沈阳航空航天大学学报2024，Vol.41Issue(5)：44-53,10.DOI:10.3969/j.issn.2095-1248.2024.05.005
一种基于C-V2X的BSM异常数据校正的两阶段学习策略
A two-phase learning strategy for BSM anomaly data correction basd on C-V2X
摘要
Abstract
Under the cellular vehicle-to-everything(C-V2X)communication technology framework,the accuracy of basic safety messages(BSM)is crucial for ensuring road traffic safety.However,BSM data is susceptible to non-malicious factors such as sensor faults or environmental disturbances,leading to data anomalies that may misguide driving decisions.In response to this issue,two-phase learning strategy for correcting anomalies in BSM was proposed.In the first phase,an unsupervised hybrid gen-erative model was used to learn the behavior patterns and distribution characteristics of normal BSM data and a memory module was introduced to construct a fine-grained prototype repository in the fea-ture space for enhancing the model's understanding of the diversity of normal behavior patterns.In the second phase,based on the network parameters obtained in the first phase,a self-supervised learning strategy was employed for data correction.Results show that the proposed solution exhibits good correc-tion capability and significantly reduces the error in BSM.关键词
蜂窝车联网/基本安全信息/数据校正/混合模型/自监督学习Key words
cellular vehicle-to-everything/basic safety message/data correction/hybrid model/self-supervised learning分类
信息技术与安全科学引用本文复制引用
赵亮,樊旭,毛超进,林娜..一种基于C-V2X的BSM异常数据校正的两阶段学习策略[J].沈阳航空航天大学学报,2024,41(5):44-53,10.基金项目
国家自然科学基金(项目编号:62372310) （项目编号:62372310）
辽宁省科技厅应用基础研究计划项目(项目编号:2023JH2/101300194) （项目编号:2023JH2/101300194）
辽宁省兴辽英才计划项目(项目编号:XLYC2203151) （项目编号:XLYC2203151）
