文献类型：期刊文献

中文题名：基于区块链的危险驾驶地图数据评估模型

英文题名：Blockchain-based Evaluation Model for Dangerous Driving Map Data

作者：陈凯[1,2];徐成[1,2];刘宏哲[1,2];代松银[1,2]

第一作者：陈凯

机构：[1]北京联合大学北京市信息服务工程重点实验室,北京100101;[2]北京联合大学机器人学院,北京100101

第一机构：北京联合大学北京市信息服务工程重点实验室

年份：2022

卷号：48

期号：8

起止页码：160-165

中文期刊名：计算机工程

外文期刊名：Computer Engineering

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD_E2021_2022】；

基金：国家自然科学基金(62171042,62102033,61871039);北京市教委项目(KM202111417001,KM201911417001);北京联合大学视觉智能协同创新中心项目(CYXC2011);北京联合大学学术研究项目(BPHR2020DZ02,ZB10202003,ZK40202101,ZK120202104)。

语种：中文

中文关键词：车联网;区块链;智能合约;全域集;评估模型

外文关键词：Internet of Vehicle(IoV);blockchain;smart contract;global set;evaluation model

摘要：在车联网中车辆与周边道路环境进行无线通信与数据交互,使车辆传感器、行人、道路单元之间实现信息互联,但大量的数据通信导致车辆运行环境复杂,车辆在行驶过程中不可避免地会遇到各种危险场景,从而引发车辆安全问题。为确保车辆数据可信及行车安全,建立基于区块链技术的危险驾驶地图数据评估模型。通过传感器实时记录环境数据,将车辆位置、速度等关键数据以及个人隐私信息加密上链实现交易传输,利用智能合约技术进行自动化脚本处理,随后执行共识将交易信息扩散到全部节点,节点承认交易合法后写入区块链。同时对车辆地图交互数据进行危险评估,并将评级结果及其对应操作反馈给车辆,使得车辆可在障碍物、网络攻击等场景下完成应急处理。实验结果表明,该模型能够根据不同危险场景对车辆进行危险评估与规避,确保车辆安全运行。
In Internet of Vehicle(IoV),vehicles and roads communicate wirelessly and interact with each other.Vehicle sensor,pedestrians,the unit can take each other information and communication.As a result of the complex data communication caused by the vehicle running environment,vehicles will encounter all sorts of dangerous situations,making vehicle travel unsafe.To ensure the credibility of vehicle data and driving safety,an evaluation model for dangerous driving map data based on blockchain technology is established.The model records environmental data in real time through sensors,encrypts key data such as vehicle position,speed,and personal privacy information on the chain to realize transaction transmission,uses smart contract technology to automate script processing,and then executes consensus to spread transaction information across all nodes.After the transaction is legal,it is written into the blockchain.Simultaneously,the danger evaluation is performed on the vehicle map interaction data,and the rating results and corresponding operations are fed back to the vehicle,so that the vehicle can conduct emergency treatment in scenarios such as obstacle and network attacks.The experimental results show that the model can conduct danger evaluation and avoidance of vehicles according to different dangerous scenarios to ensure the safe operation of vehicles.