Blockchain-based 6G High-definition Map Crowdsourcing Update Method

Abstract

The present invention discloses a method for crowdsourcing and updating 6G high-definition maps based on blockchain, which mainly solves the problem of low map update efficiency in the prior art due to low consensus efficiency. The solution is as follows: Vehicle units are registered as nodes in the blockchain network, and map update tasks are published; The blockchain network comprehensively evaluates various traffic parameters and selects high-quality miner nodes using a decision-making matrix; Miner nodes collect map resources required by the tasks and upload them to the transaction pool of the blockchain network; The blockchain network uses verifiable random numbers to select accounting nodes and packages the map resources in the transaction pool into new blocks; Vehicle units complete the update of 6G high-definition maps according to the map resources in the new blocks. The present invention uses less data volume of map resources and identity confirmation messages, and the screening time of accounting nodes and miner nodes is short, reducing the consensus time of the blockchain network and improving the update efficiency of 6G high-definition maps, which can be used for the processing of high-definition maps in the vehicle networking environment.

Description

Technical Field

[0001] The present invention belongs to the technical field of blockchain, and specifically relates to a method for crowdsourcing and updating 6G high-definition maps based on blockchain, which can be used for updating high-definition maps in the vehicle networking environment. Background Art

[0002] With the development of 5G technology and the proposal of the 6G vision, the future of the vehicle networking industry based on intelligent connected vehicles is more promising. Along with this, the update and maintenance of map information have become increasingly important. 6G technology can provide higher bandwidth and lower latency, and can support large-scale Internet of Things applications, which requires more accurate, detailed and timely map updates. The existing map updates are carried out in a crowdsourcing manner, with a trusted third party releasing, collecting and evaluating tasks. The transmission of a large amount of intermediate data causes waste of network resources, and may cause single point of failure problems as the number of service requests and information volume increase.

[0003] Blockchain is a distributed ledger, where internal blocks are connected in chronological order through hash values. As the underlying technology of many cryptocurrencies, it has the characteristics of transparency, auditability and immutability, making transactions in the network more secure and reliable. Combining blockchain technology with crowdsourcing technology can solve the trust and efficiency problems of crowdsourcing systems.

[0004] Chinese scholar Lijun Sun proposed a blockchain-based vehicle crowdsourcing service framework RC-chain in the Journal of Network and Computer Applications. This system designed a reputation model based on trust propagation and feedback similarity, improving the quality of crowdsourcing services, and applying queuing theory to generate effective configuration schemes, enhancing the performance of the entire system. However, due to the adoption of the PBFT consensus algorithm in this system, it is necessary to pre-perceive vehicle nodes in the target area. The frequent entry and exit of vehicles in the vehicle networking lead to low consensus efficiency, and the communication efficiency decreases severely as the data volume increases.

[0005] Chinese scholar Saide Zhu proposed a crowdsourcing platform based on zero-knowledge proof in IEEE Transactions on Industrial Informatics. It consists of a public chain and multiple sub-chains. The public chain adopts DPOS consensus, and at the same time uses zero-knowledge proof to verify transactions, making transactions more secure. The sub-chain adopts the PBFT consensus algorithm and can complete the consensus of a small amount of data in the system relatively quickly. However, the huge map resources in the vehicle networking cause a large amount of data transmission by the PBFT consensus algorithm, resulting in a long consensus time. At the same time, the method of specifying task receivers by task requesters is adopted in this system for task processing, which is not suitable for the highly dynamic vehicle networking environment.

[0006] Australian scholar Benny Wijaya proposed a blockchain crowdsourcing framework called CrowdBC in IEEE Transactions on Parallel and Distributed Systems. Its fully decentralized structure ensures fairness between task requesters and task receivers. The system uses users' reputation values as the threshold for task acceptance, reducing the possibility of task disputes. However, considering only a single factor allows malicious nodes to improve their reputation values through long-term disguise and then launch attacks on the system, causing greater harm to the system. Summary of the Invention

[0007] The purpose of the present invention is to propose a method for crowdsourcing and updating 6G high-definition maps based on blockchain in view of the deficiencies of the prior art, reducing the possibility of high-reputation vehicles threatening the system, reducing the amount of data in the network, and improving the consensus efficiency and communication efficiency of vehicles in the vehicle-to-everything (V2X) network.

