300, in a perception data verification unit (300), after the unmanned aerial vehicle is verified to receive a verification task, firstly, the format of the unmanned aerial vehicle is verified, and then, perception data are verified according to a verification algorithm;
s400, a road side unit (200) receives and summarizes verification results fed back by the verification unmanned aerial vehicle, analyzes the verification results to obtain final verification results of perception data of the unmanned aerial vehicle, and feeds back the final verification results to the perception unmanned aerial vehicle through an air-to-ground communication channel;
s500, in the data storage unit (400), the road side unit (200) is responsible for uploading the sensing data and the verification result information to the data storage unit (400).
5. The unmanned aerial vehicle-assisted data verification method in the automatic driving vehicle networking network according to claim 4, wherein the specific steps of the step S200 are as follows:
s210, the road side unit (200) receives the verification task and adds the verification task to a task panel;
s220, the road side units (200) communicate in real time to synchronously update a task panel for verification unmanned aerial vehicles at all places to look up;
s230, verifying that the unmanned aerial vehicle refers to a task panel and requests a verification task from the road side unit (200);
s240, the road side unit (200) issues the verification task to the unmanned aerial vehicle requesting the task after confirming the identity of the verification unmanned aerial vehicle and the state of the verification task.
6. The unmanned aerial vehicle-assisted data verification method in the automatic driving vehicle networking network according to claim 4, wherein the specific steps of the step S500 are as follows:
s510, the road side unit (200) packs the sensing data and the verification result information into a verification task packet and broadcasts the verification task packet to other road side units (200);
s520, all legally registered road side units (200) and one road side unit (200) selected by the unmanned aerial vehicle voting are used as super nodes to be responsible for data integration and storage work within a period of time;
s530, the super nodes collect and arrange verification task packages broadcast by the road side units (200), calculate and update the credit of the unmanned aerial vehicle and the credit of the road side units (200), integrate and package the verification task packages, the credit and the credit into a block to be verified, and broadcast the block to all the road side units (200) for verification;
s540, after the super node receives the authorization of the road side unit (200) exceeding 2/3, the block is added to the tail end of the block chain, and the previous block is linked to complete data storage.
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| CN115866709A (en) * | 2023-01-30 | 2023-03-28 | 中国人民解放军96901部队 | Unmanned aerial vehicle swarm clustering ad hoc network method based on delegation equity certification |
| CN117150321A (en) * | 2023-10-31 | 2023-12-01 | 北京邮电大学 | Equipment trust evaluation method and device, service equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115866709A (en) * | 2023-01-30 | 2023-03-28 | 中国人民解放军96901部队 | Unmanned aerial vehicle swarm clustering ad hoc network method based on delegation equity certification |
| CN115866709B (en) * | 2023-01-30 | 2024-05-10 | 中国人民解放军96901部队 | Unmanned aerial vehicle bee colony clustering ad hoc network method and equipment based on trust rights and interests proving |
| CN117150321A (en) * | 2023-10-31 | 2023-12-01 | 北京邮电大学 | Equipment trust evaluation method and device, service equipment and storage medium |
| CN117150321B (en) * | 2023-10-31 | 2024-01-30 | 北京邮电大学 | Equipment trust evaluation method and device, service equipment and storage medium |
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