文献类型：期刊文献

英文题名：A Real-Time Complex Road AI Perception Based on 5G-V2X for Smart City Security

作者：Xu, Cheng[1];Wu, Hongjun[1];Zhang, Yinong[2];Dai, Songyin[1];Liu, Hongzhe[1];Tian, Jin[3]

第一作者：Xu, Cheng

通讯作者：Zhang, YN[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Coll Robot, Beijing, Peoples R China;[2]Beijing Union Univ, Coll Urban Rail Transit & Logist, Beijing, Peoples R China;[3]Beijing Univ Posts & Telecommun, Beijing, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室|北京联合大学机器人学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Urban Rail Transit & Logist, Beijing, Peoples R China.|[11417]北京联合大学;

年份：2022

卷号：2022

外文期刊名：WIRELESS COMMUNICATIONS & MOBILE COMPUTING

收录：;EI(收录号：20220511571048);Scopus(收录号：2-s2.0-85123757782);WOS:【SCI-EXPANDED(收录号:WOS:000774869900004)】；

基金：This work was supported by the Beijing Municipal Commission of Education Project (Nos. KM202111417001 and KM201911417001), the 2021 Vocational Education Project of China Association of Vocational and Technical Education (Grant No. SZ21C037), the National Natural Science Foundation of China (Grant Nos. 62102033, 61906017, 62006020, 61871039, and 62171042), the Collaborative Innovation Center for Visual Intelligence (Grant No. CYXC2011), and the Academic Research Projects of Beijing Union University (Nos. ZB10202003, ZK40202101, ZK120202104, and BPHR2020DZ02).

语种：英文

外文关键词：Security of data - 5G mobile communication systems - Vehicle to Everything - Roads and streets

摘要：The Internet of Vehicles and information security are key components of a smart city. Real-time road perception is one of the most difficult tasks. Traditional detection methods require manual adjustment of parameters, which is difficult, and is susceptible to interference from object occlusion, light changes, and road wear. Designing a robust road perception algorithm is still challenging. On this basis, we combine artificial intelligence algorithms and the 5G-V2X framework to propose a real-time road perception method. First, an improved model based on Mask R-CNN is implemented to improve the accuracy of detecting lane line features. Then, the linear and polynomial fitting methods of feature points in different fields of view are combined. Finally, the optimal parameter equation of the lane line can be obtained. We tested our method in complex road scenes. Experimental results show that, combined with 5G-V2X, this method ultimately has a faster processing speed and can sense road conditions robustly under various complex actual conditions.