[0008] To achieve the above solution, the technical solution of the present invention includes the following steps:

[0009] (1) M on-board units (OBUs) of vehicles submit their identity information to the blockchain network. After authentication, the OBUs of the vehicles are registered as nodes of the blockchain network, and the blockchain network assigns a public key pk i and a private key sk i to them, where 1 < i < M;

[0010] (2) When the i-th OBU of the vehicle i needs to request map resources in a certain area, it publishes a task TR i to the blockchain network. This task TR i includes the location pos of the map resources required by the vehicle, the task completion deadline T, the minimum number of people Num required to complete the task, the minimum reputation value Rp required for the task, and the reward Pay for completing the task;

[0011] (3) Select miner nodes to collect the surrounding map resources and upload them to the transaction pool of the blockchain network:

[0012] (3a) The blockchain network reads the parameter information C i of N OBUs of vehicles in the requested area for the task TR x ={c x1 , c x2 , c x3} and constructs a decision matrix D of vehicle information:

[0013]

[0014] where Cx Indicates the on-vehicle unit OBU x The parameter matrix of, c x1 Indicates the on-vehicle unit OBU x The position parameter of, c x2 Indicates the on-vehicle unit OBU x The sensor capability parameter of, c x3 Indicates the on-vehicle unit OBU x The reputation parameter of, 1 ≤ x ≤ N;

[0015] (3b) Normalize the decision matrix D through the min-max algorithm to obtain the normalized matrix R, and calculate the position parameter cx1 and the sensor capability parameter c x2 , respectively, according to R x3 The entropy e1, e2, e3 of the reputation parameter c, and then calculate the corresponding weights w1, w2, w3;

[0016] (3c) According to the weight W = (w1, w2, w3) and the parameter information C of the on-vehicle unit OBU x Calculate the comprehensive score of the on-vehicle unit OBU: When the on-vehicle unit OBU x The position in the comprehensive score sorting array sortArr is less than the minimum number of people Num to complete the task, it becomes a miner node and executes (3e); otherwise, it becomes a verification node and executes (4c);

[0017] (3e) The on-vehicle unit OBU of the miner node collects the surrounding map resources and uploads them to the transaction pool of the blockchain system;

[0018] (4) Collect the map resources in the blockchain network transaction pool and package them into blocks;

[0019] (4a) Select a bookkeeping node. The on-vehicle unit OBU reads the random number s in the previous block header and calculates the position of the bookkeeping node in the sorting array sortArr: f = s mod Num, and the on-vehicle unit OBU at this position is the bookkeeping node;

[0020] (4b) The on-vehicle unit OBU of the bookkeeping node collects the map resources in the blockchain transaction pool and packages them into a new block, generating a random number s' and a zero-knowledge proof π for the new block header;

[0021] (4c) The on-vehicle unit OBU of the verification node verifies the newly packaged block: If more than 2 / 3 of the verification nodes recognize the block, synchronize the block to the blockchain network, otherwise, return to step (3);

[0022] (5) The on-vehicle unit OBU i Completes the update of the 6G high-definition map according to the new block in the blockchain network.

[0023] Compared with the prior art, the present invention has the following advantages:

[0024] First, the present invention considers more traffic environment data for the crowdsourcing scenario based on the blockchain, selects high-quality nodes through comprehensive evaluation for map resource collection, reduces the ineffective circulation of low-quality map data in the network, improves the consensus efficiency of the vehicle blockchain, and enhances the security of the system.

[0025] Second, the present invention uses verifiable random numbers and decision matrices to implement the selection of accounting nodes and miner nodes. On the premise of ensuring the randomness and reliability of the consensus process, it reduces the interaction volume of identity confirmation messages in the network, shortens the screening time of accounting nodes and miner nodes, and further improves the consensus efficiency of the vehicle blockchain. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flowchart for the implementation of the present invention;

[0027] Figure 2 is a blockchain network structure diagram of the present invention. DETAILED IMPLEMENTATION METHOD

[0028] The following further describes the present invention in detail with reference to the accompanying drawings and specific embodiments.

[0029] The specific implementation of this example is based on a blockchain network.

