Lantao Li;Wenqi Zhang;Xiaoxue Wang;Tao Cui;Chen Sun
{"title":"NLOS Dies Twice: Challenges and Solutions of V2X for Cooperative Perception","authors":"Lantao Li;Wenqi Zhang;Xiaoxue Wang;Tao Cui;Chen Sun","doi":"10.1109/OJITS.2024.3492211","DOIUrl":null,"url":null,"abstract":"Multi-agent multi-sensor fusion between connected vehicles for cooperative perception has recently been recognized as the best technique for minimizing the occluded zone of individual vehicular perception system and further enhancing the overall safety of autonomous driving system. This technique relies heavily on the reliability and availability of vehicle-to-everything (V2X) communication. In practical cooperative perception application scenarios, the non-line-of-sight (NLOS) issue causes occluded zones for not only the perception system but also V2X direct communication, especially for busy traffic scenarios. Cooperative perception can address the NLOS issue for vehicular perception systems once. However, to ensure effective real-world implementation, we must also solve the NLOS challenge a second time for the communication systems that support cooperative perception, NLOS “dies” twice. To counteract underlying communication issues, we introduce an abstract perception matrix matching method for quick sensor fusion matching procedures and mobility-height hybrid relay determination procedures, proactively improving the efficiency and performance of V2X communication to serve the upper layer application fusion requirements. To demonstrate the effectiveness of our solution, a new simulation framework is designed to consider autonomous driving, cooperative perception and V2X communication in general, paving the way for end-to-end performance evaluation and further solution derivation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"774-782"},"PeriodicalIF":4.6000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10745605","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of Intelligent Transportation Systems","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10745605/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Multi-agent multi-sensor fusion between connected vehicles for cooperative perception has recently been recognized as the best technique for minimizing the occluded zone of individual vehicular perception system and further enhancing the overall safety of autonomous driving system. This technique relies heavily on the reliability and availability of vehicle-to-everything (V2X) communication. In practical cooperative perception application scenarios, the non-line-of-sight (NLOS) issue causes occluded zones for not only the perception system but also V2X direct communication, especially for busy traffic scenarios. Cooperative perception can address the NLOS issue for vehicular perception systems once. However, to ensure effective real-world implementation, we must also solve the NLOS challenge a second time for the communication systems that support cooperative perception, NLOS “dies” twice. To counteract underlying communication issues, we introduce an abstract perception matrix matching method for quick sensor fusion matching procedures and mobility-height hybrid relay determination procedures, proactively improving the efficiency and performance of V2X communication to serve the upper layer application fusion requirements. To demonstrate the effectiveness of our solution, a new simulation framework is designed to consider autonomous driving, cooperative perception and V2X communication in general, paving the way for end-to-end performance evaluation and further solution derivation.