{"title":"Detecting Approaching Objects at Intersection Using on-Vehicle 3D-LiDAR for Automated Driving Vehicle","authors":"Yuki Komatsu, Shin Kato, M. Itami","doi":"10.1109/ITNAC55475.2022.9998404","DOIUrl":null,"url":null,"abstract":"In our research, we developed a method for detecting approaching objects at intersection by focusing on geometric features of point cloud obtained from 3D-LiDAR, without using pre-generated maps to understand the environment. This method can be applied to intersection with diagonal crossings, and can detect approaching vehicles and pedestrians at distances of up to 49 m and 38 m, respectively. The results also showed that the detection was robust and continuous. Furthermore, this process can be used in 50 ms per a frame, so that can be used in real time. This will lead to collision prediction and judgment of starting for automated vehicles.","PeriodicalId":205731,"journal":{"name":"2022 32nd International Telecommunication Networks and Applications Conference (ITNAC)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 32nd International Telecommunication Networks and Applications Conference (ITNAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITNAC55475.2022.9998404","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In our research, we developed a method for detecting approaching objects at intersection by focusing on geometric features of point cloud obtained from 3D-LiDAR, without using pre-generated maps to understand the environment. This method can be applied to intersection with diagonal crossings, and can detect approaching vehicles and pedestrians at distances of up to 49 m and 38 m, respectively. The results also showed that the detection was robust and continuous. Furthermore, this process can be used in 50 ms per a frame, so that can be used in real time. This will lead to collision prediction and judgment of starting for automated vehicles.
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