[0030] Refer to Figure 2 , the blockchain network consists of a central institution, a service layer, a blockchain layer, and a data storage layer, where:

[0031] The central institution is responsible for verifying the identity information of vehicle units. After successful verification, it registers the vehicle units as nodes of the blockchain network and assigns public and private keys to them;

[0032] The vehicle units in the service layer are used to publish map update tasks, respond to map update tasks, and collect surrounding map resources;

[0033] The blockchain layer selects miner nodes, verification nodes, and accounting nodes by running a consensus algorithm, and packages the data uploaded by the service layer into blocks to form traceable and immutable data;

[0034] The data storage layer is used for distributed storage of the map resources submitted by vehicle units and returns the hash digest of the map resources to the vehicle units.

[0035] Refer to Figure 1 , the implementation steps of this example are as follows:

[0036] Step 1, the vehicle unit registers as a node of the blockchain network and obtains its public-private key pair <pk i , sk i >.

[0037] 1.1) The on-board unit (OBU) of M vehicles submits identity information info to the central authority in the blockchain network for identity verification. The identity information info includes the real name name of the vehicle unit OBU, the license plate number num of the vehicle unit OBU, and the ID card number IDcard of the vehicle unit OBU;

[0038] 1.2) After the identity verification is passed, the vehicle unit OBU is registered as a node of the blockchain network;

[0039] 1.3) The central authority in the blockchain network assigns a public-private key pair <pk i , sk i > to the vehicle unit:

[0040] 1.3.1) Perform a Hash operation on the identity information of the vehicle unit OBU to obtain a 256-character address hash addr;

[0041] 1.3.2) Perform SHA-256 operation and Base58 encoding on the address hash addr in sequence to obtain a 50-character private key sk i ;

[0042] 1.3.2) Encrypt the address hash addr using Speek1 and the elliptic curve encryption algorithm in sequence to obtain a 65-byte public key pk i .

[0043] Step 2, the vehicle unit publishes a map update request to the blockchain network.

[0044] The i-th vehicle unit OBU i When requesting map resources in a certain area, it needs to publish a task TR i to the blockchain network. This task TR i includes the location pos of the map resources required by the vehicle, the deadline T for task completion, the minimum number of people Num required for task completion, the minimum reputation value Rp required for the task, and the reward Pay for task completion.

[0045] Step 3, select miner nodes to collect the surrounding map resources and upload them to the transaction pool of the blockchain network.

[0046] 3.1) The blockchain network reads the parameter information C i of N vehicle units OBU in the requested area for the task TR x , which includes the vehicle unit OBU xThe position parameter c x1 , the on-vehicle unit OBU x The sensor capability parameter c of the on-vehicle unit OBU x2 , the on-vehicle unit OBU x The reputation parameter c of the on-vehicle unit OBU x3 , construct the decision matrix D of vehicle information:

[0047]

[0048] where 1 ≤ x ≤ N, the on-vehicle unit OBU x The position parameter c x1 is obtained by calculating the Euclidean distance between the current on-vehicle unit OBU and the position pos of the map resources required by the task TR i ; the sensor capability parameter c of the on-vehicle unit OBU x is objectively scored for the current on-vehicle unit OBU according to the ICT300 intelligent vehicle evaluation standard: 2 points for equipped with microwave radar, 2 points for equipped with millimeter-wave radar, 4 points for equipped with long-distance high-definition camera, and 2 points for equipped with lidar; the sensor capability parameter c of the on-vehicle unit OBU x2 The reputation parameter c of the on-vehicle unit OBU x varies between 0 and 1 according to the behavior accumulation of the on-vehicle unit OBU. For nodes with good behavior, its reputation parameter c x3 is closer to 1; x3

[0049] 3.2) By traversing the decision matrix D, obtain the maximum value max(c x1 ) and the minimum value min(c x1 ) of the position parameter c, the maximum value max(c x1 ) and the minimum value min(c x2 ) of the sensor capability parameter c, and the maximum value max(c x2 ) and the minimum value min(c x2 ) of the reputation parameter c; normalize the position parameter c x3 , the sensor parameter c x3 , and the reputation parameter c x3 in the decision matrix D respectively to obtain the normalized position parameter r x1 , the normalized sensor parameter r x2 , and the normalized reputation parameter r x3 : x1 , the normalized sensor parameter r x2 , the normalized reputation parameter r x3 :

[0050]

[0051]

[0052] ​

[0053] 3.3) Normalize the parameters of the N vehicle units OBU in the decision matrix D to obtain the normalized matrix R:

[0054]

[0055] 3.4) Perform information entropy operations on the normalized position parameter r x1 , the normalized sensor capability parameter r x2 , and the normalized reputation parameter r x3 respectively to obtain the information entropy e1 corresponding to the position parameter r x1 , the information entropy e2 corresponding to the sensor capability parameter r x2 , and the information entropy e3 corresponding to the reputation parameter r x3 :

[0056]

[0057]

[0058]

[0059] 3.5) Calculate the weights w1 corresponding to the position parameter r x1 , the weight w2 corresponding to the sensor capability r x2 , and the weight w3 corresponding to the reputation parameter r x3 according to the results of the information entropy:

[0060]

[0061]

[0062]

[0063] 3.6) Calculate the comprehensive score score x1 of the vehicle unit OBU according to the position parameter r x2 and its corresponding weight w1, the sensor capability r x3 and its corresponding weight w2, and the reputation parameter r x and its corresponding weight w3:

[0064] score x = w1c x1 + w2c x2 + w3c x3 ;

[0065] 3.7) Calculate the comprehensive scores of the N vehicle units according to the comprehensive score calculation formula, and construct the comprehensive score sorting array sortArr:

[0066] sortArr = {score1, score2,..., score x ..., score n};

[0067] 3.8) Determine whether the current vehicle unit can become a miner node:

[0068] If the vehicle unit OBU x is less than the minimum number of task completions Num in the comprehensive score sorting array sortArr, it becomes a miner node and executes (3.9); otherwise, it becomes a verification node and executes (4.3);

[0069] 3.9) The vehicle unit OBU of the miner node collects the surrounding map resources and uploads them to the transaction pool in the blockchain layer of the blockchain system:

[0070] 3.9.1) The vehicle unit OBU of the miner node collects the surrounding map resources, uploads the collected map resource upload data to the data storage layer, and the data storage layer returns the hash digest of the map resource;

[0071] 3.9.2) The vehicle unit OBU of the miner node uploads the map resource information including the reputation parameter c x3 , timestamp T now and the hash digest to the transaction pool of the blockchain system.

[0072] Step 4, collect the map resources in the blockchain network transaction pool and package them into blocks.

[0073] 4.1) Select the accounting node. The vehicle unit OBU reads the random number s in the previous block header and calculates the position of the accounting node in the sorting array sortArr: f = s mod Num, and the vehicle unit OBU at this position is the accounting node;

[0074] 4.2) The vehicle unit OBU of the accounting node collects the map resources in the blockchain transaction pool and packages them into a new block, generating the random number s' of the new block header and the zero-knowledge proof π:

[0075] s' = VRF_HASH(pk x , score x )

[0076] π = VRF_proof(pk x , s')

[0077] Among them, the VRF_HASH() function is used to generate the verifiable random number VRF, and the VRF_proof() function is used to generate the zero-knowledge proof of the random number s', score xFor the vehicle unit OBU x 's comprehensive evaluation score;

[0078] 4.3) The vehicle unit OBU of the verification node verifies the newly packaged block:

[0079] 4.3.1) The vehicle unit OBU of the verification node verifies the identity of the new block accounting node:

[0080] If the calculated position of the accounting node in the sorted array is equal to f, the identity verification of the accounting node passes, and step (4.3.2) is executed;

[0081] Otherwise, the verification node does not recognize the block;

[0082] 4.3.2) The verification node reads the random number s' and the zero-knowledge proof π in the new block header, and calculates the verification number R and the verification result O:

[0083] R = VRF_P2H(π)

[0084] O = VRF_verify(s', π, pk x )

[0085] where pk x is the public key of the vehicle unit OBU x , VRF_P2H() is used to parse the verification number from the zero-knowledge proof, and VRF_verify() is used to verify the random number and the zero-knowledge proof;

[0086] 4.3.3) According to the verification number R, verify whether the random number s' in the new block is valid:

[0087] If R = s' and O = True, the random number s' of the block is valid, and step (4.3.4) is executed;

[0088] Otherwise, the verification node does not recognize the block;

[0089] 4.3.4) The verification node verifies whether the quantity of map resources in the new block meets the requirements:

[0090] If the quantity of map resources in the new block is greater than or equal to the minimum number of people requirement Num of the task TR i , the quantity of map resources meets the requirements, and step (4.3.5) is executed;

[0091] Otherwise, the verification node does not recognize the block;

[0092] 4.3.5) The verification node verifies whether the submission time of the map resources is valid:

[0093] If the timestamps T of all submitted map resourcesnow All are earlier than task TR i If the submission time of the map resource is earlier than the deadline T required by the task, the submission time of the map resource is valid, and step (4.3.6) is executed;

[0094] Otherwise, the verification node does not recognize the block;

[0095] 4.3.6) The verification node verifies the reputation parameter c of the on-vehicle unit OBU that submits the map resource x3 Whether it is valid:

[0096] If the reputation parameter c of the on-vehicle unit OBU that submits the map resource x3 All are greater than the minimum reputation Rp required by task TR i The verification node recognizes the block;

[0097] Otherwise, the verification node does not recognize the block;

[0098] 4.4) The accounting node verifies whether the number of verification nodes that recognize the block meets the requirements:

[0099] If the number of verification nodes that recognize the block exceeds 2 / 3 of the total number of verification nodes, synchronize the block to the blockchain network and execute step 5;

[0100] Otherwise, return to step (3).

[0101] Step 5, the on-vehicle unit OBU i Completes the update of the 6G high-definition map according to the new block in the blockchain network.

[0102] The on-vehicle unit OBU i Reads the hash digest of the map resource in the new block, obtains the map resource in the data storage layer according to the hash digest, and uses the existing map data fusion algorithm to complete the update of the 6G high-definition map.

[0103] The technical effects of the present invention are further described below in conjunction with simulation experiments:

[0104] I. Simulation conditions

[0105] The simulation platform for the simulation experiment of the present invention is a public chain written in Go 1.13 in the Ubuntu 21.04 environment.

[0106] II. Simulation content and results

[0107] Under the above conditions, the 6G high-definition map is updated using the present invention and the existing proof-of-stake consensus algorithm PoS and delegated proof-of-stake consensus algorithm DPoS with different numbers of nodes, and their consensus times are compared. The results are shown in Table 1.

[0108] Table 1: Comparison table of consensus times

[0109]

[0110] As can be seen from Table 1, the consensus time for map updates of the 6G high-definition map crowdsourcing update solution of the present invention at different numbers of nodes is lower than the consensus times of the existing proof-of-stake (PoS) and delegated proof-of-stake (DPoS) consensus algorithms, indicating that the present invention can effectively improve the efficiency of 6G high-definition map updates in the vehicle-to-everything (V2X) blockchain network.

Claims

1. A 6G high-definition map crowdsourcing update method based on blockchain, characterized in that, Including the following steps: (1) M vehicle units OBU submit identity information to the blockchain network. After authentication, the vehicle unit OBU is registered as a node of the blockchain network, and the blockchain network assigns a public key pk i and a private key sk i , where 1 < i < M; (2) The i-th vehicle unit OBU i When it is necessary to request map resources in a certain area, a task TR needs to be published to the blockchain network i , and this task TR i includes the location pos of the map resources required by the vehicle, the task completion deadline T, the minimum number of people Num required to complete the task, the minimum reputation value Rp required by the task, and the reward Pay for completing this task; (3) Select miner nodes to collect surrounding map resources and upload them to the transaction pool of the blockchain network: (3a) Blockchain network reads task TR i Parameter information C of N vehicle units OBU within the requested area x ={c x1 , c x2 , c x3}}, and construct the decision matrix D of vehicle information: Among them, C x represents the parameter matrix of the vehicle unit OBU x , c x1 represents the position parameter of the vehicle unit OBU x , c x2 represents the sensor capability parameter of the vehicle unit oBU x , c x3 represents the reputation parameter of the vehicle unit oBU x , 1 ≤ x ≤ N; (3b) Normalize the decision matrix D through the min-max algorithm to obtain the normalized matrix R, and calculate the position parameter c, the sensor ability parameter c, and the reputation parameter c according to R, respectively. Calculate the entropy e1, e2, and e3 of x1 , x2 , and x3 , and then calculate the corresponding weights w1, w2, and w3; x1 , the sensor ability parameter c x2 , the reputation parameter c x3 , and calculate the entropies e1, e2, and e3 of x1 , x2 , and x3 , respectively, and then calculate the corresponding weights w1, w2, and w3; (3c) According to the weight W = (w1, w2, w3) and the parameter information C of the on-vehicle unit OBU x Calculate the comprehensive score of the on-vehicle unit OBU: When the on-vehicle unit OBU x is in a position in the comprehensive score sorting array sortArr that is less than the minimum number of people Num to complete the task, it becomes a miner node and executes (3d); otherwise, it becomes a verification node and executes (4c); (3d) The on-vehicle unit OBU of the miner node collects surrounding map resources and uploads them to the transaction pool of the blockchain system; (4) Collect the map resources in the blockchain network transaction pool and package them into blocks; (4a) Select a bookkeeping node. The on-vehicle unit OBU reads the random number s in the previous block header and calculates the position of the bookkeeping node in the sorting array sortArr: f = s mod Num. The on-vehicle unit OBU at this position is the bookkeeping node; (4b) The on-vehicle unit OBU of the bookkeeping node collects the map resources in the blockchain transaction pool and packages them into a new block, generating a random number s' and a zero-knowledge proof π for the new block header; (4c) The on-vehicle unit OBU of the verification node verifies the newly packaged block: If more than 2 / 3 of the verification nodes approve the block, synchronize the block to the blockchain network; otherwise, return to step (3); (5) Vehicle unit OBU i Update the 6G high-definition map according to the new block in the blockchain network.

2. The method according to claim 1, wherein In step (1), the blockchain network assigns the public key pk i and the private key sk i to the on-vehicle unit OBU. First, a 256-character address hash addr is obtained by performing a Hash operation on the identity information of the on-vehicle unit OBU; then, the SHA-256 operation and Base58 encoding are performed on the address hash addr to obtain a 50-character private key sk i ; meanwhile, the address hash addr is encrypted through Speek1 and the elliptic curve encryption algorithm to obtain a 65-byte public key pk i .

3. The method according to claim 1, wherein In step (3b), the decision matrix D is normalized through the min-max algorithm, as follows: (3b1) Obtain the position parameter c by traversing the decision matrix D x1 's maximum value max(c x1 ), and minimum value min(c x1 ), the sensor ability parameter c x2 's maximum value max(c x2 ), and minimum value min(c x2 ), the reputation parameter c x3 's maximum value max(c x3 ), and minimum value min(c x3 ); (3b2) The position parameter c in the decision matrix D x1 , the sensor parameter c x2 , the reputation parameter c x3 are normalized respectively to obtain the normalized position parameter r x1 , the normalized sensor parameter r x2 , the normalized reputation parameter r x3 : (3b3) According to step (3b2), the parameters of the N on-vehicle unit OBUs in the decision matrix D are all normalized to obtain a normalized matrix R:

4. The method according to claim 1, characterized in that In step (3b), calculate the position parameter c, the sensor ability parameter c, and the reputation parameter c respectively according to the normalization matrix R, and then calculate the entropies e1, e2, and e3 of the corresponding parameters, and further calculate the corresponding weights w1, w2, and w3, as follows: x1 , the sensor ability parameter c x2 , the reputation parameter c x3 , and the entropies e1, e2, and e3, and then calculate the corresponding weights w1, w2, and w3, as follows: (3b4)For the normalized position parameter r x1 , the normalized sensor capability parameter r x2 , and the normalized reputation parameter r x3 perform information entropy operations respectively to obtain the information entropy e1 corresponding to the position parameter r x1 , the information entropy e2 corresponding to the sensor capability parameter r x2 , and the information entropy e3 corresponding to the reputation parameter r x3 : Calculate the position parameter r based on the result of (3b4) x1 The corresponding weight w1, sensor capability r x2 The corresponding weight w2 and reputation parameter r x3 The corresponding weight w3:

5. The method according to claim 1, wherein In step (4b), the random number s' and the zero-knowledge proof π for the new block header are generated, respectively, as follows: s′ = VRF_HASH(pk x , score x ) π = VRF_proof(pk x , s′) Among them, the VRF_HASH() function is used to generate a verifiable random function VRF, the VRF_proof() function is used to generate a zero-knowledge proof of the random number s′, and pk x is the public key of the on-vehicle unit OBU x , and score x is the comprehensive evaluation score of the on-vehicle unit OBU x .

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